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R/plotting.R
In monocle: Clustering, differential expression, and trajectory analysis for single- cell RNA-Seq
Defines functions
plot_cell_trajectory
monocle_theme_opts
Documented in
plot_cell_trajectory
utils::globalVariables(c("Pseudotime", "value", "ids", "prin_graph_dim_1", "prin_graph_dim_2", "State",
"value", "feature_label", "expectation", "colInd", "rowInd", "value",
"source_prin_graph_dim_1", "source_prin_graph_dim_2"))
monocle_theme_opts <- function()
{
theme(strip.background = element_rect(colour = 'white', fill = 'white')) +
theme(panel.border = element_blank()) +
theme(axis.line.x = element_line(size=0.25, color="black")) +
theme(axis.line.y = element_line(size=0.25, color="black")) +
theme(panel.grid.minor.x = element_blank(), panel.grid.minor.y = element_blank()) +
theme(panel.grid.major.x = element_blank(), panel.grid.major.y = element_blank()) +
theme(panel.background = element_rect(fill='white')) +
theme(legend.key=element_blank())
}
#' Plots the minimum spanning tree on cells.
#'
#' @param cds CellDataSet for the experiment
#' @param x the column of reducedDimS(cds) to plot on the horizontal axis
#' @param y the column of reducedDimS(cds) to plot on the vertical axis
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to map to each cell's color
#' @param show_tree whether to show the links between cells connected in the minimum spanning tree
#' @param show_backbone whether to show the diameter path of the MST used to order the cells
#' @param backbone_color the color used to render the backbone.
#' @param markers a gene name or gene id to use for setting the size of each cell in the plot
#' @param use_color_gradient Whether or not to use color gradient instead of cell size to show marker expression level
#' @param markers_linear a boolean used to indicate whether you want to scale the markers logarithimically or linearly
#' @param show_cell_names draw the name of each cell in the plot
#' @param show_state_number show state number
#' @param cell_size The size of the point for each cell
#' @param cell_link_size The size of the line segments connecting cells (when used with ICA) or the principal graph (when used with DDRTree)
#' @param cell_name_size the size of cell name labels
#' @param state_number_size the size of the state number
#' @param show_branch_points Whether to show icons for each branch point (only available when reduceDimension was called with DDRTree)
#' @param theta How many degrees you want to rotate the trajectory
#' @param ... Additional arguments passed into scale_color_viridis function
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
#' @importFrom igraph get.edgelist
#' @importFrom tibble rownames_to_column
#' @importFrom viridis scale_color_viridis
#' @importFrom dplyr left_join mutate n slice
#' @export
#' @examples
#' \dontrun{
#' lung <- load_lung()
#' plot_cell_trajectory(lung)
#' plot_cell_trajectory(lung, color_by="Pseudotime", show_backbone=FALSE)
#' plot_cell_trajectory(lung, markers="MYH3")
#' }
plot_cell_trajectory <- function(cds,
x=1,
y=2,
color_by="State",
show_tree=TRUE,
show_backbone=TRUE,
backbone_color="black",
markers=NULL,
use_color_gradient = FALSE,
markers_linear = FALSE,
show_cell_names=FALSE,
show_state_number = FALSE,
cell_size=1.5,
cell_link_size=0.75,
cell_name_size=2,
state_number_size = 2.9,
show_branch_points=TRUE,
theta = 0,
...) {
requireNamespace("igraph")
gene_short_name <- NA
sample_name <- NA
sample_state <- pData(cds)$State
data_dim_1 <- NA
data_dim_2 <- NA
#TODO: need to validate cds as ready for this plot (need mst, pseudotime, etc)
lib_info_with_pseudo <- pData(cds)
if (is.null(cds@dim_reduce_type)){
stop("Error: dimensionality not yet reduced. Please call reduceDimension() before calling this function.")
}
if (cds@dim_reduce_type == "ICA"){
reduced_dim_coords <- reducedDimS(cds)
} else if (cds@dim_reduce_type %in% c("simplePPT", "DDRTree") ){
reduced_dim_coords <- reducedDimK(cds)
} else {
stop("Error: unrecognized dimensionality reduction method.")
}
ica_space_df <- Matrix::t(reduced_dim_coords) %>%
as.data.frame() %>%
select_(prin_graph_dim_1 = x, prin_graph_dim_2 = y) %>%
mutate(sample_name = rownames(.), sample_state = rownames(.))
dp_mst <- minSpanningTree(cds)
if (is.null(dp_mst)){
stop("You must first call orderCells() before using this function")
}
edge_df <- dp_mst %>%
igraph::as_data_frame() %>%
select_(source = "from", target = "to") %>%
left_join(ica_space_df %>% select_(source="sample_name", source_prin_graph_dim_1="prin_graph_dim_1", source_prin_graph_dim_2="prin_graph_dim_2"), by = "source") %>%
left_join(ica_space_df %>% select_(target="sample_name", target_prin_graph_dim_1="prin_graph_dim_1", target_prin_graph_dim_2="prin_graph_dim_2"), by = "target")
data_df <- t(monocle::reducedDimS(cds)) %>%
as.data.frame() %>%
select_(data_dim_1 = x, data_dim_2 = y) %>%
rownames_to_column("sample_name") %>%
mutate(sample_state) %>%
left_join(lib_info_with_pseudo %>% rownames_to_column("sample_name"), by = "sample_name")
return_rotation_mat <- function(theta) {
theta <- theta / 180 * pi
matrix(c(cos(theta), sin(theta), -sin(theta), cos(theta)), nrow = 2)
}
rot_mat <- return_rotation_mat(theta)
cn1 <- c("data_dim_1", "data_dim_2")
cn2 <- c("source_prin_graph_dim_1", "source_prin_graph_dim_2")
cn3 <- c("target_prin_graph_dim_1", "target_prin_graph_dim_2")
data_df[, cn1] <- as.matrix(data_df[, cn1]) %*% t(rot_mat)
edge_df[, cn2] <- as.matrix(edge_df[, cn2]) %*% t(rot_mat)
edge_df[, cn3] <- as.matrix(edge_df[, cn3]) %*% t(rot_mat)
markers_exprs <- NULL
if (is.null(markers) == FALSE) {
markers_fData <- subset(fData(cds), gene_short_name %in% markers)
if (nrow(markers_fData) >= 1) {
markers_exprs <- reshape2::melt(as.matrix(exprs(cds[row.names(markers_fData),])))
colnames(markers_exprs)[1:2] <- c('feature_id','cell_id')
markers_exprs <- merge(markers_exprs, markers_fData, by.x = "feature_id", by.y="row.names")
#print (head( markers_exprs[is.na(markers_exprs$gene_short_name) == FALSE,]))
markers_exprs$feature_label <- as.character(markers_exprs$gene_short_name)
markers_exprs$feature_label[is.na(markers_exprs$feature_label)] <- markers_exprs$Var1
}
}
if (is.null(markers_exprs) == FALSE && nrow(markers_exprs) > 0){
data_df <- merge(data_df, markers_exprs, by.x="sample_name", by.y="cell_id")
if(use_color_gradient) {
if(markers_linear){
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2)) + geom_point(aes(color= value), size=I(cell_size), na.rm = TRUE) +
scale_color_viridis(name = paste0("value"), ...) + facet_wrap(~feature_label)
} else {
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2)) + geom_point(aes(color=log10(value + 0.1)), size=I(cell_size), na.rm = TRUE) +
scale_color_viridis(name = paste0("log10(value + 0.1)"), ...) + facet_wrap(~feature_label)
}
} else {
if(markers_linear){
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2, size= (value * 0.1))) + facet_wrap(~feature_label)
} else {
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2, size=log10(value + 0.1))) + facet_wrap(~feature_label)
}
}
} else {
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2))
}
if (show_tree){
g <- g + geom_segment(aes_string(x="source_prin_graph_dim_1", y="source_prin_graph_dim_2", xend="target_prin_graph_dim_1", yend="target_prin_graph_dim_2"), size=cell_link_size, linetype="solid", na.rm=TRUE, data=edge_df)
}
# FIXME: setting size here overrides the marker expression funtionality.
# Don't do it!
if (is.null(markers_exprs) == FALSE && nrow(markers_exprs) > 0){
if(use_color_gradient) {
# g <- g + geom_point(aes_string(color = color_by), na.rm = TRUE)
} else {
g <- g + geom_point(aes_string(color = color_by), na.rm = TRUE)
}
}else {
if(use_color_gradient) {
# g <- g + geom_point(aes_string(color = color_by), na.rm = TRUE)
} else {
g <- g + geom_point(aes_string(color = color_by), size=I(cell_size), na.rm = TRUE)
}
}
if (show_branch_points && cds@dim_reduce_type == 'DDRTree'){
mst_branch_nodes <- cds@auxOrderingData[[cds@dim_reduce_type]]$branch_points
branch_point_df <- ica_space_df %>%
slice(match(mst_branch_nodes, sample_name)) %>%
mutate(branch_point_idx = seq_len(n()))
g <- g +
geom_point(aes_string(x="prin_graph_dim_1", y="prin_graph_dim_2"),
size=5, na.rm=TRUE, branch_point_df) +
geom_text(aes_string(x="prin_graph_dim_1", y="prin_graph_dim_2", label="branch_point_idx"),
size=4, color="white", na.rm=TRUE, branch_point_df)
}
if (show_cell_names){
g <- g + geom_text(aes(label=sample_name), size=cell_name_size)
}
if (show_state_number){
g <- g + geom_text(aes(label = sample_state), size = state_number_size)
}
g <- g +
#scale_color_brewer(palette="Set1") +
monocle_theme_opts() +
xlab(paste("Component", x)) +
ylab(paste("Component", y)) +
theme(legend.position="top", legend.key.height=grid::unit(0.35, "in")) +
#guides(color = guide_legend(label.position = "top")) +
theme(legend.key = element_blank()) +
theme(panel.background = element_rect(fill='white'))
g
}
#' @rdname package-deprecated
#' @title Plots the minimum spanning tree on cells.
#' This function is deprecated.
#' @description This function arranges all of the cells in the cds in a tree and
#' predicts their location based on their pseudotime value
#' @param cds CellDataSet for the experiment
#' @param x the column of reducedDimS(cds) to plot on the horizontal axis
#' @param y the column of reducedDimS(cds) to plot on the vertical axis
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to map to each cell's color
#' @param show_tree whether to show the links between cells connected in the minimum spanning tree
#' @param show_backbone whether to show the diameter path of the MST used to order the cells
#' @param backbone_color the color used to render the backbone.
#' @param markers a gene name or gene id to use for setting the size of each cell in the plot
#' @param show_cell_names draw the name of each cell in the plot
#' @param cell_size The size of the point for each cell
#' @param cell_link_size The size of the line segments connecting cells (when used with ICA) or the principal graph (when used with DDRTree)
#' @param cell_name_size the size of cell name labels
#' @param show_branch_points Whether to show icons for each branch point (only available when reduceDimension was called with DDRTree)
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
#' @export
#' @seealso plot_cell_trajectory
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' plot_cell_trajectory(HSMM)
#' plot_cell_trajectory(HSMM, color_by="Pseudotime", show_backbone=FALSE)
#' plot_cell_trajectory(HSMM, markers="MYH3")
#' }
plot_spanning_tree <- function(cds,
x=1,
y=2,
color_by="State",
show_tree=TRUE,
show_backbone=TRUE,
backbone_color="black",
markers=NULL,
show_cell_names=FALSE,
cell_size=1.5,
cell_link_size=0.75,
cell_name_size=2,
show_branch_points=TRUE){
.Deprecated("plot_cell_trajectory") #include a package argument, too
plot_cell_trajectory(cds=cds,
x=x,
y=y,
color_by=color_by,
show_tree=show_tree,
show_backbone=show_backbone,
backbone_color=backbone_color,
markers=markers,
show_cell_names=show_cell_names,
cell_size=cell_size,
cell_link_size=cell_link_size,
cell_name_size=cell_name_size,
show_branch_points=show_branch_points)
}
#' @title Plots expression for one or more genes as a violin plot
#'
#' @description Accepts a subset of a CellDataSet and an attribute to group cells by,
#' and produces one or more ggplot2 objects that plots the level of expression for
#' each group of cells.
#'
#' @param cds_subset CellDataSet for the experiment
#' @param grouping the cell attribute (e.g. the column of pData(cds)) to group cells by on the horizontal axis
#' @param min_expr the minimum (untransformed) expression level to use in plotted the genes.
#' @param cell_size the size (in points) of each cell used in the plot
#' @param nrow the number of rows used when laying out the panels for each gene's expression
#' @param ncol the number of columns used when laying out the panels for each gene's expression
#' @param panel_order the order in which genes should be layed out (left-to-right, top-to-bottom)
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to be used to color each cell
#' @param plot_trend whether to plot a trendline tracking the average expression across the horizontal axis.
#' @param label_by_short_name label figure panels by gene_short_name (TRUE) or feature id (FALSE)
#' @param relative_expr Whether to transform expression into relative values
#' @param log_scale a boolean that determines whether or not to scale data logarithmically
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
#' @importFrom BiocGenerics sizeFactors
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' my_genes <- HSMM[row.names(subset(fData(HSMM), gene_short_name %in% c("ACTA1", "ID1", "CCNB2"))),]
#' plot_genes_violin(my_genes, grouping="Hours", ncol=2, min_expr=0.1)
#' }
plot_genes_violin <- function (cds_subset, grouping = "State", min_expr = NULL, cell_size = 0.75,
nrow = NULL, ncol = 1, panel_order = NULL, color_by = NULL,
plot_trend = FALSE, label_by_short_name = TRUE, relative_expr = TRUE,
log_scale = TRUE)
{
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial",
"negbinomial.size")) {
integer_expression = TRUE
}
else {
integer_expression = FALSE
relative_expr = TRUE
}
if (integer_expression) {
cds_exprs = exprs(cds_subset)
if (relative_expr) {
if (is.null(sizeFactors(cds_subset))) {
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
cds_exprs = Matrix::t(Matrix::t(cds_exprs)/sizeFactors(cds_subset))
}
#cds_exprs = reshape2::melt(round(as.matrix(cds_exprs)))
cds_exprs = reshape2::melt(as.matrix(cds_exprs))
}
else {
cds_exprs = exprs(cds_subset)
cds_exprs = reshape2::melt(as.matrix(cds_exprs))
}
if (is.null(min_expr)) {
min_expr = cds_subset@lowerDetectionLimit
}
colnames(cds_exprs) = c("f_id", "Cell", "expression")
cds_exprs$expression[cds_exprs$expression < min_expr] = min_expr
cds_pData = pData(cds_subset)
#
# # Custom bit for adding in a group for
# if(! is.null(show_combined)) {
# for(combine_gene in show_combined) {
# cds_pData_all = subset(cds_pData, gene == combine_gene)
# cds_pData_all[, grouping] = paste("All", combine_gene)
# cds_pData = rbind(cds_pData, cds_pData_all)
# }
# }
cds_fData = fData(cds_subset)
cds_exprs = merge(cds_exprs, cds_fData, by.x = "f_id", by.y = "row.names")
cds_exprs = merge(cds_exprs, cds_pData, by.x = "Cell", by.y = "row.names")
cds_exprs$adjusted_expression = log10(cds_exprs$expression)
if (label_by_short_name == TRUE) {
if (is.null(cds_exprs$gene_short_name) == FALSE) {
cds_exprs$feature_label = cds_exprs$gene_short_name
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] = cds_exprs$f_id
}
else {
cds_exprs$feature_label = cds_exprs$f_id
}
}
else {
cds_exprs$feature_label = cds_exprs$f_id
}
if (is.null(panel_order) == FALSE) {
cds_exprs$feature_label = factor(cds_exprs$feature_label,
levels = panel_order)
}
q = ggplot(aes_string(x = grouping, y = "expression"), data = cds_exprs)
if (is.null(color_by) == FALSE) {
q = q + geom_violin(aes_string(fill = color_by))
}
else {
q = q + geom_violin()
}
if (plot_trend == TRUE) {
q = q + stat_summary(fun.data = "mean_cl_boot",
size = 0.2)
q = q + stat_summary(aes_string(x = grouping, y = "expression",
group = color_by), fun.data = "mean_cl_boot",
size = 0.2, geom = "line")
}
q = q + facet_wrap(~feature_label, nrow = nrow,
ncol = ncol, scales = "free_y")
if (min_expr < 1) {
q = q + expand_limits(y = c(min_expr, 1))
}
q = q + ylab("Expression") + xlab(grouping)
if (log_scale == TRUE){
q = q + scale_y_log10()
}
q
}
#' Plots expression for one or more genes as a jittered, grouped points
#'
#' @description Accepts a subset of a CellDataSet and an attribute to group cells by,
#' and produces one or more ggplot2 objects that plots the level of expression for
#' each group of cells.
#'
#' @param cds_subset CellDataSet for the experiment
#' @param grouping the cell attribute (e.g. the column of pData(cds)) to group cells by on the horizontal axis
#' @param min_expr the minimum (untransformed) expression level to use in plotted the genes.
#' @param cell_size the size (in points) of each cell used in the plot
#' @param nrow the number of rows used when laying out the panels for each gene's expression
#' @param ncol the number of columns used when laying out the panels for each gene's expression
#' @param panel_order the order in which genes should be layed out (left-to-right, top-to-bottom)
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to be used to color each cell
#' @param plot_trend whether to plot a trendline tracking the average expression across the horizontal axis.
#' @param label_by_short_name label figure panels by gene_short_name (TRUE) or feature id (FALSE)
#' @param relative_expr Whether to transform expression into relative values
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
#' @importFrom BiocGenerics sizeFactors
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' my_genes <- HSMM[row.names(subset(fData(HSMM), gene_short_name %in% c("MYOG", "ID1", "CCNB2"))),]
#' plot_genes_jitter(my_genes, grouping="Media", ncol=2)
#' }
plot_genes_jitter <- function(cds_subset,
grouping = "State",
min_expr=NULL,
cell_size=0.75,
nrow=NULL,
ncol=1,
panel_order=NULL,
color_by=NULL,
plot_trend=FALSE,
label_by_short_name=TRUE,
relative_expr=TRUE){
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")){
integer_expression <- TRUE
}else{
integer_expression <- FALSE
relative_expr <- TRUE
}
if (integer_expression)
{
cds_exprs <- exprs(cds_subset)
if (relative_expr){
if (is.null(sizeFactors(cds_subset)))
{
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
cds_exprs <- Matrix::t(Matrix::t(cds_exprs) / sizeFactors(cds_subset))
}
cds_exprs <- reshape2::melt(round(as.matrix(cds_exprs)))
}else{
cds_exprs <- exprs(cds_subset)
cds_exprs <- reshape2::melt(as.matrix(cds_exprs))
}
if (is.null(min_expr)){
min_expr <- cds_subset@lowerDetectionLimit
}
colnames(cds_exprs) <- c("f_id", "Cell", "expression")
cds_exprs$expression[cds_exprs$expression < min_expr] <- min_expr
cds_pData <- pData(cds_subset)
cds_fData <- fData(cds_subset)
cds_exprs <- merge(cds_exprs, cds_fData, by.x="f_id", by.y="row.names")
cds_exprs <- merge(cds_exprs, cds_pData, by.x="Cell", by.y="row.names")
cds_exprs$adjusted_expression <- log10(cds_exprs$expression)
#cds_exprs$adjusted_expression <- log10(cds_exprs$adjusted_expression + abs(rnorm(nrow(cds_exprs), min_expr, sqrt(min_expr))))
if (label_by_short_name == TRUE){
if (is.null(cds_exprs$gene_short_name) == FALSE){
cds_exprs$feature_label <- cds_exprs$gene_short_name
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
}else{
cds_exprs$feature_label <- cds_exprs$f_id
}
}else{
cds_exprs$feature_label <- cds_exprs$f_id
}
#print (head(cds_exprs))
if (is.null(panel_order) == FALSE)
{
cds_exprs$feature_label <- factor(cds_exprs$feature_label, levels=panel_order)
}
q <- ggplot(aes_string(x=grouping, y="expression"), data=cds_exprs)
if (is.null(color_by) == FALSE){
q <- q + geom_jitter(aes_string(color=color_by), size=I(cell_size))
}else{
q <- q + geom_jitter(size=I(cell_size))
}
if (plot_trend == TRUE){
q <- q + stat_summary(aes_string(color=color_by), fun.data = "mean_cl_boot", size=0.35)
q <- q + stat_summary(aes_string(x=grouping, y="expression", color=color_by, group=color_by), fun.data = "mean_cl_boot", size=0.35, geom="line")
}
q <- q + scale_y_log10() + facet_wrap(~feature_label, nrow=nrow, ncol=ncol, scales="free_y")
# Need this to guard against plotting failures caused by non-expressed genes
if (min_expr < 1)
{
q <- q + expand_limits(y=c(min_expr, 1))
}
q <- q + ylab("Expression") + xlab(grouping)
q <- q + monocle_theme_opts()
q
}
#' Plots the number of cells expressing one or more genes as a barplot
#'
#' @description Accetps a CellDataSet and a parameter,"grouping", used for dividing cells into groups.
#' Returns one or more bar graphs (one graph for each gene in the CellDataSet).
#' Each graph shows the percentage of cells that express a gene in the in the CellDataSet for
#' each sub-group of cells created by "grouping".
#'
#' Let's say the CellDataSet passed in included genes A, B, and C and the "grouping parameter divided
#' all of the cells into three groups called X, Y, and Z. Then three graphs would be produced called A,
#' B, and C. In the A graph there would be three bars one for X, one for Y, and one for Z. So X bar in the
#' A graph would show the percentage of cells in the X group that express gene A.
#'
#' @param cds_subset CellDataSet for the experiment
#' @param grouping the cell attribute (e.g. the column of pData(cds)) to group cells by on the horizontal axis
#' @param min_expr the minimum (untransformed) expression level to use in plotted the genes.
#' @param nrow the number of rows used when laying out the panels for each gene's expression
#' @param ncol the number of columns used when laying out the panels for each gene's expression
#' @param panel_order the order in which genes should be layed out (left-to-right, top-to-bottom)
#' @param plot_as_fraction whether to show the percent instead of the number of cells expressing each gene
#' @param label_by_short_name label figure panels by gene_short_name (TRUE) or feature id (FALSE)
#' @param relative_expr Whether to transform expression into relative values
#' @param plot_limits A pair of number specifying the limits of the y axis. If NULL, scale to the range of the data.
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom plyr ddply
#' @importFrom reshape2 melt
#' @importFrom BiocGenerics sizeFactors
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' MYOG_ID1 <- HSMM[row.names(subset(fData(HSMM), gene_short_name %in% c("MYOG", "ID1"))),]
#' plot_genes_positive_cells(MYOG_ID1, grouping="Media", ncol=2)
#' }
plot_genes_positive_cells <- function(cds_subset,
grouping = "State",
min_expr=0.1,
nrow=NULL,
ncol=1,
panel_order=NULL,
plot_as_fraction=TRUE,
label_by_short_name=TRUE,
relative_expr=TRUE,
plot_limits=c(0,100)){
percent <- NULL
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")){
integer_expression <- TRUE
}else{
integer_expression <- FALSE
relative_expr <- TRUE
}
if (integer_expression)
{
marker_exprs <- exprs(cds_subset)
if (relative_expr){
if (is.null(sizeFactors(cds_subset)))
{
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
marker_exprs <- Matrix::t(Matrix::t(marker_exprs) / sizeFactors(cds_subset))
}
marker_exprs_melted <- reshape2::melt(round(as.matrix(marker_exprs)))
}else{
marker_exprs_melted <- reshape2::melt(as.matrix(exprs(cds_subset)))
}
colnames(marker_exprs_melted) <- c("f_id", "Cell", "expression")
marker_exprs_melted <- merge(marker_exprs_melted, pData(cds_subset), by.x="Cell", by.y="row.names")
marker_exprs_melted <- merge(marker_exprs_melted, fData(cds_subset), by.x="f_id", by.y="row.names")
if (label_by_short_name == TRUE){
if (is.null(marker_exprs_melted$gene_short_name) == FALSE){
marker_exprs_melted$feature_label <- marker_exprs_melted$gene_short_name
marker_exprs_melted$feature_label[is.na(marker_exprs_melted$feature_label)] <- marker_exprs_melted$f_id
}else{
marker_exprs_melted$feature_label <- marker_exprs_melted$f_id
}
}else{
marker_exprs_melted$feature_label <- marker_exprs_melted$f_id
}
if (is.null(panel_order) == FALSE)
{
marker_exprs_melted$feature_label <- factor(marker_exprs_melted$feature_label, levels=panel_order)
}
marker_counts <- plyr::ddply(marker_exprs_melted, c("feature_label", grouping), function(x) {
data.frame(target=sum(x$expression > min_expr),
target_fraction=sum(x$expression > min_expr)/nrow(x)) } )
#print (head(marker_counts))
if (plot_as_fraction){
marker_counts$target_fraction <- marker_counts$target_fraction * 100
qp <- ggplot(aes_string(x=grouping, y="target_fraction", fill=grouping), data=marker_counts) +
ylab("Cells (percent)")
if (is.null(plot_limits) == FALSE)
qp <- qp + scale_y_continuous(limits=plot_limits)
}else{
qp <- ggplot(aes_string(x=grouping, y="target", fill=grouping), data=marker_counts) +
ylab("Cells")
}
qp <- qp + facet_wrap(~feature_label, nrow=nrow, ncol=ncol, scales="free_y")
qp <- qp + geom_bar(stat="identity") + monocle_theme_opts()
return(qp)
}
#' Plots expression for one or more genes as a function of pseudotime
#'
#' @description Plots expression for one or more genes as a function of pseudotime.
#' Plotting allows you determine if the ordering produced by orderCells() is correct
#' and it does not need to be flipped using the "reverse" flag in orderCells
#'
#' @param cds_subset CellDataSet for the experiment
#' @param min_expr the minimum (untransformed) expression level to use in plotted the genes.
#' @param cell_size the size (in points) of each cell used in the plot
#' @param nrow the number of rows used when laying out the panels for each gene's expression
#' @param ncol the number of columns used when laying out the panels for each gene's expression
#' @param panel_order the order in which genes should be layed out (left-to-right, top-to-bottom)
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to be used to color each cell
#' @param trend_formula the model formula to be used for fitting the expression trend over pseudotime
#' @param label_by_short_name label figure panels by gene_short_name (TRUE) or feature id (FALSE)
#' @param relative_expr Whether to transform expression into relative values
#' @param vertical_jitter A value passed to ggplot to jitter the points in the vertical dimension. Prevents overplotting, and is particularly helpful for rounded transcript count data.
#' @param horizontal_jitter A value passed to ggplot to jitter the points in the horizontal dimension. Prevents overplotting, and is particularly helpful for rounded transcript count data.
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom plyr ddply .
#' @importFrom reshape2 melt
#' @importFrom ggplot2 Position
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' my_genes <- row.names(subset(fData(HSMM), gene_short_name %in% c("CDK1", "MEF2C", "MYH3")))
#' cds_subset <- HSMM[my_genes,]
#' plot_genes_in_pseudotime(cds_subset, color_by="Time")
#' }
plot_genes_in_pseudotime <-function(cds_subset,
min_expr=NULL,
cell_size=0.75,
nrow=NULL,
ncol=1,
panel_order=NULL,
color_by="State",
trend_formula="~ sm.ns(Pseudotime, df=3)",
label_by_short_name=TRUE,
relative_expr=TRUE,
vertical_jitter=NULL,
horizontal_jitter=NULL){
f_id <- NA
Cell <- NA
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")) {
integer_expression <- TRUE
}
else {
integer_expression <- FALSE
relative_expr <- TRUE
}
if (integer_expression) {
cds_exprs <- exprs(cds_subset)
if (relative_expr) {
if (is.null(sizeFactors(cds_subset))) {
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
cds_exprs <- Matrix::t(Matrix::t(cds_exprs)/sizeFactors(cds_subset))
}
cds_exprs <- reshape2::melt(round(as.matrix(cds_exprs)))
}
else {
cds_exprs <- reshape2::melt(as.matrix(exprs(cds_subset)))
}
if (is.null(min_expr)) {
min_expr <- cds_subset@lowerDetectionLimit
}
colnames(cds_exprs) <- c("f_id", "Cell", "expression")
cds_pData <- pData(cds_subset)
cds_fData <- fData(cds_subset)
cds_exprs <- merge(cds_exprs, cds_fData, by.x = "f_id", by.y = "row.names")
cds_exprs <- merge(cds_exprs, cds_pData, by.x = "Cell", by.y = "row.names")
#cds_exprs$f_id <- as.character(cds_exprs$f_id)
#cds_exprs$Cell <- as.character(cds_exprs$Cell)
if (integer_expression) {
cds_exprs$adjusted_expression <- cds_exprs$expression
}
else {
cds_exprs$adjusted_expression <- log10(cds_exprs$expression)
}
# trend_formula <- paste("adjusted_expression", trend_formula,
# sep = "")
if (label_by_short_name == TRUE) {
if (is.null(cds_exprs$gene_short_name) == FALSE) {
cds_exprs$feature_label <- as.character(cds_exprs$gene_short_name)
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
cds_exprs$f_id <- as.character(cds_exprs$f_id)
cds_exprs$feature_label <- factor(cds_exprs$feature_label)
new_data <- data.frame(Pseudotime = pData(cds_subset)$Pseudotime)
model_expectation <- genSmoothCurves(cds_subset, cores=1, trend_formula = trend_formula,
relative_expr = T, new_data = new_data)
colnames(model_expectation) <- colnames(cds_subset)
expectation <- ddply(cds_exprs, .(f_id, Cell), function(x) data.frame("expectation"=model_expectation[x$f_id, x$Cell]))
cds_exprs <- merge(cds_exprs, expectation)
#cds_exprs$expectation <- expectation#apply(cds_exprs,1, function(x) model_expectation[x$f_id, x$Cell])
cds_exprs$expression[cds_exprs$expression < min_expr] <- min_expr
cds_exprs$expectation[cds_exprs$expectation < min_expr] <- min_expr
if (is.null(panel_order) == FALSE) {
cds_exprs$feature_label <- factor(cds_exprs$feature_label,
levels = panel_order)
}
q <- ggplot(aes(Pseudotime, expression), data = cds_exprs)
if (is.null(color_by) == FALSE) {
q <- q + geom_point(aes_string(color = color_by), size = I(cell_size), position=position_jitter(horizontal_jitter, vertical_jitter))
}
else {
q <- q + geom_point(size = I(cell_size), position=position_jitter(horizontal_jitter, vertical_jitter))
}
q <- q + geom_line(aes(x = Pseudotime, y = expectation), data = cds_exprs)
q <- q + scale_y_log10() + facet_wrap(~feature_label, nrow = nrow,
ncol = ncol, scales = "free_y")
if (min_expr < 1) {
q <- q + expand_limits(y = c(min_expr, 1))
}
if (relative_expr) {
q <- q + ylab("Relative Expression")
}
else {
q <- q + ylab("Absolute Expression")
}
q <- q + xlab("Pseudo-time")
q <- q + monocle_theme_opts()
q
}
#' Plots kinetic clusters of genes.
#'
#' @description returns a ggplot2 object showing the shapes of the
#' expression patterns followed by a set of pre-selected genes.
#' The topographic lines highlight the distributions of the kinetic patterns
#' relative to overall trend lines.
#'
#' @param cds CellDataSet for the experiment
#' @param clustering a clustering object produced by clusterCells
#' @param drawSummary whether to draw the summary line for each cluster
#' @param sumFun whether the function used to generate the summary for each cluster
#' @param ncol number of columns used to layout the faceted cluster panels
#' @param nrow number of columns used to layout the faceted cluster panels
#' @param row_samples how many genes to randomly select from the data
#' @param callout_ids a vector of gene names or gene ids to manually render as part of the plot
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
#' @importFrom stringr str_c
#' @importFrom ggplot2 Position
#' @import grid
#' @export
#' @examples
#' \dontrun{
#' full_model_fits <- fitModel(HSMM_filtered[sample(nrow(fData(HSMM_filtered)), 100),],
#' modelFormulaStr="~VGAM::bs(Pseudotime)")
#' expression_curve_matrix <- responseMatrix(full_model_fits)
#' clusters <- clusterGenes(expression_curve_matrix, k=4)
#' plot_clusters(HSMM_filtered[ordering_genes,], clusters)
#' }
plot_clusters<-function(cds,
clustering,
drawSummary=TRUE,
sumFun=mean_cl_boot,
ncol=NULL,
nrow=NULL,
row_samples=NULL,
callout_ids=NULL){
.Deprecated("plot_genes_heatmap")
m <- as.data.frame(clustering$exprs)
m$ids <- rownames(clustering$exprs)
if (is.null(clustering$labels) == FALSE)
{
m$cluster = factor(clustering$labels[clustering$clustering], levels = levels(clustering$labels))
}else{
m$cluster <- factor(clustering$clustering)
}
cluster_sizes <- as.data.frame(table(m$cluster))
cluster_sizes$Freq <- paste("(", cluster_sizes$Freq, ")")
facet_labels <- str_c(cluster_sizes$Var1, cluster_sizes$Freq, sep=" ") #update the function
m.melt <- melt(m, id.vars = c("ids", "cluster"))
m.melt <- merge(m.melt, pData(cds), by.x="variable", by.y="row.names")
if (is.null(row_samples) == FALSE){
m.melt <- m.melt[sample(nrow(m.melt), row_samples),]
}
c <- ggplot(m.melt) + facet_wrap("cluster", ncol=ncol, nrow=nrow, scales="free_y")
#c <- c + stat_density2d(aes(x = Pseudotime, y = value), geom="polygon", fill="white", color="black", size=I(0.1)) + facet_wrap("cluster", ncol=ncol, nrow=nrow)
if (drawSummary) {
c <- c + stat_summary(aes(x = Pseudotime, y = value, group = 1),
fun.data = sumFun, color = "red",
alpha = 0.2, size = 0.5, geom = "smooth")
}
#cluster_medians <- subset(m.melt, ids %in% clustering$medoids)
#c <- c + geom_line()
#c <- c + geom_line(aes(x=Pseudotime, y=value), data=cluster_medians, color=I("red"))
c <- c + scale_color_hue(l = 50, h.start = 200) + theme(axis.text.x = element_text(angle = 0,
hjust = 0)) + xlab("Pseudo-time") + ylab("Expression")
c <- c + theme(strip.background = element_rect(colour = 'white', fill = 'white')) +
theme(panel.border = element_blank()) +
theme(legend.position="none") +
theme(panel.grid.minor.x = element_blank(), panel.grid.minor.y = element_blank()) +
theme(panel.grid.major.x = element_blank(), panel.grid.major.y = element_blank())
# if (draw_cluster_size){
# cluster_sizes <- as.data.frame(table(m$cluster))
# colnames(cluster_sizes) <- c("cluster", "Freq")
# cluster_sizes <- cbind (cluster_sizes, Pseudotime = cluster_label_text_x, value = cluster_label_text_y)
# c <- c + geom_text(aes(x=Pseudotime, y=value, label=Freq), data=cluster_sizes, size=cluster_label_text_size)
# }
if (is.null(callout_ids) == FALSE)
{
callout_melt <- subset(m.melt, ids %in% callout_ids)
c <- c + geom_line(aes(x=Pseudotime, y=value), data=callout_melt, color=I("steelblue"))
}
c <- c + monocle_theme_opts()
#c <- facet_wrap_labeller(c, facet_labels)
c
}
#
# #' Plots a pseudotime-ordered, row-centered heatmap
# #' @export
# plot_genes_heatmap <- function(cds,
# rescaling='row',
# clustering='row',
# labCol=FALSE,
# labRow=TRUE,
# logMode=TRUE,
# use_vst=TRUE,
# border=FALSE,
# heatscale=c(low='steelblue',mid='white',high='tomato'),
# heatMidpoint=0,
# method="none",
# scaleMax=2,
# scaleMin=-2,
# relative_expr=TRUE,
# ...){
#
# ## the function can be be viewed as a two step process
# ## 1. using the rehape package and other funcs the data is clustered, scaled, and reshaped
# ## using simple options or by a user supplied function
# ## 2. with the now resahped data the plot, the chosen labels and plot style are built
# FM <- exprs(cds)
#
# if (cds@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")){
# integer_expression <- TRUE
# }else{
# integer_expression <- FALSE
# relative_expr <- TRUE
# }
#
# if (integer_expression)
# {
# if (relative_expr){
# if (is.null(sizeFactors(cds)))
# {
# stop("Error: you must call estimateSizeFactors() first")
# }
# FM <- Matrix::t(Matrix::t(FM) / sizeFactors(cds))
# }
# FM <- round(FM)
# }
#
# m=FM
#
# if (is.null(fData(cds)$gene_short_name) == FALSE){
# feature_labels <- fData(cds)$gene_short_name
# feature_labels[is.na(feature_labels)] <- fData(cds)$f_id
# row.names(m) <- feature_labels
# }
#
# #remove genes with no expression in any condition
# m=m[!apply(m,1,sum)==0,]
#
# if (use_vst && is.null(cds@dispFitInfo[["blind"]]$disp_func) == FALSE){
# m = vstExprs(cds, expr_matrix=m)
# }else if(logMode){
# m = log10(m+pseudocount)
# }
#
# #remove genes with no sd
# #m=m[!apply(m,1,sd)==0,]
#
# ## you can either scale by row or column not both!
# ## if you wish to scale by both or use a different scale method then simply supply a scale
# ## function instead NB scale is a base funct
#
# ## I have supplied the default cluster and euclidean distance (JSdist) - and chose to cluster after scaling
# ## if you want a different distance/cluster method-- or to cluster and then scale
# ## then you can supply a custom function
#
# if(!is.function(method)){
# method = function(mat){as.dist((1 - cor(Matrix::t(mat)))/2)}
# }
#
# ## this is just reshaping into a ggplot format matrix and making a ggplot layer
#
# if(is.function(rescaling))
# {
# m=rescaling(m)
# } else {
# if(rescaling=='column'){
# m=m[!apply(m,2,sd)==0,]
# m=scale(m, center=TRUE)
# m[is.nan(m)] = 0
# m[m>scaleMax] = scaleMax
# m[m<scaleMin] = scaleMin
# }
# if(rescaling=='row'){
# m=m[!apply(m,1,sd)==0,]
# m=Matrix::t(scale(Matrix::t(m),center=TRUE))
# m[is.nan(m)] = 0
# m[m>scaleMax] = scaleMax
# m[m<scaleMin] = scaleMin
# }
# }
#
# # If we aren't going to re-ordering the columns, order them by Pseudotime
# if (clustering %in% c("row", "none"))
# m = m[,row.names(pData(cds)[order(-pData(cds)$Pseudotime),])]
#
# if(clustering=='row')
# m=m[hclust(method(m))$order, ]
# if(clustering=='column')
# m=m[,hclust(method(Matrix::t(m)))$order]
# if(clustering=='both')
# m=m[hclust(method(m))$order ,hclust(method(Matrix::t(m)))$order]
#
#
# rows=dim(m)[1]
# cols=dim(m)[2]
#
#
#
# # if(logMode) {
# # melt.m=cbind(rowInd=rep(1:rows, times=cols), colInd=rep(1:cols, each=rows), reshape2::melt( log10(m+pseudocount)))
# # }else{
# # melt.m=cbind(rowInd=rep(1:rows, times=cols), colInd=rep(1:cols, each=rows), reshape2::melt(m))
# # }
#
# melt.m=cbind(rowInd=rep(1:rows, times=cols), colInd=rep(1:cols, each=rows), reshape2::melt(m))
#
# g=ggplot(data=melt.m)
#
# ## add the heat tiles with or without a white border for clarity
#
# if(border==TRUE)
# g2=g+geom_raster(aes(x=colInd,y=rowInd, fill=value),colour='grey')
# if(border==FALSE)
# g2=g+geom_raster(aes(x=colInd,y=rowInd,ymax=rowInd, fill=value))
#
# ## add axis labels either supplied or from the colnames rownames of the matrix
#
# if(labCol==TRUE)
# {
# g2=g2+scale_x_continuous(breaks=(1:cols)-0.5, labels=colnames(m))
# }
# if(labCol==FALSE)
# {
# g2=g2+scale_x_continuous(breaks=(1:cols)-0.5, labels=rep('',cols))
# }
#
#
# if(labRow==TRUE)
# {
# g2=g2+scale_y_continuous(breaks=(1:rows)-0.5, labels=rownames(m))
# }
# if(labRow==FALSE)
# {
# g2=g2+scale_y_continuous(breaks=(1:rows)-0.5, labels=rep('',rows))
# }
#
# # Get rid of the ticks, they get way too dense with lots of rows
# g2 <- g2 + theme(axis.ticks = element_blank())
#
# ## get rid of grey panel background and gridlines
#
# g2=g2+theme(panel.grid.minor=element_line(colour=NA), panel.grid.major=element_line(colour=NA),
# panel.background=element_rect(fill=NA, colour=NA))
#
# ##adjust x-axis labels
# g2=g2+theme(axis.text.x=element_text(angle=-90, hjust=0))
#
# #write(paste(c("Length of heatscale is :", length(heatscale))), stderr())
#
# if(is.function(rescaling))
# {
#
# }else{
# if(rescaling=='row' || rescaling == 'column'){
# legendTitle <- "Relative\nexpression"
# }else{
# if (logMode)
# {
# legendTitle <- bquote(paste(log[10]," FPKM + ",.(pseudocount),sep=""))
# #legendTitle <- paste(expression(plain(log)[10])," FPKM + ",pseudocount,sep="")
# } else {
# legendTitle <- "FPKM"
# }
# }
# }
#
# if (length(heatscale) == 2){
# g2 <- g2 + scale_fill_gradient(low=heatscale[1], high=heatscale[2], name=legendTitle)
# } else if (length(heatscale) == 3) {
# if (is.null(heatMidpoint))
# {
# heatMidpoint = (max(m) + min(m)) / 2.0
# #write(heatMidpoint, stderr())
# }
# g2 <- g2 + theme(panel.border = element_blank())
# g2 <- g2 + scale_fill_gradient2(low=heatscale[1], mid=heatscale[2], high=heatscale[3], midpoint=heatMidpoint, name=legendTitle)
# }else {
# g2 <- g2 + scale_fill_gradientn(colours=heatscale, name=legendTitle)
# }
#
# #g2<-g2+scale_x_discrete("",breaks=tracking_ids,labels=gene_short_names)
#
# g2 <- g2 + theme(axis.title.x=element_blank(), axis.title.y=element_blank())
#
# ## finally add the fill colour ramp of your choice (default is blue to red)-- and return
# return (g2)
# }
plot_genes_heatmap <- function(...){
.Deprecated("plot_pseudotime_heatmap")
plot_pseudotime_heatmap(...)
}
#' Plots a pseudotime-ordered, row-centered heatmap
#'
#' @description The function plot_pseudotime_heatmap takes a CellDataSet object
#' (usually containing a only subset of significant genes) and generates smooth expression
#' curves much like plot_genes_in_pseudotime.
#' Then, it clusters these genes and plots them using the pheatmap package.
#' This allows you to visualize modules of genes that co-vary across pseudotime.
#'
#' @param cds_subset CellDataSet for the experiment (normally only the branching genes detected with branchTest)
#' @param cluster_rows Whether to cluster the rows of the heatmap.
#' @param hclust_method The method used by pheatmap to perform hirearchical clustering of the rows.
#' @param num_clusters Number of clusters for the heatmap of branch genes
#' @param hmcols The color scheme for drawing the heatmap.
#' @param add_annotation_row Additional annotations to show for each row in the heatmap. Must be a dataframe with one row for each row in the fData table of cds_subset, with matching IDs.
#' @param add_annotation_col Additional annotations to show for each column in the heatmap. Must be a dataframe with one row for each cell in the pData table of cds_subset, with matching IDs.
#' @param show_rownames Whether to show the names for each row in the table.
#' @param use_gene_short_name Whether to use the short names for each row. If FALSE, uses row IDs from the fData table.
#' @param scale_max The maximum value (in standard deviations) to show in the heatmap. Values larger than this are set to the max.
#' @param scale_min The minimum value (in standard deviations) to show in the heatmap. Values smaller than this are set to the min.
#' @param norm_method Determines how to transform expression values prior to rendering
#' @param trend_formula A formula string specifying the model used in fitting the spline curve for each gene/feature.
#' @param return_heatmap Whether to return the pheatmap object to the user.
#' @param cores Number of cores to use when smoothing the expression curves shown in the heatmap.
#' @return A list of heatmap_matrix (expression matrix for the branch committment), ph (pheatmap heatmap object),
#' annotation_row (annotation data.frame for the row), annotation_col (annotation data.frame for the column).
#' @import pheatmap
#' @importFrom stats sd as.dist cor cutree
#' @export
#'
plot_pseudotime_heatmap <- function(cds_subset,
cluster_rows = TRUE,
hclust_method = "ward.D2",
num_clusters = 6,
hmcols = NULL,
add_annotation_row = NULL,
add_annotation_col = NULL,
show_rownames = FALSE,
use_gene_short_name = TRUE,
norm_method = c("log", "vstExprs"),
scale_max=3,
scale_min=-3,
trend_formula = '~sm.ns(Pseudotime, df=3)',
return_heatmap=FALSE,
cores=1){
num_clusters <- min(num_clusters, nrow(cds_subset))
pseudocount <- 1
newdata <- data.frame(Pseudotime = seq(min(pData(cds_subset)$Pseudotime), max(pData(cds_subset)$Pseudotime),length.out = 100))
m <- genSmoothCurves(cds_subset, cores=cores, trend_formula = trend_formula,
relative_expr = T, new_data = newdata)
#remove genes with no expression in any condition
m=m[!apply(m,1,sum)==0,]
norm_method <- match.arg(norm_method)
# FIXME: this needs to check that vst values can even be computed. (They can only be if we're using NB as the expressionFamily)
if(norm_method == 'vstExprs' && is.null(cds_subset@dispFitInfo[["blind"]]$disp_func) == FALSE) {
m = vstExprs(cds_subset, expr_matrix=m)
}
else if(norm_method == 'log') {
m = log10(m+pseudocount)
}
# Row-center the data.
m=m[!apply(m,1,sd)==0,]
m=Matrix::t(scale(Matrix::t(m),center=TRUE))
m=m[is.na(row.names(m)) == FALSE,]
m[is.nan(m)] = 0
m[m>scale_max] = scale_max
m[m<scale_min] = scale_min
heatmap_matrix <- m
row_dist <- as.dist((1 - cor(Matrix::t(heatmap_matrix)))/2)
row_dist[is.na(row_dist)] <- 1
if(is.null(hmcols)) {
bks <- seq(-3.1,3.1, by = 0.1)
hmcols <- blue2green2red(length(bks) - 1)
}
else {
bks <- seq(-3.1,3.1, length.out = length(hmcols))
}
ph <- pheatmap(heatmap_matrix,
useRaster = T,
cluster_cols=FALSE,
cluster_rows=cluster_rows,
show_rownames=F,
show_colnames=F,
clustering_distance_rows=row_dist,
clustering_method = hclust_method,
cutree_rows=num_clusters,
silent=TRUE,
filename=NA,
breaks=bks,
border_color = NA,
color=hmcols)
if(cluster_rows) {
annotation_row <- data.frame(Cluster=factor(cutree(ph$tree_row, num_clusters)))
} else {
annotation_row <- NULL
}
if(!is.null(add_annotation_row)) {
old_colnames_length <- ncol(annotation_row)
annotation_row <- cbind(annotation_row, add_annotation_row[row.names(annotation_row), ])
colnames(annotation_row)[(old_colnames_length+1):ncol(annotation_row)] <- colnames(add_annotation_row)
# annotation_row$bif_time <- add_annotation_row[as.character(fData(absolute_cds[row.names(annotation_row), ])$gene_short_name), 1]
}
if(!is.null(add_annotation_col)) {
if(nrow(add_annotation_col) != 100) {
stop('add_annotation_col should have only 100 rows (check genSmoothCurves before you supply the annotation data)!')
}
annotation_col <- add_annotation_col
} else {
annotation_col <- NA
}
if (use_gene_short_name == TRUE) {
if (is.null(fData(cds_subset)$gene_short_name) == FALSE) {
feature_label <- as.character(fData(cds_subset)[row.names(heatmap_matrix), 'gene_short_name'])
feature_label[is.na(feature_label)] <- row.names(heatmap_matrix)
row_ann_labels <- as.character(fData(cds_subset)[row.names(annotation_row), 'gene_short_name'])
row_ann_labels[is.na(row_ann_labels)] <- row.names(annotation_row)
}
else {
feature_label <- row.names(heatmap_matrix)
row_ann_labels <- row.names(annotation_row)
}
}
else {
feature_label <- row.names(heatmap_matrix)
if(!is.null(annotation_row))
row_ann_labels <- row.names(annotation_row)
}
row.names(heatmap_matrix) <- feature_label
if(!is.null(annotation_row))
row.names(annotation_row) <- row_ann_labels
colnames(heatmap_matrix) <- c(1:ncol(heatmap_matrix))
ph_res <- pheatmap(heatmap_matrix[, ], #ph$tree_row$order
useRaster = T,
cluster_cols = FALSE,
cluster_rows = cluster_rows,
show_rownames=show_rownames,
show_colnames=F,
#scale="row",
clustering_distance_rows=row_dist, #row_dist
clustering_method = hclust_method, #ward.D2
cutree_rows=num_clusters,
# cutree_cols = 2,
annotation_row=annotation_row,
annotation_col=annotation_col,
treeheight_row = 20,
breaks=bks,
fontsize = 6,
color=hmcols,
border_color = NA,
silent=TRUE,
filename=NA
)
grid::grid.rect(gp=grid::gpar("fill", col=NA))
grid::grid.draw(ph_res$gtable)
if (return_heatmap){
return(ph_res)
}
}
#' Plot the branch genes in pseduotime with separate branch curves.
#'
#' @description Works similarly to plot_genes_in_psuedotime esceptit shows
#' one kinetic trend for each lineage.
#'
#' @details This plotting function is used to make the branching plots for a branch dependent gene goes through the progenitor state
#' and bifurcating into two distinct branchs (Similar to the pitch-fork bifurcation in dynamic systems). In order to make the
#' bifurcation plot, we first duplicated the progenitor states and by default stretch each branch into maturation level 0-100.
#' Then we fit two nature spline curves for each branchs using VGAM package.
#'
#' @param cds CellDataSet for the experiment
#' @param branch_states The states for two branching branchs
#' @param branch_point The ID of the branch point to analyze. Can only be used when reduceDimension is called with method = "DDRTree".
#' @param branch_labels The names for each branching branch
#' @param method The method to draw the curve for the gene expression branching pattern, either loess ('loess') or VGLM fitting ('fitting')
#' @param min_expr The minimum (untransformed) expression level to use in plotted the genes.
#' @param cell_size The size (in points) of each cell used in the plot
#' @param nrow Number of columns used to layout the faceted cluster panels
#' @param ncol Number of columns used to layout the faceted cluster panels
#' @param panel_order The a character vector of gene short names (or IDs, if that's what you're using), specifying order in which genes should be layed out (left-to-right, top-to-bottom)
#' @param color_by The cell attribute (e.g. the column of pData(cds)) to be used to color each cell
#' @param expression_curve_linetype_by The cell attribute (e.g. the column of pData(cds)) to be used for the linetype of each branch curve
#' @param trend_formula The model formula to be used for fitting the expression trend over pseudotime
#' @param reducedModelFormulaStr A formula specifying a null model. If used, the plot shows a p value from the likelihood ratio test that uses trend_formula as the full model
#' @param label_by_short_name Whether to label figure panels by gene_short_name (TRUE) or feature id (FALSE)
#' @param relative_expr Whether or not the plot should use relative expression values (only relevant for CellDataSets using transcript counts)
#' @param ... Additional arguments passed on to branchTest. Only used when reducedModelFormulaStr is not NULL.
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom plyr ddply
#' @importFrom reshape2 melt
#' @importFrom BiocGenerics sizeFactors
#' @export
plot_genes_branched_pseudotime <- function (cds,
branch_states = NULL,
branch_point=1,
branch_labels = NULL,
method = "fitting",
min_expr = NULL,
cell_size = 0.75,
nrow = NULL,
ncol = 1,
panel_order = NULL,
color_by = "State",
expression_curve_linetype_by = "Branch",
trend_formula = "~ sm.ns(Pseudotime, df=3) * Branch",
reducedModelFormulaStr = NULL,
label_by_short_name = TRUE,
relative_expr = TRUE,
#gene_pairs = NULL,
...)
{
Branch <- NA
if (is.null(reducedModelFormulaStr) == FALSE) {
pval_df <- branchTest(cds,
branch_states=branch_states,
branch_point=branch_point,
fullModelFormulaStr = trend_formula,
reducedModelFormulaStr = "~ sm.ns(Pseudotime, df=3)",
...)
fData(cds)[, "pval"] <- pval_df[row.names(cds), 'pval']
}
if("Branch" %in% all.vars(terms(as.formula(trend_formula)))) { #only when Branch is in the model formula we will duplicate the "progenitor" cells
cds_subset <- buildBranchCellDataSet(cds = cds,
branch_states = branch_states,
branch_point=branch_point,
branch_labels = branch_labels,
progenitor_method = 'duplicate',
...)
}
else {
cds_subset <- cds
pData(cds_subset)$Branch <- pData(cds_subset)$State
}
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")) {
integer_expression <- TRUE
}
else {
integer_expression <- FALSE
}
if (integer_expression) {
CM <- exprs(cds_subset)
if (relative_expr){
if (is.null(sizeFactors(cds_subset))) {
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
CM <- Matrix::t(Matrix::t(CM)/sizeFactors(cds_subset))
}
cds_exprs <- reshape2::melt(round(as.matrix(CM)))
}
else {
cds_exprs <- reshape2::melt(exprs(cds_subset))
}
if (is.null(min_expr)) {
min_expr <- cds_subset@lowerDetectionLimit
}
colnames(cds_exprs) <- c("f_id", "Cell", "expression")
cds_pData <- pData(cds_subset)
cds_fData <- fData(cds_subset)
cds_exprs <- merge(cds_exprs, cds_fData, by.x = "f_id", by.y = "row.names")
cds_exprs <- merge(cds_exprs, cds_pData, by.x = "Cell", by.y = "row.names")
if (integer_expression) {
cds_exprs$adjusted_expression <- round(cds_exprs$expression)
}
else {
cds_exprs$adjusted_expression <- log10(cds_exprs$expression)
}
if (label_by_short_name == TRUE) {
if (is.null(cds_exprs$gene_short_name) == FALSE) {
cds_exprs$feature_label <- as.character(cds_exprs$gene_short_name)
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
cds_exprs$feature_label <- as.factor(cds_exprs$feature_label)
# trend_formula <- paste("adjusted_expression", trend_formula,
# sep = "")
cds_exprs$Branch <- as.factor(cds_exprs$Branch)
new_data <- data.frame(Pseudotime = pData(cds_subset)$Pseudotime, Branch = pData(cds_subset)$Branch)
full_model_expectation <- genSmoothCurves(cds_subset, cores=1, trend_formula = trend_formula,
relative_expr = T, new_data = new_data)
colnames(full_model_expectation) <- colnames(cds_subset)
cds_exprs$full_model_expectation <- apply(cds_exprs,1, function(x) full_model_expectation[x[2], x[1]])
if(!is.null(reducedModelFormulaStr)){
reduced_model_expectation <- genSmoothCurves(cds_subset, cores=1, trend_formula = reducedModelFormulaStr,
relative_expr = T, new_data = new_data)
colnames(reduced_model_expectation) <- colnames(cds_subset)
cds_exprs$reduced_model_expectation <- apply(cds_exprs,1, function(x) reduced_model_expectation[x[2], x[1]])
}
# FIXME: If you want to show the bifurcation time for each gene, this function
# should just compute it. Passing it in as a dataframe is just too complicated
# and will be hard on the user.
# if(!is.null(bifurcation_time)){
# cds_exprs$bifurcation_time <- bifurcation_time[as.vector(cds_exprs$gene_short_name)]
# }
if (method == "loess")
cds_exprs$expression <- cds_exprs$expression + cds@lowerDetectionLimit
if (label_by_short_name == TRUE) {
if (is.null(cds_exprs$gene_short_name) == FALSE) {
cds_exprs$feature_label <- as.character(cds_exprs$gene_short_name)
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
cds_exprs$feature_label <- factor(cds_exprs$feature_label)
if (is.null(panel_order) == FALSE) {
cds_exprs$feature_label <- factor(cds_exprs$feature_label,
levels = panel_order)
}
cds_exprs$expression[is.na(cds_exprs$expression)] <- min_expr
cds_exprs$expression[cds_exprs$expression < min_expr] <- min_expr
cds_exprs$full_model_expectation[is.na(cds_exprs$full_model_expectation)] <- min_expr
cds_exprs$full_model_expectation[cds_exprs$full_model_expectation < min_expr] <- min_expr
if(!is.null(reducedModelFormulaStr)){
cds_exprs$reduced_model_expectation[is.na(cds_exprs$reduced_model_expectation)] <- min_expr
cds_exprs$reduced_model_expectation[cds_exprs$reduced_model_expectation < min_expr] <- min_expr
}
cds_exprs$State <- as.factor(cds_exprs$State)
cds_exprs$Branch <- as.factor(cds_exprs$Branch)
q <- ggplot(aes(Pseudotime, expression), data = cds_exprs)
# if (!is.null(bifurcation_time)) {
# q <- q + geom_vline(aes(xintercept = bifurcation_time),
# color = "black", linetype = "longdash")
# }
if (is.null(color_by) == FALSE) {
q <- q + geom_point(aes_string(color = color_by), size = I(cell_size))
}
if (is.null(reducedModelFormulaStr) == FALSE)
q <- q + scale_y_log10() + facet_wrap(~feature_label +
pval, nrow = nrow, ncol = ncol, scales = "free_y")
else q <- q + scale_y_log10() + facet_wrap(~feature_label,
nrow = nrow, ncol = ncol, scales = "free_y")
if (method == "loess")
q <- q + stat_smooth(aes(fill = Branch, color = Branch),
method = "loess")
else if (method == "fitting") {
q <- q + geom_line(aes_string(x = "Pseudotime", y = "full_model_expectation",
linetype = "Branch"), data = cds_exprs) #+ scale_color_manual(name = "Type", values = c(colour_cell, colour), labels = c("Pre-branch", "AT1", "AT2", "AT1", "AT2")
}
if(!is.null(reducedModelFormulaStr)) {
q <- q + geom_line(aes_string(x = "Pseudotime", y = "reduced_model_expectation"),
color = 'black', linetype = 2, data = cds_exprs)
}
q <- q + ylab("Expression") + xlab("Pseudotime (stretched)")
q <- q + monocle_theme_opts()
q + expand_limits(y = min_expr)
}
#' Not sure we're ready to release this one quite yet:
#' Plot the branch genes in pseduotime with separate branch curves
#' @param cds CellDataSet for the experiment
#' @param rowgenes Gene ids or short names to be arrayed on the vertical axis.
#' @param colgenes Gene ids or short names to be arrayed on the horizontal axis
#' @param relative_expr Whether to transform expression into relative values
#' @param min_expr The minimum level of expression to show in the plot
#' @param cell_size A number how large the cells should be in the plot
#' @param label_by_short_name a boolean that indicates whether cells should be labeled by their short name
#' @param show_density a boolean that indicates whether a 2D density estimation should be shown in the plot
#' @param round_expr a boolean that indicates whether cds_expr values should be rounded or not
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
plot_coexpression_matrix <- function(cds,
rowgenes,
colgenes,
relative_expr=TRUE,
min_expr=NULL,
cell_size=0.85,
label_by_short_name=TRUE,
show_density=TRUE,
round_expr=FALSE){
gene_short_name <- NA
f_id <- NA
adjusted_expression.x <- NULL
adjusted_expression.y <- NULL
..density.. <- NULL
row_gene_ids <- row.names(subset(fData(cds), gene_short_name %in% rowgenes))
row_gene_ids <- union(row_gene_ids, intersect(rowgenes, row.names(fData(cds))))
col_gene_ids <- row.names(subset(fData(cds), gene_short_name %in% colgenes))
col_gene_ids <- union(col_gene_ids, intersect(colgenes, row.names(fData(cds))))
cds_subset <- cds[union(row_gene_ids, col_gene_ids),]
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")){
integer_expression <- TRUE
}else{
integer_expression <- FALSE
relative_expr <- TRUE
}
if (integer_expression)
{
cds_exprs <- exprs(cds_subset)
if (relative_expr){
if (is.null(sizeFactors(cds_subset)))
{
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
cds_exprs <- Matrix::t(Matrix::t(cds_exprs) / sizeFactors(cds_subset))
}
if (round_expr){
cds_exprs <- reshape2::melt(round(as.matrix(cds_exprs)))
} else {
cds_exprs <- reshape2::melt(as.matrix(cds_exprs))
}
}else{
cds_exprs <- reshape2::melt(exprs(cds_subset))
}
if (is.null(min_expr)){
min_expr <- cds_subset@lowerDetectionLimit
}
colnames(cds_exprs) <- c("f_id", "Cell", "expression")
cds_exprs$expression[cds_exprs$expression < min_expr] <- min_expr
cds_pData <- pData(cds_subset)
cds_fData <- fData(cds_subset)
cds_exprs <- merge(cds_exprs, cds_fData, by.x="f_id", by.y="row.names")
cds_exprs$adjusted_expression <- cds_exprs$expression
#cds_exprs$adjusted_expression <- log10(cds_exprs$adjusted_expression + abs(rnorm(nrow(cds_exprs), min_expr, sqrt(min_expr))))
if (label_by_short_name == TRUE){
if (is.null(cds_exprs$gene_short_name) == FALSE){
cds_exprs$feature_label <- as.character(cds_exprs$gene_short_name)
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
}else{
cds_exprs$feature_label <- cds_exprs$f_id
}
}else{
cds_exprs$feature_label <- cds_exprs$f_id
}
cds_exprs$feature_label <- factor(cds_exprs$feature_label)
row_cds_exprs <- subset(cds_exprs, f_id %in% row_gene_ids)
col_cds_exprs <- subset(cds_exprs, f_id %in% col_gene_ids)
joined_exprs <- merge(row_cds_exprs, col_cds_exprs, by="Cell")
cds_exprs <- joined_exprs
cds_exprs <- merge(cds_exprs, cds_pData, by.x="Cell", by.y="row.names")
cds_exprs <- subset(cds_exprs, adjusted_expression.x > min_expr | adjusted_expression.y > min_expr)
q <- ggplot(aes(adjusted_expression.x, adjusted_expression.y), data=cds_exprs, size=I(1))
if (show_density){
q <- q + stat_density2d(geom="raster", aes(fill = ..density..), contour = FALSE) +
scale_fill_gradient(low="white", high="red")
}
q <- q + scale_x_log10() + scale_y_log10() +
geom_point(color=I("black"), size=I(cell_size * 1.50)) +
geom_point(color=I("white"), size=I(cell_size)) +
facet_grid(feature_label.x ~ feature_label.y, scales="free")
#scale_color_brewer(palette="Set1") +
if (min_expr < 1)
{
q <- q + expand_limits(y=c(min_expr, 1), x=c(min_expr, 1))
}
#q <- q + monocle_theme_opts()
q
}
#The following code is swipped from colorRamps package which is used to make the pallette
table.ramp <- function(n, mid = 0.5, sill = 0.5, base = 1, height = 1)
{
x <- seq(0, 1, length.out = n)
y <- rep(0, length(x))
sill.min <- max(c(1, round((n - 1) * (mid - sill / 2)) + 1))
sill.max <- min(c(n, round((n - 1) * (mid + sill / 2)) + 1))
y[sill.min:sill.max] <- 1
base.min <- round((n - 1) * (mid - base / 2)) + 1
base.max <- round((n - 1) * (mid + base / 2)) + 1
xi <- base.min:sill.min
yi <- seq(0, 1, length.out = length(xi))
i <- which(xi > 0 & xi <= n)
y[xi[i]] <- yi[i]
xi <- sill.max:base.max
yi <- seq(1, 0, length.out = length(xi))
i <- which(xi > 0 & xi <= n)
y[xi[i]] <- yi[i]
height * y
}
#' @importFrom grDevices rgb
rgb.tables <- function(n,
red = c(0.75, 0.25, 1),
green = c(0.5, 0.25, 1),
blue = c(0.25, 0.25, 1))
{
rr <- do.call("table.ramp", as.list(c(n, red)))
gr <- do.call("table.ramp", as.list(c(n, green)))
br <- do.call("table.ramp", as.list(c(n, blue)))
rgb(rr, gr, br)
}
matlab.like <- function(n) rgb.tables(n)
matlab.like2 <- function(n)
rgb.tables(n,
red = c(0.8, 0.2, 1),
green = c(0.5, 0.4, 0.8),
blue = c(0.2, 0.2, 1))
blue2green2red <- matlab.like2
#' Create a heatmap to demonstrate the bifurcation of gene expression along two branchs
#'
#' @description returns a heatmap that shows changes in both lineages at the same time.
#' It also requires that you choose a branch point to inspect.
#' Columns are points in pseudotime, rows are genes, and the beginning of pseudotime is in the middle of the heatmap.
#' As you read from the middle of the heatmap to the right, you are following one lineage through pseudotime. As you read left, the other.
#' The genes are clustered hierarchically, so you can visualize modules of genes that have similar lineage-dependent expression patterns.
#'
#' @param cds_subset CellDataSet for the experiment (normally only the branching genes detected with branchTest)
#' @param branch_point The ID of the branch point to visualize. Can only be used when reduceDimension is called with method = "DDRTree".
#' @param branch_states The two states to compare in the heatmap. Mutually exclusive with branch_point.
#' @param branch_labels The labels for the branchs.
#' @param cluster_rows Whether to cluster the rows of the heatmap.
#' @param hclust_method The method used by pheatmap to perform hirearchical clustering of the rows.
#' @param num_clusters Number of clusters for the heatmap of branch genes
#' @param hmcols The color scheme for drawing the heatmap.
#' @param branch_colors The colors used in the annotation strip indicating the pre- and post-branch cells.
#' @param add_annotation_row Additional annotations to show for each row in the heatmap. Must be a dataframe with one row for each row in the fData table of cds_subset, with matching IDs.
#' @param add_annotation_col Additional annotations to show for each column in the heatmap. Must be a dataframe with one row for each cell in the pData table of cds_subset, with matching IDs.
#' @param show_rownames Whether to show the names for each row in the table.
#' @param use_gene_short_name Whether to use the short names for each row. If FALSE, uses row IDs from the fData table.
#' @param scale_max The maximum value (in standard deviations) to show in the heatmap. Values larger than this are set to the max.
#' @param scale_min The minimum value (in standard deviations) to show in the heatmap. Values smaller than this are set to the min.
#' @param norm_method Determines how to transform expression values prior to rendering
#' @param trend_formula A formula string specifying the model used in fitting the spline curve for each gene/feature.
#' @param return_heatmap Whether to return the pheatmap object to the user.
#' @param cores Number of cores to use when smoothing the expression curves shown in the heatmap.
#' @param ... Additional arguments passed to buildBranchCellDataSet
#' @return A list of heatmap_matrix (expression matrix for the branch committment), ph (pheatmap heatmap object),
#' annotation_row (annotation data.frame for the row), annotation_col (annotation data.frame for the column).
#' @import pheatmap
#' @importFrom stats sd as.dist cor cutree
#' @export
#'
plot_genes_branched_heatmap <- function(cds_subset,
branch_point=1,
branch_states=NULL,
branch_labels = c("Cell fate 1", "Cell fate 2"),
cluster_rows = TRUE,
hclust_method = "ward.D2",
num_clusters = 6,
hmcols = NULL,
branch_colors = c('#979797', '#F05662', '#7990C8'),
add_annotation_row = NULL,
add_annotation_col = NULL,
show_rownames = FALSE,
use_gene_short_name = TRUE,
scale_max=3,
scale_min=-3,
norm_method = c("log", "vstExprs"),
trend_formula = '~sm.ns(Pseudotime, df=3) * Branch',
return_heatmap=FALSE,
cores = 1, ...) {
cds <- NA
new_cds <- buildBranchCellDataSet(cds_subset,
branch_states=branch_states,
branch_point=branch_point,
progenitor_method = 'duplicate',
...)
new_cds@dispFitInfo <- cds_subset@dispFitInfo
if(is.null(branch_states)) {
progenitor_state <- subset(pData(cds_subset), Pseudotime == 0)[, 'State']
branch_states <- setdiff(pData(cds_subset)$State, progenitor_state)
}
col_gap_ind <- 101
# newdataA <- data.frame(Pseudotime = seq(0, 100, length.out = 100))
# newdataB <- data.frame(Pseudotime = seq(0, 100, length.out = 100))
newdataA <- data.frame(Pseudotime = seq(0, 100,
length.out = 100), Branch = as.factor(unique(as.character(pData(new_cds)$Branch))[1]))
newdataB <- data.frame(Pseudotime = seq(0, 100,
length.out = 100), Branch = as.factor(unique(as.character(pData(new_cds)$Branch))[2]))
BranchAB_exprs <- genSmoothCurves(new_cds[, ], cores=cores, trend_formula = trend_formula,
relative_expr = T, new_data = rbind(newdataA, newdataB))
BranchA_exprs <- BranchAB_exprs[, 1:100]
BranchB_exprs <- BranchAB_exprs[, 101:200]
#common_ancestor_cells <- row.names(pData(new_cds)[duplicated(pData(new_cds)$original_cell_id),])
common_ancestor_cells <- row.names(pData(new_cds)[pData(new_cds)$State == setdiff(pData(new_cds)$State, branch_states),])
BranchP_num <- (100 - floor(max(pData(new_cds)[common_ancestor_cells, 'Pseudotime'])))
BranchA_num <- floor(max(pData(new_cds)[common_ancestor_cells, 'Pseudotime']))
BranchB_num <- BranchA_num
norm_method <- match.arg(norm_method)
# FIXME: this needs to check that vst values can even be computed. (They can only be if we're using NB as the expressionFamily)
if(norm_method == 'vstExprs') {
BranchA_exprs <- vstExprs(new_cds, expr_matrix=BranchA_exprs)
BranchB_exprs <- vstExprs(new_cds, expr_matrix=BranchB_exprs)
}
else if(norm_method == 'log') {
BranchA_exprs <- log10(BranchA_exprs + 1)
BranchB_exprs <- log10(BranchB_exprs + 1)
}
heatmap_matrix <- cbind(BranchA_exprs[, (col_gap_ind - 1):1], BranchB_exprs)
heatmap_matrix=heatmap_matrix[!apply(heatmap_matrix, 1, sd)==0,]
heatmap_matrix=Matrix::t(scale(Matrix::t(heatmap_matrix),center=TRUE))
heatmap_matrix=heatmap_matrix[is.na(row.names(heatmap_matrix)) == FALSE,]
heatmap_matrix[is.nan(heatmap_matrix)] = 0
heatmap_matrix[heatmap_matrix>scale_max] = scale_max
heatmap_matrix[heatmap_matrix<scale_min] = scale_min
heatmap_matrix_ori <- heatmap_matrix
heatmap_matrix <- heatmap_matrix[is.finite(heatmap_matrix[, 1]) & is.finite(heatmap_matrix[, col_gap_ind]), ] #remove the NA fitting failure genes for each branch
row_dist <- as.dist((1 - cor(Matrix::t(heatmap_matrix)))/2)
row_dist[is.na(row_dist)] <- 1
exp_rng <- range(heatmap_matrix) #bks is based on the expression range
bks <- seq(exp_rng[1] - 0.1, exp_rng[2] + 0.1, by=0.1)
if(is.null(hmcols)) {
hmcols <- blue2green2red(length(bks) - 1)
}
# prin t(hmcols)
ph <- pheatmap(heatmap_matrix,
useRaster = T,
cluster_cols=FALSE,
cluster_rows=TRUE,
show_rownames=F,
show_colnames=F,
#scale="row",
clustering_distance_rows=row_dist,
clustering_method = hclust_method,
cutree_rows=num_clusters,
silent=TRUE,
filename=NA,
breaks=bks,
color=hmcols
#color=hmcols#,
# filename="expression_pseudotime_pheatmap.pdf",
)
#save(heatmap_matrix, row_dist, num_clusters, hmcols, ph, branchTest_df, qval_lowest_thrsd, branch_labels, BranchA_num, BranchP_num, BranchB_num, file = 'heatmap_matrix')
annotation_row <- data.frame(Cluster=factor(cutree(ph$tree_row, num_clusters)))
if(!is.null(add_annotation_row)) {
annotation_row <- cbind(annotation_row, add_annotation_row[row.names(annotation_row), ])
# annotation_row$bif_time <- add_annotation_row[as.character(fData(absolute_cds[row.names(annotation_row), ])$gene_short_name), 1]
}
colnames(heatmap_matrix) <- c(1:ncol(heatmap_matrix))
annotation_col <- data.frame(row.names = c(1:ncol(heatmap_matrix)), "Cell Type" = c(rep(branch_labels[1], BranchA_num),
rep("Pre-branch", 2 * BranchP_num),
rep(branch_labels[2], BranchB_num)))
colnames(annotation_col) <- "Cell Type"
if(!is.null(add_annotation_col)) {
annotation_col <- cbind(annotation_col, add_annotation_col[fData(cds[row.names(annotation_col), ])$gene_short_name, 1])
}
names(branch_colors) <- c("Pre-branch", branch_labels[1], branch_labels[2])
annotation_colors=list("Cell Type"=branch_colors)
names(annotation_colors$`Cell Type`) = c('Pre-branch', branch_labels)
if (use_gene_short_name == TRUE) {
if (is.null(fData(cds_subset)$gene_short_name) == FALSE) {
feature_label <- as.character(fData(cds_subset)[row.names(heatmap_matrix), 'gene_short_name'])
feature_label[is.na(feature_label)] <- row.names(heatmap_matrix)
row_ann_labels <- as.character(fData(cds_subset)[row.names(annotation_row), 'gene_short_name'])
row_ann_labels[is.na(row_ann_labels)] <- row.names(annotation_row)
}
else {
feature_label <- row.names(heatmap_matrix)
row_ann_labels <- row.names(annotation_row)
}
}
else {
feature_label <- row.names(heatmap_matrix)
row_ann_labels <- row.names(annotation_row)
}
row.names(heatmap_matrix) <- feature_label
row.names(annotation_row) <- row_ann_labels
ph_res <- pheatmap(heatmap_matrix[, ], #ph$tree_row$order
useRaster = T,
cluster_cols=FALSE,
cluster_rows=TRUE,
show_rownames=show_rownames,
show_colnames=F,
#scale="row",
clustering_distance_rows=row_dist, #row_dist
clustering_method = hclust_method, #ward.D2
cutree_rows=num_clusters,
# cutree_cols = 2,
annotation_row=annotation_row,
annotation_col=annotation_col,
annotation_colors=annotation_colors,
gaps_col = col_gap_ind,
treeheight_row = 20,
breaks=bks,
fontsize = 6,
color=hmcols,
border_color = NA,
silent=TRUE)
grid::grid.rect(gp=grid::gpar("fill", col=NA))
grid::grid.draw(ph_res$gtable)
if (return_heatmap){
return(list(BranchA_exprs = BranchA_exprs, BranchB_exprs = BranchB_exprs, heatmap_matrix = heatmap_matrix,
heatmap_matrix_ori = heatmap_matrix_ori, ph = ph, col_gap_ind = col_gap_ind, row_dist = row_dist, hmcols = hmcols,
annotation_colors = annotation_colors, annotation_row = annotation_row, annotation_col = annotation_col,
ph_res = ph_res))
}
}
#' Plots genes by mean vs. dispersion, highlighting those selected for ordering
#'
#' Each gray point in the plot is a gene. The black dots are those that were included
#' in the last call to setOrderingFilter. The red curve shows the mean-variance
#' model learning by estimateDispersions().
#'
#' @param cds The CellDataSet to be used for the plot.
#' @export
plot_ordering_genes <- function(cds){
if(class(cds)[1] != "CellDataSet") {
stop("Error input object is not of type 'CellDataSet'")
}
disp_table <- dispersionTable(cds)
use_for_ordering <- NA
mean_expression <- NA
dispersion_empirical <- NA
dispersion_fit <- NA
gene_id <- NA
ordering_genes <- row.names(subset(fData(cds), use_for_ordering == TRUE))
g <- qplot(mean_expression, dispersion_empirical, data=disp_table, log="xy", color=I("darkgrey")) +
geom_line(aes(y=dispersion_fit), color="red")
if (length(ordering_genes) > 0){
g <- g + geom_point(aes(mean_expression, dispersion_empirical),
data=subset(disp_table, gene_id %in% ordering_genes), color="black")
}
g <- g + monocle_theme_opts()
g
}
#' Plots clusters of cells .
#'
#' @param cds CellDataSet for the experiment
#' @param x the column of reducedDimS(cds) to plot on the horizontal axis
#' @param y the column of reducedDimS(cds) to plot on the vertical axis
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to map to each cell's color
#' @param markers a gene name or gene id to use for setting the size of each cell in the plot
#' @param show_cell_names draw the name of each cell in the plot
#' @param cell_size The size of the point for each cell
#' @param cell_name_size the size of cell name labels
#' @param ... additional arguments passed into the scale_color_viridis function
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
#' @importFrom viridis scale_color_viridis
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' HSMM <- reduceD
#' plot_cell_clusters(HSMM)
#' plot_cell_clusters(HSMM, color_by="Pseudotime")
#' plot_cell_clusters(HSMM, markers="MYH3")
#' }
plot_cell_clusters <- function(cds,
x=1,
y=2,
color_by="Cluster",
markers=NULL,
show_cell_names=FALSE,
cell_size=1.5,
cell_name_size=2,
...){
if (is.null(cds@reducedDimA) | length(pData(cds)$Cluster) == 0){
stop("Error: Clustering is not performed yet. Please call clusterCells() before calling this function.")
}
gene_short_name <- NULL
sample_name <- NULL
data_dim_1 <- NULL
data_dim_2 <- NULL
#TODO: need to validate cds as ready for this plot (need mst, pseudotime, etc)
lib_info <- pData(cds)
tSNE_dim_coords <- reducedDimA(cds)
data_df <- data.frame(t(tSNE_dim_coords[c(x,y),]))
colnames(data_df) <- c("data_dim_1", "data_dim_2")
data_df$sample_name <- colnames(cds)
data_df <- merge(data_df, lib_info, by.x="sample_name", by.y="row.names")
markers_exprs <- NULL
if (is.null(markers) == FALSE){
markers_fData <- subset(fData(cds), gene_short_name %in% markers)
if (nrow(markers_fData) >= 1){
cds_subset <- cds[row.names(markers_fData),]
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")) {
integer_expression <- TRUE
}
else {
integer_expression <- FALSE
}
if (integer_expression) {
cds_exprs <- exprs(cds_subset)
if (is.null(sizeFactors(cds_subset))) {
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
cds_exprs <- Matrix::t(Matrix::t(cds_exprs)/sizeFactors(cds_subset))
cds_exprs <- reshape2::melt(round(as.matrix(cds_exprs)))
}
else {
cds_exprs <- reshape2::melt(as.matrix(exprs(cds_subset)))
}
markers_exprs <- cds_exprs
#markers_exprs <- reshape2::melt(as.matrix(cds_exprs))
colnames(markers_exprs)[1:2] <- c('feature_id','cell_id')
markers_exprs <- merge(markers_exprs, markers_fData, by.x = "feature_id", by.y="row.names")
#print (head( markers_exprs[is.na(markers_exprs$gene_short_name) == FALSE,]))
markers_exprs$feature_label <- as.character(markers_exprs$gene_short_name)
markers_exprs$feature_label[is.na(markers_exprs$feature_label)] <- markers_exprs$Var1
}
}
if (is.null(markers_exprs) == FALSE && nrow(markers_exprs) > 0){
data_df <- merge(data_df, markers_exprs, by.x="sample_name", by.y="cell_id")
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2)) + facet_wrap(~feature_label)
}else{
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2))
}
# FIXME: setting size here overrides the marker expression funtionality.
# Don't do it!
if (is.null(markers_exprs) == FALSE && nrow(markers_exprs) > 0){
g <- g + geom_point(aes(color=log10(value + 0.1)), size=I(cell_size), na.rm = TRUE) +
scale_color_viridis(name = paste0("log10(value + 0.1)"), ...)
}else {
g <- g + geom_point(aes_string(color = color_by), size=I(cell_size), na.rm = TRUE)
}
g <- g +
#scale_color_brewer(palette="Set1") +
monocle_theme_opts() +
xlab(paste("Component", x)) +
ylab(paste("Component", y)) +
theme(legend.position="top", legend.key.height=grid::unit(0.35, "in")) +
#guides(color = guide_legend(label.position = "top")) +
theme(legend.key = element_blank()) +
theme(panel.background = element_rect(fill='white')) +
theme(text = element_text(size = 15))
g
}
#' Plots the decision map of density clusters .
#'
#' @param cds CellDataSet for the experiment after running clusterCells_Density_Peak
#' @param rho_threshold The threshold of local density (rho) used to select the density peaks for plotting
#' @param delta_threshold The threshold of local distance (delta) used to select the density peaks for plotting
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' plot_rho_delta(HSMM)
#' }
plot_rho_delta <- function(cds, rho_threshold = NULL, delta_threshold = NULL){
if(!is.null(cds@auxClusteringData[["tSNE"]]$densityPeak)
& !is.null(pData(cds)$Cluster)
& !is.null(pData(cds)$peaks)
& !is.null(pData(cds)$halo)
& !is.null(pData(cds)$delta)
& !is.null(pData(cds)$rho)) {
rho <- NULL
delta <- NULL
# df <- data.frame(rho = as.numeric(levels(pData(cds)$rho))[pData(cds)$rho],
# delta = as.numeric(levels(pData(cds)$delta))[pData(cds)$delta])
if(!is.null(rho_threshold) & !is.null(delta_threshold)){
peaks <- pData(cds)$rho >= rho_threshold & pData(cds)$delta >= delta_threshold
}
else
peaks <- pData(cds)$peaks
df <- data.frame(rho = pData(cds)$rho, delta = pData(cds)$delta, peaks = peaks)
g <- qplot(rho, delta, data = df, alpha = I(0.5), color = peaks) + monocle_theme_opts() +
theme(legend.position="top", legend.key.height=grid::unit(0.35, "in")) +
scale_color_manual(values=c("grey","black")) +
theme(legend.key = element_blank()) +
theme(panel.background = element_rect(fill='white'))
}
else {
stop('Please run clusterCells_Density_Peak before using this plotting function')
}
g
}
#' Plots the percentage of variance explained by the each component based on PCA from the normalized expression
#' data using the same procedure used in reduceDimension function.
#'
#' @param cds CellDataSet for the experiment after running reduceDimension with reduction_method as tSNE
#' @param max_components Maximum number of components shown in the scree plot (variance explained by each component)
#' @param norm_method Determines how to transform expression values prior to reducing dimensionality
#' @param residualModelFormulaStr A model formula specifying the effects to subtract from the data before clustering.
#' @param pseudo_expr amount to increase expression values before dimensionality reduction
#' @param return_all A logical argument to determine whether or not the variance of each component is returned
#' @param use_existing_pc_variance Whether to plot existing results for variance explained by each PC
#' @param verbose Whether to emit verbose output during dimensionality reduction
#' @param ... additional arguments to pass to the dimensionality reduction function
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' plot_pc_variance_explained(HSMM)
#' }
plot_pc_variance_explained <- function(cds,
max_components=100,
# reduction_method=c("DDRTree", "ICA", 'tSNE'),
norm_method = c("log", "vstExprs", "none"),
residualModelFormulaStr=NULL,
pseudo_expr=NULL,
return_all = F,
use_existing_pc_variance=FALSE,
verbose=FALSE,
...){
set.seed(2016)
if(!is.null(cds@auxClusteringData[["tSNE"]]$variance_explained) & use_existing_pc_variance == T){
prop_varex <- cds@auxClusteringData[["tSNE"]]$variance_explained
}
else{
FM <- normalize_expr_data(cds, norm_method, pseudo_expr)
#FM <- FM[unlist(sparseApply(FM, 1, sd, convert_to_dense=TRUE)) > 0, ]
xm <- Matrix::rowMeans(FM)
xsd <- sqrt(Matrix::rowMeans((FM - xm)^2))
FM <- FM[xsd > 0,]
if (is.null(residualModelFormulaStr) == FALSE) {
if (verbose)
message("Removing batch effects")
X.model_mat <- sparse.model.matrix(as.formula(residualModelFormulaStr),
data = pData(cds), drop.unused.levels = TRUE)
fit <- limma::lmFit(FM, X.model_mat, ...)
beta <- fit$coefficients[, -1, drop = FALSE]
beta[is.na(beta)] <- 0
FM <- as.matrix(FM) - beta %*% t(X.model_mat[, -1])
}else{
X.model_mat <- NULL
}
if (nrow(FM) == 0) {
stop("Error: all rows have standard deviation zero")
}
# FM <- convert2DRData(cds, norm_method = 'log')
# FM <- FM[rowSums(is.na(FM)) == 0, ]
irlba_res <- prcomp_irlba(t(FM), n = min(max_components, min(dim(FM)) - 1),
center = TRUE, scale. = TRUE)
prop_varex <- irlba_res$sdev^2 / sum(irlba_res$sdev^2)
#
# cell_means <- Matrix::rowMeans(FM_t)
# cell_vars <- Matrix::rowMeans((FM_t - cell_means)^2)
#
# irlba_res <- irlba(FM,
# nv= min(max_components, min(dim(FM)) - 1), #avoid calculating components in the tail
# nu=0,
# center=cell_means,
# scale=sqrt(cell_vars),
# right_only=TRUE)
# prop_varex <- irlba_res$d / sum(irlba_res$d)
#
# # pca_res <- prcomp(t(FM), center = T, scale = T)
# # std_dev <- pca_res$sdev
# # pr_var <- std_dev^2
# prop_varex <- pr_var/sum(pr_var)
}
p <- qplot(1:length(prop_varex), prop_varex, alpha = I(0.5)) + monocle_theme_opts() +
theme(legend.position="top", legend.key.height=grid::unit(0.35, "in")) +
theme(panel.background = element_rect(fill='white')) + xlab('components') +
ylab('Variance explained \n by each component')
cds@auxClusteringData[["tSNE"]]$variance_explained <- prop_varex # update CDS slot for variance_explained
if(return_all) {
return(list(variance_explained = prop_varex, p = p))
}
else
return(p)
}
#' @importFrom igraph shortest_paths degree shortest.paths
traverseTree <- function(g, starting_cell, end_cells){
distance <- shortest.paths(g, v=starting_cell, to=end_cells)
branchPoints <- which(degree(g) == 3)
path <- shortest_paths(g, from = starting_cell, end_cells)
return(list(shortest_path = path$vpath, distance = distance, branch_points = intersect(branchPoints, unlist(path$vpath))))
}
#' Plots the minimum spanning tree on cells.
#' @description Plots the minimum spanning tree on cells.
#' @param cds CellDataSet for the experiment
#' @param x the column of reducedDimS(cds) to plot on the horizontal axis
#' @param y the column of reducedDimS(cds) to plot on the vertical axis
#' @param root_states the state used to set as the root of the graph
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to map to each cell's color
#' @param show_tree whether to show the links between cells connected in the minimum spanning tree
#' @param show_backbone whether to show the diameter path of the MST used to order the cells
#' @param backbone_color the color used to render the backbone.
#' @param markers a gene name or gene id to use for setting the size of each cell in the plot
#' @param show_cell_names draw the name of each cell in the plot
#' @param cell_size The size of the point for each cell
#' @param cell_link_size The size of the line segments connecting cells (when used with ICA) or the principal graph (when used with DDRTree)
#' @param cell_name_size the size of cell name labels
#' @param show_branch_points Whether to show icons for each branch point (only available when reduceDimension was called with DDRTree)
#' @param ... Additional arguments passed to the scale_color_viridis function
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom igraph V get.edgelist layout_as_tree
#' @importFrom reshape2 melt
#' @importFrom viridis scale_color_viridis
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' plot_complex_cell_trajectory(HSMM)
#' plot_complex_cell_trajectory(HSMM, color_by="Pseudotime", show_backbone=FALSE)
#' plot_complex_cell_trajectory(HSMM, markers="MYH3")
#' }
plot_complex_cell_trajectory <- function(cds,
x=1,
y=2,
root_states = NULL,
color_by="State",
show_tree=TRUE,
show_backbone=TRUE,
backbone_color="black",
markers=NULL,
show_cell_names=FALSE,
cell_size=1.5,
cell_link_size=0.75,
cell_name_size=2,
show_branch_points=TRUE,
...){
gene_short_name <- NA
sample_name <- NA
data_dim_1 <- NA
data_dim_2 <- NA
#TODO: need to validate cds as ready for this plot (need mst, pseudotime, etc)
lib_info_with_pseudo <- pData(cds)
if (is.null(cds@dim_reduce_type)){
stop("Error: dimensionality not yet reduced. Please call reduceDimension() before calling this function.")
}
if (cds@dim_reduce_type == "ICA"){
reduced_dim_coords <- reducedDimS(cds)
}else if (cds@dim_reduce_type %in% c("SimplePPT", "DDRTree", "SGL-tree") ){
reduced_dim_coords <- reducedDimK(cds)
closest_vertex <- cds@auxOrderingData[["DDRTree"]]$pr_graph_cell_proj_closest_vertex
}else {
stop("Error: unrecognized dimensionality reduction method.")
}
if (is.null(reduced_dim_coords)){
stop("You must first call reduceDimension() before using this function")
}
dp_mst <- minSpanningTree(cds)
if(is.null(root_states)) {
if(is.null(lib_info_with_pseudo$Pseudotime)){
root_cell <- row.names(lib_info_with_pseudo)[degree(dp_mst) == 1][1]
}
else
root_cell <- row.names(subset(lib_info_with_pseudo, Pseudotime == 0))
if(cds@dim_reduce_type != "ICA")
root_cell <- V(dp_mst)$name[cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex[root_cell, ]]
}
else {
candidate_root_cells <- row.names(subset(pData(cds), State %in% root_states))
if(cds@dim_reduce_type == "ICA") {
root_cell <- candidate_root_cells[which(degree(dp_mst, candidate_root_cells) == 1)]
} else {
Y_candidate_root_cells <- V(dp_mst)$name[cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex[candidate_root_cells, ]]
root_cell <- Y_candidate_root_cells[which(degree(dp_mst, Y_candidate_root_cells) == 1)]
}
}
# #root_cell <- cds@auxOrderingData[[cds@dim_reduce_type]]$root_cell
# root_state <- pData(cds)[root_cell,]$State
# #root_state <- V(pr_graph_cell_proj_mst)[root_cell,]$State
# pr_graph_root <- subset(pData(cds), State == root_state)
# closest_vertex <- cds@auxOrderingData[["DDRTree"]]$pr_graph_cell_proj_closest_vertex
# root_cell_point_in_Y <- closest_vertex[row.names(pr_graph_root),]
tree_coords <- layout_as_tree(dp_mst, root=root_cell)
#ica_space_df <- data.frame(Matrix::t(reduced_dim_coords[c(x,y),]))
ica_space_df <- data.frame(tree_coords)
row.names(ica_space_df) <- colnames(reduced_dim_coords)
colnames(ica_space_df) <- c("prin_graph_dim_1", "prin_graph_dim_2")
ica_space_df$sample_name <- row.names(ica_space_df)
#ica_space_with_state_df <- merge(ica_space_df, lib_info_with_pseudo, by.x="sample_name", by.y="row.names")
#print(ica_space_with_state_df)
if (is.null(dp_mst)){
stop("You must first call orderCells() before using this function")
}
edge_list <- as.data.frame(get.edgelist(dp_mst))
colnames(edge_list) <- c("source", "target")
edge_df <- merge(ica_space_df, edge_list, by.x="sample_name", by.y="source", all=TRUE)
#edge_df <- ica_space_df
edge_df <- plyr::rename(edge_df, c("prin_graph_dim_1"="source_prin_graph_dim_1", "prin_graph_dim_2"="source_prin_graph_dim_2"))
edge_df <- merge(edge_df, ica_space_df[,c("sample_name", "prin_graph_dim_1", "prin_graph_dim_2")], by.x="target", by.y="sample_name", all=TRUE)
edge_df <- plyr::rename(edge_df, c("prin_graph_dim_1"="target_prin_graph_dim_1", "prin_graph_dim_2"="target_prin_graph_dim_2"))
#S_matrix <- reducedDimS(cds)
#data_df <- data.frame(t(S_matrix[c(x,y),]))
if(cds@dim_reduce_type == "ICA"){
S_matrix <- tree_coords[,] #colnames(cds)
} else if(cds@dim_reduce_type %in% c("DDRTree", "SimplePPT", "SGL-tree")){
S_matrix <- tree_coords[closest_vertex,]
closest_vertex <- cds@auxOrderingData[["DDRTree"]]$pr_graph_cell_proj_closest_vertex
}
data_df <- data.frame(S_matrix)
row.names(data_df) <- colnames(reducedDimS(cds))
colnames(data_df) <- c("data_dim_1", "data_dim_2")
data_df$sample_name <- row.names(data_df)
data_df <- merge(data_df, lib_info_with_pseudo, by.x="sample_name", by.y="row.names")
markers_exprs <- NULL
if (is.null(markers) == FALSE){
markers_fData <- subset(fData(cds), gene_short_name %in% markers)
if (nrow(markers_fData) >= 1){
markers_exprs <- reshape2::melt(as.matrix(exprs(cds[row.names(markers_fData),])))
colnames(markers_exprs)[1:2] <- c('feature_id','cell_id')
markers_exprs <- merge(markers_exprs, markers_fData, by.x = "feature_id", by.y="row.names")
#print (head( markers_exprs[is.na(markers_exprs$gene_short_name) == FALSE,]))
markers_exprs$feature_label <- as.character(markers_exprs$gene_short_name)
markers_exprs$feature_label[is.na(markers_exprs$feature_label)] <- markers_exprs$Var1
}
}
if (is.null(markers_exprs) == FALSE && nrow(markers_exprs) > 0){
data_df <- merge(data_df, markers_exprs, by.x="sample_name", by.y="cell_id")
#print (head(edge_df))
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2, I(cell_size))) + facet_wrap(~feature_label)
}else{
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2))
}
if (show_tree){
g <- g + geom_segment(aes_string(x="source_prin_graph_dim_1", y="source_prin_graph_dim_2", xend="target_prin_graph_dim_1", yend="target_prin_graph_dim_2"), size=cell_link_size, linetype="solid", na.rm=TRUE, data=edge_df)
}
# FIXME: setting size here overrides the marker expression funtionality.
# Don't do it!
if (is.null(markers_exprs) == FALSE && nrow(markers_exprs) > 0){
if(class(data_df[, color_by]) == 'numeric') {
g <- g + geom_jitter(aes_string(color = paste0("log10(", color_by, " + 0.1)")), size=I(cell_size), na.rm = TRUE, height=5) +
scale_color_viridis(name = paste0("log10(", color_by, ")"), ...)
} else {
g <- g + geom_jitter(aes_string(color = color_by), size=I(cell_size), na.rm = TRUE, height=5)
}
}else {
if(class(data_df[, color_by]) == 'numeric') {
g <- g + geom_jitter(aes_string(color = paste0("log10(", color_by, " + 0.1)")), size=I(cell_size), na.rm = TRUE, height=5) +
scale_color_viridis(name = paste0("log10(", color_by, " + 0.1)"), ...)
} else {
g <- g + geom_jitter(aes_string(color = color_by), size=I(cell_size), na.rm = TRUE, height=5)
}
}
if (show_branch_points && cds@dim_reduce_type == 'DDRTree'){
mst_branch_nodes <- cds@auxOrderingData[[cds@dim_reduce_type]]$branch_points
branch_point_df <- subset(edge_df, sample_name %in% mst_branch_nodes)[,c("sample_name", "source_prin_graph_dim_1", "source_prin_graph_dim_2")]
branch_point_df$branch_point_idx <- match(branch_point_df$sample_name, mst_branch_nodes)
branch_point_df <- branch_point_df[!duplicated(branch_point_df$branch_point_idx), ]
g <- g + geom_point(aes_string(x="source_prin_graph_dim_1", y="source_prin_graph_dim_2"),
size=2 * cell_size, na.rm=TRUE, data=branch_point_df) +
geom_text(aes_string(x="source_prin_graph_dim_1", y="source_prin_graph_dim_2", label="branch_point_idx"),
size=1.5 * cell_size, color="white", na.rm=TRUE, data=branch_point_df)
}
if (show_cell_names){
g <- g +geom_text(aes(label=sample_name), size=cell_name_size)
}
g <- g +
#scale_color_brewer(palette="Set1") +
theme(strip.background = element_rect(colour = 'white', fill = 'white')) +
theme(panel.border = element_blank()) +
# theme(axis.line.x = element_line(size=0.25, color="black")) +
# theme(axis.line.y = element_line(size=0.25, color="black")) +
theme(panel.grid.minor.x = element_blank(), panel.grid.minor.y = element_blank()) +
theme(panel.grid.major.x = element_blank(), panel.grid.major.y = element_blank()) +
theme(panel.background = element_rect(fill='white')) +
theme(legend.key=element_blank()) +
xlab('') +
ylab('') +
theme(legend.position="top", legend.key.height=grid::unit(0.35, "in")) +
#guides(color = guide_legend(label.position = "top")) +
theme(legend.key = element_blank()) +
theme(panel.background = element_rect(fill='white')) +
theme(line = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank())
g
}
# Modified function: Plot heatmap of 3 branches with the same coloring. Each CDS subset has to have the same set of genes.
#' Create a heatmap to demonstrate the bifurcation of gene expression along multiple branches
#'
#' @param cds CellDataSet for the experiment (normally only the branching genes detected with BEAM)
#' @param branches The terminal branches (states) on the developmental tree you want to investigate.
#' @param branches_name Name (for example, cell type) of branches you believe the cells on the branches are associated with.
#' @param cluster_rows Whether to cluster the rows of the heatmap.
#' @param hclust_method The method used by pheatmap to perform hirearchical clustering of the rows.
#' @param num_clusters Number of clusters for the heatmap of branch genes
#' @param hmcols The color scheme for drawing the heatmap.
#' @param add_annotation_row Additional annotations to show for each row in the heatmap. Must be a dataframe with one row for each row in the fData table of cds_subset, with matching IDs.
#' @param add_annotation_col Additional annotations to show for each column in the heatmap. Must be a dataframe with one row for each cell in the pData table of cds_subset, with matching IDs.
#' @param show_rownames Whether to show the names for each row in the table.
#' @param use_gene_short_name Whether to use the short names for each row. If FALSE, uses row IDs from the fData table.
#' @param norm_method Determines how to transform expression values prior to rendering
#' @param scale_max The maximum value (in standard deviations) to show in the heatmap. Values larger than this are set to the max.
#' @param scale_min The minimum value (in standard deviations) to show in the heatmap. Values smaller than this are set to the min.
#' @param trend_formula A formula string specifying the model used in fitting the spline curve for each gene/feature.
#' @param return_heatmap Whether to return the pheatmap object to the user.
#' @param cores Number of cores to use when smoothing the expression curves shown in the heatmap.
#' @return A list of heatmap_matrix (expression matrix for the branch committment), ph (pheatmap heatmap object),
#' annotation_row (annotation data.frame for the row), annotation_col (annotation data.frame for the column).
#' @import pheatmap
#' @export
#'
plot_multiple_branches_heatmap <- function(cds,
branches,
branches_name = NULL,
cluster_rows = TRUE,
hclust_method = "ward.D2",
num_clusters = 6,
hmcols = NULL,
add_annotation_row = NULL,
add_annotation_col = NULL,
show_rownames = FALSE,
use_gene_short_name = TRUE,
norm_method = c("vstExprs", "log"),
scale_max=3,
scale_min=-3,
trend_formula = '~sm.ns(Pseudotime, df=3)',
return_heatmap=FALSE,
cores=1){
pseudocount <- 1
if(!(all(branches %in% pData(cds)$State)) & length(branches) == 1){
stop('This function only allows to make multiple branch plots where branches is included in the pData')
}
branch_label <- branches
if(!is.null(branches_name)){
if(length(branches) != length(branches_name)){
stop('branches_name should have the same length as branches')
}
branch_label <- branches_name
}
#test whether or not the states passed to branches are true branches (not truncks) or there are terminal cells
g <- cds@minSpanningTree
m <- NULL
# branche_cell_num <- c()
for(branch_in in branches) {
branches_cells <- row.names(subset(pData(cds), State == branch_in))
root_state <- subset(pData(cds), Pseudotime == 0)[, 'State']
root_state_cells <- row.names(subset(pData(cds), State == root_state))
if(cds@dim_reduce_type != 'ICA') {
root_state_cells <- unique(paste('Y_', cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex[root_state_cells, ], sep = ''))
branches_cells <- unique(paste('Y_', cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex[branches_cells, ], sep = ''))
}
root_cell <- root_state_cells[which(degree(g, v = root_state_cells) == 1)]
tip_cell <- branches_cells[which(degree(g, v = branches_cells) == 1)]
traverse_res <- traverseTree(g, root_cell, tip_cell)
path_cells <- names(traverse_res$shortest_path[[1]])
if(cds@dim_reduce_type != 'ICA') {
pc_ind <- cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex
path_cells <- row.names(pc_ind)[paste('Y_', pc_ind[, 1], sep = '') %in% path_cells]
}
cds_subset <- cds[, path_cells]
newdata <- data.frame(Pseudotime = seq(0, max(pData(cds_subset)$Pseudotime),length.out = 100))
tmp <- genSmoothCurves(cds_subset, cores=cores, trend_formula = trend_formula,
relative_expr = T, new_data = newdata)
if(is.null(m))
m <- tmp
else
m <- cbind(m, tmp)
}
#remove genes with no expression in any condition
m=m[!apply(m,1,sum)==0,]
norm_method <- match.arg(norm_method)
# FIXME: this needs to check that vst values can even be computed. (They can only be if we're using NB as the expressionFamily)
if(norm_method == 'vstExprs' && is.null(cds@dispFitInfo[["blind"]]$disp_func) == FALSE) {
m = vstExprs(cds, expr_matrix=m)
}
else if(norm_method == 'log') {
m = log10(m+pseudocount)
}
# Row-center the data.
m=m[!apply(m,1,sd)==0,]
m=Matrix::t(scale(Matrix::t(m),center=TRUE))
m=m[is.na(row.names(m)) == FALSE,]
m[is.nan(m)] = 0
m[m>scale_max] = scale_max
m[m<scale_min] = scale_min
heatmap_matrix <- m
row_dist <- as.dist((1 - cor(Matrix::t(heatmap_matrix)))/2)
row_dist[is.na(row_dist)] <- 1
if(is.null(hmcols)) {
bks <- seq(-3.1,3.1, by = 0.1)
hmcols <- blue2green2red(length(bks) - 1)
}
else {
bks <- seq(-3.1,3.1, length.out = length(hmcols))
}
ph <- pheatmap(heatmap_matrix,
useRaster = T,
cluster_cols=FALSE,
cluster_rows=T,
show_rownames=F,
show_colnames=F,
clustering_distance_rows=row_dist,
clustering_method = hclust_method,
cutree_rows=num_clusters,
silent=TRUE,
filename=NA,
breaks=bks,
color=hmcols)
annotation_col <- data.frame(Branch=factor(rep(rep(branch_label, each = 100))))
annotation_row <- data.frame(Cluster=factor(cutree(ph$tree_row, num_clusters)))
col_gaps_ind <- c(1:(length(branches) - 1)) * 100
if(!is.null(add_annotation_row)) {
old_colnames_length <- ncol(annotation_row)
annotation_row <- cbind(annotation_row, add_annotation_row[row.names(annotation_row), ])
colnames(annotation_row)[(old_colnames_length+1):ncol(annotation_row)] <- colnames(add_annotation_row)
# annotation_row$bif_time <- add_annotation_row[as.character(fData(absolute_cds[row.names(annotation_row), ])$gene_short_name), 1]
}
if (use_gene_short_name == TRUE) {
if (is.null(fData(cds)$gene_short_name) == FALSE) {
feature_label <- as.character(fData(cds)[row.names(heatmap_matrix), 'gene_short_name'])
feature_label[is.na(feature_label)] <- row.names(heatmap_matrix)
row_ann_labels <- as.character(fData(cds)[row.names(annotation_row), 'gene_short_name'])
row_ann_labels[is.na(row_ann_labels)] <- row.names(annotation_row)
}
else {
feature_label <- row.names(heatmap_matrix)
row_ann_labels <- row.names(annotation_row)
}
}
else {
feature_label <- row.names(heatmap_matrix)
row_ann_labels <- row.names(annotation_row)
}
row.names(heatmap_matrix) <- feature_label
row.names(annotation_row) <- row_ann_labels
colnames(heatmap_matrix) <- c(1:ncol(heatmap_matrix))
if(!(cluster_rows)) {
annotation_row <- NA
}
ph_res <- pheatmap(heatmap_matrix[, ], #ph$tree_row$order
useRaster = T,
cluster_cols = FALSE,
cluster_rows = cluster_rows,
show_rownames=show_rownames,
show_colnames=F,
#scale="row",
clustering_distance_rows=row_dist, #row_dist
clustering_method = hclust_method, #ward.D2
cutree_rows=num_clusters,
# cutree_cols = 2,
annotation_row=annotation_row,
annotation_col=annotation_col,
gaps_col = col_gaps_ind,
treeheight_row = 20,
breaks=bks,
fontsize = 12,
color=hmcols,
silent=TRUE,
border_color = NA,
filename=NA
)
grid::grid.rect(gp=grid::gpar("fill", col=NA))
grid::grid.draw(ph_res$gtable)
if (return_heatmap){
return(ph_res)
}
}
#' Create a kinetic curves to demonstrate the bifurcation of gene expression along multiple branches
#'
#' @param cds CellDataSet for the experiment (normally only the branching genes detected with BEAM)
#' @param branches The terminal branches (states) on the developmental tree you want to investigate.
#' @param branches_name Name (for example, cell type) of branches you believe the cells on the branches are associated with.
#' @param min_expr The minimum level of expression to show in the plot
#' @param cell_size A number how large the cells should be in the plot
#' @param norm_method Determines how to transform expression values prior to rendering
#' @param nrow the number of rows used when laying out the panels for each gene's expression
#' @param ncol the number of columns used when laying out the panels for each gene's expression
#' @param panel_order the order in which genes should be layed out (left-to-right, top-to-bottom)
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to be used to color each cell
#' @param trend_formula the model formula to be used for fitting the expression trend over pseudotime
#' @param label_by_short_name label figure panels by gene_short_name (TRUE) or feature id (FALSE)
#' @param TPM Whether to convert the expression value into TPM values.
#' @param cores Number of cores to use when smoothing the expression curves shown in the heatmap.
#' @return a ggplot2 plot object
#'
#' @importFrom Biobase esApply exprs<-
#' @importFrom stats lowess
#'
#' @export
#'
plot_multiple_branches_pseudotime <- function(cds,
branches,
branches_name = NULL,
min_expr = NULL,
cell_size = 0.75,
norm_method = c("vstExprs", "log"),
nrow = NULL,
ncol = 1,
panel_order = NULL,
color_by = "Branch",
trend_formula = '~sm.ns(Pseudotime, df=3)',
label_by_short_name = TRUE,
TPM = FALSE,
cores=1){
if(TPM) {
exprs(cds) <- esApply(cds, 2, function(x) x / sum(x) * 1e6)
}
if(!(all(branches %in% pData(cds)$State)) & length(branches) == 1){
stop('This function only allows to make multiple branch plots where branches is included in the pData')
}
branch_label <- branches
if(!is.null(branches_name)){
if(length(branches) != length(branches_name)){
stop('branches_name should have the same length as branches')
}
branch_label <- branches_name
}
#test whether or not the states passed to branches are true branches (not truncks) or there are terminal cells
g <- cds@minSpanningTree
m <- NULL
cds_exprs <- NULL
# branche_cell_num <- c()
for(branch_in in branches) {
branches_cells <- row.names(subset(pData(cds), State == branch_in))
root_state <- subset(pData(cds), Pseudotime == 0)[, 'State']
root_state_cells <- row.names(subset(pData(cds), State == root_state))
if(cds@dim_reduce_type != 'ICA') {
root_state_cells <- unique(paste('Y_', cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex[root_state_cells, ], sep = ''))
branches_cells <- unique(paste('Y_', cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex[branches_cells, ], sep = ''))
}
root_cell <- root_state_cells[which(degree(g, v = root_state_cells) == 1)]
tip_cell <- branches_cells[which(degree(g, v = branches_cells) == 1)]
traverse_res <- traverseTree(g, root_cell, tip_cell)
path_cells <- names(traverse_res$shortest_path[[1]])
if(cds@dim_reduce_type != 'ICA') {
pc_ind <- cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex
path_cells <- row.names(pc_ind)[paste('Y_', pc_ind[, 1], sep = '') %in% path_cells]
}
#if(is.null(pData(cds)$no_expression)) {
cds_subset <- cds[, path_cells]
# } else {
# cds_subset <- cds[, path_cells %in% colnames(cds)[!pData(cds)$no_expression]]
# }
newdata <- data.frame(Pseudotime = pData(cds_subset)$Pseudotime, row.names = colnames(cds_subset))
tmp <- t(esApply(cds_subset, 1, function(x) lowess(x[order(pData(cds_subset)$Pseudotime)])$y))
# tmp <- t(esApply(cds_subset, 1, function(x) {
# x <- x[order(pData(cds_subset)$Pseudotime)]
# c(smooth::sma(x, order = 100, h = 1, silent="all")$fitted)}) #, x[length(x)]
# )
colnames(tmp) <- colnames(cds_subset)[order(pData(cds_subset)$Pseudotime)]
# tmp <- genSmoothCurves(cds_subset, cores=cores, trend_formula = trend_formula,
# relative_expr = T, new_data = newdata)
cds_exprs_tmp <- reshape2::melt(log2(tmp + 1))
cds_exprs_tmp <- reshape2::melt(tmp)
colnames(cds_exprs_tmp) <- c("f_id", "Cell", "expression")
cds_exprs_tmp$Branch <- branch_label[which(branches == branch_in)]
if(is.null(cds_exprs))
cds_exprs <- cds_exprs_tmp
else
cds_exprs <- rbind(cds_exprs, cds_exprs_tmp)
if(is.null(m))
m <- tmp
else
m <- cbind(m, tmp)
}
#remove genes with no expression in any condition
m=m[!apply(m,1,sum)==0,]
norm_method <- match.arg(norm_method)
# FIXME: this needs to check that vst values can even be computed. (They can only be if we're using NB as the expressionFamily)
if(norm_method == 'vstExprs' && is.null(cds@dispFitInfo[["blind"]]$disp_func) == FALSE) {
m = vstExprs(cds, expr_matrix=m)
}
else if(norm_method == 'log') {
m = log10(m+pseudocount)
}
if (is.null(min_expr)) {
min_expr <- cds@lowerDetectionLimit
}
cds_pData <- pData(cds)
cds_fData <- fData(cds)
cds_exprs <- merge(cds_exprs, cds_fData, by.x = "f_id", by.y = "row.names")
cds_exprs <- merge(cds_exprs, cds_pData, by.x = "Cell", by.y = "row.names")
cds_exprs <- plyr::ddply(cds_exprs, .(Branch), mutate, Pseudotime = (Pseudotime - min(Pseudotime)) * 100 / (max(Pseudotime) - min(Pseudotime)) )
# if (integer_expression) {
# cds_exprs$adjusted_expression <- round(cds_exprs$expression)
# }
# else {
# cds_exprs$adjusted_expression <- log10(cds_exprs$expression)
# }
if (label_by_short_name == TRUE) {
if (is.null(cds_exprs$gene_short_name) == FALSE) {
cds_exprs$feature_label <- as.character(cds_exprs$gene_short_name)
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
cds_exprs$feature_label <- as.factor(cds_exprs$feature_label)
# trend_formula <- paste("adjusted_expression", trend_formula,
# sep = "")
cds_exprs$Branch <- as.factor(cds_exprs$Branch)
# new_data <- data.frame(Pseudotime = pData(cds_subset)$Pseudotime, Branch = pData(cds_subset)$Branch)
# full_model_expectation <- genSmoothCurves(cds_subset, cores=1, trend_formula = trend_formula,
# relative_expr = T, new_data = new_data)
# colnames(full_model_expectation) <- colnames(cds_subset)
# cds_exprs$full_model_expectation <- apply(cds_exprs,1, function(x) full_model_expectation[x[2], x[1]])
# if(!is.null(reducedModelFormulaStr)){
# reduced_model_expectation <- genSmoothCurves(cds_subset, cores=1, trend_formula = reducedModelFormulaStr,
# relative_expr = T, new_data = new_data)
# colnames(reduced_model_expectation) <- colnames(cds_subset)
# cds_exprs$reduced_model_expectation <- apply(cds_exprs,1, function(x) reduced_model_expectation[x[2], x[1]])
# }
# # FIXME: If you want to show the bifurcation time for each gene, this function
# # should just compute it. Passing it in as a dataframe is just too complicated
# # and will be hard on the user.
# # if(!is.null(bifurcation_time)){
# # cds_exprs$bifurcation_time <- bifurcation_time[as.vector(cds_exprs$gene_short_name)]
# # }
# if (method == "loess")
# cds_exprs$expression <- cds_exprs$expression + cds@lowerDetectionLimit
# if (label_by_short_name == TRUE) {
# if (is.null(cds_exprs$gene_short_name) == FALSE) {
# cds_exprs$feature_label <- as.character(cds_exprs$gene_short_name)
# cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
# }
# else {
# cds_exprs$feature_label <- cds_exprs$f_id
# }
# }
# else {
# cds_exprs$feature_label <- cds_exprs$f_id
# }
# cds_exprs$feature_label <- factor(cds_exprs$feature_label)
# if (is.null(panel_order) == FALSE) {
# cds_exprs$feature_label <- factor(cds_exprs$feature_label,
# levels = panel_order)
# }
# cds_exprs$expression[is.na(cds_exprs$expression)] <- min_expr
# cds_exprs$expression[cds_exprs$expression < min_expr] <- min_expr
# cds_exprs$full_model_expectation[is.na(cds_exprs$full_model_expectation)] <- min_expr
# cds_exprs$full_model_expectation[cds_exprs$full_model_expectation < min_expr] <- min_expr
# if(!is.null(reducedModelFormulaStr)){
# cds_exprs$reduced_model_expectation[is.na(cds_exprs$reduced_model_expectation)] <- min_expr
# cds_exprs$reduced_model_expectation[cds_exprs$reduced_model_expectation < min_expr] <- min_expr
# }
cds_exprs$State <- as.factor(cds_exprs$State)
cds_exprs$Branch <- as.factor(cds_exprs$Branch)
q <- ggplot(aes(Pseudotime, expression), data = cds_exprs)
# if (!is.null(bifurcation_time)) {
# q <- q + geom_vline(aes(xintercept = bifurcation_time),
# color = "black", linetype = "longdash")
# }
if (is.null(color_by) == FALSE) {
q <- q + geom_line(aes_string(color = color_by), size = I(cell_size))
}
#if (is.null(reducedModelFormulaStr) == FALSE)
q <- q + facet_wrap(~feature_label, nrow = nrow, ncol = ncol, scales = "free_y") #+ scale_y_log10()
#else q <- q + scale_y_log10() + facet_wrap(~feature_label,
# nrow = nrow, ncol = ncol, scales = "free_y")
#if (method == "loess")
# q <- q + stat_smooth(aes(fill = Branch, color = Branch),
# method = "loess")
#else if (method == "fitting") {
# q <- q + geom_line(aes_string(x = "Pseudotime", y = "full_model_expectation",
# linetype = "Branch"), data = cds_exprs) #+ scale_color_manual(name = "Type", values = c(colour_cell, colour), labels = c("Pre-branch", "AT1", "AT2", "AT1", "AT2")
#}
#if(!is.null(reducedModelFormulaStr)) {
# q <- q + geom_line(aes_string(x = "Pseudotime", y = "reduced_model_expectation"),
# color = 'black', linetype = 2, data = cds_exprs)
#}
q <- q + ylab("Expression") + xlab("Pseudotime (stretched)")
q <- q + monocle_theme_opts()
q + expand_limits(y = min_expr)
}
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Defines functions plot_cell_trajectory monocle_theme_opts
Documented in plot_cell_trajectory
utils::globalVariables(c("Pseudotime", "value", "ids", "prin_graph_dim_1", "prin_graph_dim_2", "State",
"value", "feature_label", "expectation", "colInd", "rowInd", "value",
"source_prin_graph_dim_1", "source_prin_graph_dim_2"))
monocle_theme_opts <- function()
{
theme(strip.background = element_rect(colour = 'white', fill = 'white')) +
theme(panel.border = element_blank()) +
theme(axis.line.x = element_line(size=0.25, color="black")) +
theme(axis.line.y = element_line(size=0.25, color="black")) +
theme(panel.grid.minor.x = element_blank(), panel.grid.minor.y = element_blank()) +
theme(panel.grid.major.x = element_blank(), panel.grid.major.y = element_blank()) +
theme(panel.background = element_rect(fill='white')) +
theme(legend.key=element_blank())
}
#' Plots the minimum spanning tree on cells.
#'
#' @param cds CellDataSet for the experiment
#' @param x the column of reducedDimS(cds) to plot on the horizontal axis
#' @param y the column of reducedDimS(cds) to plot on the vertical axis
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to map to each cell's color
#' @param show_tree whether to show the links between cells connected in the minimum spanning tree
#' @param show_backbone whether to show the diameter path of the MST used to order the cells
#' @param backbone_color the color used to render the backbone.
#' @param markers a gene name or gene id to use for setting the size of each cell in the plot
#' @param use_color_gradient Whether or not to use color gradient instead of cell size to show marker expression level
#' @param markers_linear a boolean used to indicate whether you want to scale the markers logarithimically or linearly
#' @param show_cell_names draw the name of each cell in the plot
#' @param show_state_number show state number
#' @param cell_size The size of the point for each cell
#' @param cell_link_size The size of the line segments connecting cells (when used with ICA) or the principal graph (when used with DDRTree)
#' @param cell_name_size the size of cell name labels
#' @param state_number_size the size of the state number
#' @param show_branch_points Whether to show icons for each branch point (only available when reduceDimension was called with DDRTree)
#' @param theta How many degrees you want to rotate the trajectory
#' @param ... Additional arguments passed into scale_color_viridis function
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
#' @importFrom igraph get.edgelist
#' @importFrom tibble rownames_to_column
#' @importFrom viridis scale_color_viridis
#' @importFrom dplyr left_join mutate n slice
#' @export
#' @examples
#' \dontrun{
#' lung <- load_lung()
#' plot_cell_trajectory(lung)
#' plot_cell_trajectory(lung, color_by="Pseudotime", show_backbone=FALSE)
#' plot_cell_trajectory(lung, markers="MYH3")
#' }
plot_cell_trajectory <- function(cds,
x=1,
y=2,
color_by="State",
show_tree=TRUE,
show_backbone=TRUE,
backbone_color="black",
markers=NULL,
use_color_gradient = FALSE,
markers_linear = FALSE,
show_cell_names=FALSE,
show_state_number = FALSE,
cell_size=1.5,
cell_link_size=0.75,
cell_name_size=2,
state_number_size = 2.9,
show_branch_points=TRUE,
theta = 0,
...) {
requireNamespace("igraph")
gene_short_name <- NA
sample_name <- NA
sample_state <- pData(cds)$State
data_dim_1 <- NA
data_dim_2 <- NA
#TODO: need to validate cds as ready for this plot (need mst, pseudotime, etc)
lib_info_with_pseudo <- pData(cds)
if (is.null(cds@dim_reduce_type)){
stop("Error: dimensionality not yet reduced. Please call reduceDimension() before calling this function.")
}
if (cds@dim_reduce_type == "ICA"){
reduced_dim_coords <- reducedDimS(cds)
} else if (cds@dim_reduce_type %in% c("simplePPT", "DDRTree") ){
reduced_dim_coords <- reducedDimK(cds)
} else {
stop("Error: unrecognized dimensionality reduction method.")
}
ica_space_df <- Matrix::t(reduced_dim_coords) %>%
as.data.frame() %>%
select_(prin_graph_dim_1 = x, prin_graph_dim_2 = y) %>%
mutate(sample_name = rownames(.), sample_state = rownames(.))
dp_mst <- minSpanningTree(cds)
if (is.null(dp_mst)){
stop("You must first call orderCells() before using this function")
}
edge_df <- dp_mst %>%
igraph::as_data_frame() %>%
select_(source = "from", target = "to") %>%
left_join(ica_space_df %>% select_(source="sample_name", source_prin_graph_dim_1="prin_graph_dim_1", source_prin_graph_dim_2="prin_graph_dim_2"), by = "source") %>%
left_join(ica_space_df %>% select_(target="sample_name", target_prin_graph_dim_1="prin_graph_dim_1", target_prin_graph_dim_2="prin_graph_dim_2"), by = "target")
data_df <- t(monocle::reducedDimS(cds)) %>%
as.data.frame() %>%
select_(data_dim_1 = x, data_dim_2 = y) %>%
rownames_to_column("sample_name") %>%
mutate(sample_state) %>%
left_join(lib_info_with_pseudo %>% rownames_to_column("sample_name"), by = "sample_name")
return_rotation_mat <- function(theta) {
theta <- theta / 180 * pi
matrix(c(cos(theta), sin(theta), -sin(theta), cos(theta)), nrow = 2)
}
rot_mat <- return_rotation_mat(theta)
cn1 <- c("data_dim_1", "data_dim_2")
cn2 <- c("source_prin_graph_dim_1", "source_prin_graph_dim_2")
cn3 <- c("target_prin_graph_dim_1", "target_prin_graph_dim_2")
data_df[, cn1] <- as.matrix(data_df[, cn1]) %*% t(rot_mat)
edge_df[, cn2] <- as.matrix(edge_df[, cn2]) %*% t(rot_mat)
edge_df[, cn3] <- as.matrix(edge_df[, cn3]) %*% t(rot_mat)
markers_exprs <- NULL
if (is.null(markers) == FALSE) {
markers_fData <- subset(fData(cds), gene_short_name %in% markers)
if (nrow(markers_fData) >= 1) {
markers_exprs <- reshape2::melt(as.matrix(exprs(cds[row.names(markers_fData),])))
colnames(markers_exprs)[1:2] <- c('feature_id','cell_id')
markers_exprs <- merge(markers_exprs, markers_fData, by.x = "feature_id", by.y="row.names")
#print (head( markers_exprs[is.na(markers_exprs$gene_short_name) == FALSE,]))
markers_exprs$feature_label <- as.character(markers_exprs$gene_short_name)
markers_exprs$feature_label[is.na(markers_exprs$feature_label)] <- markers_exprs$Var1
}
}
if (is.null(markers_exprs) == FALSE && nrow(markers_exprs) > 0){
data_df <- merge(data_df, markers_exprs, by.x="sample_name", by.y="cell_id")
if(use_color_gradient) {
if(markers_linear){
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2)) + geom_point(aes(color= value), size=I(cell_size), na.rm = TRUE) +
scale_color_viridis(name = paste0("value"), ...) + facet_wrap(~feature_label)
} else {
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2)) + geom_point(aes(color=log10(value + 0.1)), size=I(cell_size), na.rm = TRUE) +
scale_color_viridis(name = paste0("log10(value + 0.1)"), ...) + facet_wrap(~feature_label)
}
} else {
if(markers_linear){
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2, size= (value * 0.1))) + facet_wrap(~feature_label)
} else {
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2, size=log10(value + 0.1))) + facet_wrap(~feature_label)
}
}
} else {
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2))
}
if (show_tree){
g <- g + geom_segment(aes_string(x="source_prin_graph_dim_1", y="source_prin_graph_dim_2", xend="target_prin_graph_dim_1", yend="target_prin_graph_dim_2"), size=cell_link_size, linetype="solid", na.rm=TRUE, data=edge_df)
}
# FIXME: setting size here overrides the marker expression funtionality.
# Don't do it!
if (is.null(markers_exprs) == FALSE && nrow(markers_exprs) > 0){
if(use_color_gradient) {
# g <- g + geom_point(aes_string(color = color_by), na.rm = TRUE)
} else {
g <- g + geom_point(aes_string(color = color_by), na.rm = TRUE)
}
}else {
if(use_color_gradient) {
# g <- g + geom_point(aes_string(color = color_by), na.rm = TRUE)
} else {
g <- g + geom_point(aes_string(color = color_by), size=I(cell_size), na.rm = TRUE)
}
}
if (show_branch_points && cds@dim_reduce_type == 'DDRTree'){
mst_branch_nodes <- cds@auxOrderingData[[cds@dim_reduce_type]]$branch_points
branch_point_df <- ica_space_df %>%
slice(match(mst_branch_nodes, sample_name)) %>%
mutate(branch_point_idx = seq_len(n()))
g <- g +
geom_point(aes_string(x="prin_graph_dim_1", y="prin_graph_dim_2"),
size=5, na.rm=TRUE, branch_point_df) +
geom_text(aes_string(x="prin_graph_dim_1", y="prin_graph_dim_2", label="branch_point_idx"),
size=4, color="white", na.rm=TRUE, branch_point_df)
}
if (show_cell_names){
g <- g + geom_text(aes(label=sample_name), size=cell_name_size)
}
if (show_state_number){
g <- g + geom_text(aes(label = sample_state), size = state_number_size)
}
g <- g +
#scale_color_brewer(palette="Set1") +
monocle_theme_opts() +
xlab(paste("Component", x)) +
ylab(paste("Component", y)) +
theme(legend.position="top", legend.key.height=grid::unit(0.35, "in")) +
#guides(color = guide_legend(label.position = "top")) +
theme(legend.key = element_blank()) +
theme(panel.background = element_rect(fill='white'))
g
}
#' @rdname package-deprecated
#' @title Plots the minimum spanning tree on cells.
#' This function is deprecated.
#' @description This function arranges all of the cells in the cds in a tree and
#' predicts their location based on their pseudotime value
#' @param cds CellDataSet for the experiment
#' @param x the column of reducedDimS(cds) to plot on the horizontal axis
#' @param y the column of reducedDimS(cds) to plot on the vertical axis
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to map to each cell's color
#' @param show_tree whether to show the links between cells connected in the minimum spanning tree
#' @param show_backbone whether to show the diameter path of the MST used to order the cells
#' @param backbone_color the color used to render the backbone.
#' @param markers a gene name or gene id to use for setting the size of each cell in the plot
#' @param show_cell_names draw the name of each cell in the plot
#' @param cell_size The size of the point for each cell
#' @param cell_link_size The size of the line segments connecting cells (when used with ICA) or the principal graph (when used with DDRTree)
#' @param cell_name_size the size of cell name labels
#' @param show_branch_points Whether to show icons for each branch point (only available when reduceDimension was called with DDRTree)
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
#' @export
#' @seealso plot_cell_trajectory
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' plot_cell_trajectory(HSMM)
#' plot_cell_trajectory(HSMM, color_by="Pseudotime", show_backbone=FALSE)
#' plot_cell_trajectory(HSMM, markers="MYH3")
#' }
plot_spanning_tree <- function(cds,
x=1,
y=2,
color_by="State",
show_tree=TRUE,
show_backbone=TRUE,
backbone_color="black",
markers=NULL,
show_cell_names=FALSE,
cell_size=1.5,
cell_link_size=0.75,
cell_name_size=2,
show_branch_points=TRUE){
.Deprecated("plot_cell_trajectory") #include a package argument, too
plot_cell_trajectory(cds=cds,
x=x,
y=y,
color_by=color_by,
show_tree=show_tree,
show_backbone=show_backbone,
backbone_color=backbone_color,
markers=markers,
show_cell_names=show_cell_names,
cell_size=cell_size,
cell_link_size=cell_link_size,
cell_name_size=cell_name_size,
show_branch_points=show_branch_points)
}
#' @title Plots expression for one or more genes as a violin plot
#'
#' @description Accepts a subset of a CellDataSet and an attribute to group cells by,
#' and produces one or more ggplot2 objects that plots the level of expression for
#' each group of cells.
#'
#' @param cds_subset CellDataSet for the experiment
#' @param grouping the cell attribute (e.g. the column of pData(cds)) to group cells by on the horizontal axis
#' @param min_expr the minimum (untransformed) expression level to use in plotted the genes.
#' @param cell_size the size (in points) of each cell used in the plot
#' @param nrow the number of rows used when laying out the panels for each gene's expression
#' @param ncol the number of columns used when laying out the panels for each gene's expression
#' @param panel_order the order in which genes should be layed out (left-to-right, top-to-bottom)
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to be used to color each cell
#' @param plot_trend whether to plot a trendline tracking the average expression across the horizontal axis.
#' @param label_by_short_name label figure panels by gene_short_name (TRUE) or feature id (FALSE)
#' @param relative_expr Whether to transform expression into relative values
#' @param log_scale a boolean that determines whether or not to scale data logarithmically
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
#' @importFrom BiocGenerics sizeFactors
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' my_genes <- HSMM[row.names(subset(fData(HSMM), gene_short_name %in% c("ACTA1", "ID1", "CCNB2"))),]
#' plot_genes_violin(my_genes, grouping="Hours", ncol=2, min_expr=0.1)
#' }
plot_genes_violin <- function (cds_subset, grouping = "State", min_expr = NULL, cell_size = 0.75,
nrow = NULL, ncol = 1, panel_order = NULL, color_by = NULL,
plot_trend = FALSE, label_by_short_name = TRUE, relative_expr = TRUE,
log_scale = TRUE)
{
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial",
"negbinomial.size")) {
integer_expression = TRUE
}
else {
integer_expression = FALSE
relative_expr = TRUE
}
if (integer_expression) {
cds_exprs = exprs(cds_subset)
if (relative_expr) {
if (is.null(sizeFactors(cds_subset))) {
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
cds_exprs = Matrix::t(Matrix::t(cds_exprs)/sizeFactors(cds_subset))
}
#cds_exprs = reshape2::melt(round(as.matrix(cds_exprs)))
cds_exprs = reshape2::melt(as.matrix(cds_exprs))
}
else {
cds_exprs = exprs(cds_subset)
cds_exprs = reshape2::melt(as.matrix(cds_exprs))
}
if (is.null(min_expr)) {
min_expr = cds_subset@lowerDetectionLimit
}
colnames(cds_exprs) = c("f_id", "Cell", "expression")
cds_exprs$expression[cds_exprs$expression < min_expr] = min_expr
cds_pData = pData(cds_subset)
#
# # Custom bit for adding in a group for
# if(! is.null(show_combined)) {
# for(combine_gene in show_combined) {
# cds_pData_all = subset(cds_pData, gene == combine_gene)
# cds_pData_all[, grouping] = paste("All", combine_gene)
# cds_pData = rbind(cds_pData, cds_pData_all)
# }
# }
cds_fData = fData(cds_subset)
cds_exprs = merge(cds_exprs, cds_fData, by.x = "f_id", by.y = "row.names")
cds_exprs = merge(cds_exprs, cds_pData, by.x = "Cell", by.y = "row.names")
cds_exprs$adjusted_expression = log10(cds_exprs$expression)
if (label_by_short_name == TRUE) {
if (is.null(cds_exprs$gene_short_name) == FALSE) {
cds_exprs$feature_label = cds_exprs$gene_short_name
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] = cds_exprs$f_id
}
else {
cds_exprs$feature_label = cds_exprs$f_id
}
}
else {
cds_exprs$feature_label = cds_exprs$f_id
}
if (is.null(panel_order) == FALSE) {
cds_exprs$feature_label = factor(cds_exprs$feature_label,
levels = panel_order)
}
q = ggplot(aes_string(x = grouping, y = "expression"), data = cds_exprs)
if (is.null(color_by) == FALSE) {
q = q + geom_violin(aes_string(fill = color_by))
}
else {
q = q + geom_violin()
}
if (plot_trend == TRUE) {
q = q + stat_summary(fun.data = "mean_cl_boot",
size = 0.2)
q = q + stat_summary(aes_string(x = grouping, y = "expression",
group = color_by), fun.data = "mean_cl_boot",
size = 0.2, geom = "line")
}
q = q + facet_wrap(~feature_label, nrow = nrow,
ncol = ncol, scales = "free_y")
if (min_expr < 1) {
q = q + expand_limits(y = c(min_expr, 1))
}
q = q + ylab("Expression") + xlab(grouping)
if (log_scale == TRUE){
q = q + scale_y_log10()
}
q
}
#' Plots expression for one or more genes as a jittered, grouped points
#'
#' @description Accepts a subset of a CellDataSet and an attribute to group cells by,
#' and produces one or more ggplot2 objects that plots the level of expression for
#' each group of cells.
#'
#' @param cds_subset CellDataSet for the experiment
#' @param grouping the cell attribute (e.g. the column of pData(cds)) to group cells by on the horizontal axis
#' @param min_expr the minimum (untransformed) expression level to use in plotted the genes.
#' @param cell_size the size (in points) of each cell used in the plot
#' @param nrow the number of rows used when laying out the panels for each gene's expression
#' @param ncol the number of columns used when laying out the panels for each gene's expression
#' @param panel_order the order in which genes should be layed out (left-to-right, top-to-bottom)
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to be used to color each cell
#' @param plot_trend whether to plot a trendline tracking the average expression across the horizontal axis.
#' @param label_by_short_name label figure panels by gene_short_name (TRUE) or feature id (FALSE)
#' @param relative_expr Whether to transform expression into relative values
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
#' @importFrom BiocGenerics sizeFactors
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' my_genes <- HSMM[row.names(subset(fData(HSMM), gene_short_name %in% c("MYOG", "ID1", "CCNB2"))),]
#' plot_genes_jitter(my_genes, grouping="Media", ncol=2)
#' }
plot_genes_jitter <- function(cds_subset,
grouping = "State",
min_expr=NULL,
cell_size=0.75,
nrow=NULL,
ncol=1,
panel_order=NULL,
color_by=NULL,
plot_trend=FALSE,
label_by_short_name=TRUE,
relative_expr=TRUE){
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")){
integer_expression <- TRUE
}else{
integer_expression <- FALSE
relative_expr <- TRUE
}
if (integer_expression)
{
cds_exprs <- exprs(cds_subset)
if (relative_expr){
if (is.null(sizeFactors(cds_subset)))
{
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
cds_exprs <- Matrix::t(Matrix::t(cds_exprs) / sizeFactors(cds_subset))
}
cds_exprs <- reshape2::melt(round(as.matrix(cds_exprs)))
}else{
cds_exprs <- exprs(cds_subset)
cds_exprs <- reshape2::melt(as.matrix(cds_exprs))
}
if (is.null(min_expr)){
min_expr <- cds_subset@lowerDetectionLimit
}
colnames(cds_exprs) <- c("f_id", "Cell", "expression")
cds_exprs$expression[cds_exprs$expression < min_expr] <- min_expr
cds_pData <- pData(cds_subset)
cds_fData <- fData(cds_subset)
cds_exprs <- merge(cds_exprs, cds_fData, by.x="f_id", by.y="row.names")
cds_exprs <- merge(cds_exprs, cds_pData, by.x="Cell", by.y="row.names")
cds_exprs$adjusted_expression <- log10(cds_exprs$expression)
#cds_exprs$adjusted_expression <- log10(cds_exprs$adjusted_expression + abs(rnorm(nrow(cds_exprs), min_expr, sqrt(min_expr))))
if (label_by_short_name == TRUE){
if (is.null(cds_exprs$gene_short_name) == FALSE){
cds_exprs$feature_label <- cds_exprs$gene_short_name
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
}else{
cds_exprs$feature_label <- cds_exprs$f_id
}
}else{
cds_exprs$feature_label <- cds_exprs$f_id
}
#print (head(cds_exprs))
if (is.null(panel_order) == FALSE)
{
cds_exprs$feature_label <- factor(cds_exprs$feature_label, levels=panel_order)
}
q <- ggplot(aes_string(x=grouping, y="expression"), data=cds_exprs)
if (is.null(color_by) == FALSE){
q <- q + geom_jitter(aes_string(color=color_by), size=I(cell_size))
}else{
q <- q + geom_jitter(size=I(cell_size))
}
if (plot_trend == TRUE){
q <- q + stat_summary(aes_string(color=color_by), fun.data = "mean_cl_boot", size=0.35)
q <- q + stat_summary(aes_string(x=grouping, y="expression", color=color_by, group=color_by), fun.data = "mean_cl_boot", size=0.35, geom="line")
}
q <- q + scale_y_log10() + facet_wrap(~feature_label, nrow=nrow, ncol=ncol, scales="free_y")
# Need this to guard against plotting failures caused by non-expressed genes
if (min_expr < 1)
{
q <- q + expand_limits(y=c(min_expr, 1))
}
q <- q + ylab("Expression") + xlab(grouping)
q <- q + monocle_theme_opts()
q
}
#' Plots the number of cells expressing one or more genes as a barplot
#'
#' @description Accetps a CellDataSet and a parameter,"grouping", used for dividing cells into groups.
#' Returns one or more bar graphs (one graph for each gene in the CellDataSet).
#' Each graph shows the percentage of cells that express a gene in the in the CellDataSet for
#' each sub-group of cells created by "grouping".
#'
#' Let's say the CellDataSet passed in included genes A, B, and C and the "grouping parameter divided
#' all of the cells into three groups called X, Y, and Z. Then three graphs would be produced called A,
#' B, and C. In the A graph there would be three bars one for X, one for Y, and one for Z. So X bar in the
#' A graph would show the percentage of cells in the X group that express gene A.
#'
#' @param cds_subset CellDataSet for the experiment
#' @param grouping the cell attribute (e.g. the column of pData(cds)) to group cells by on the horizontal axis
#' @param min_expr the minimum (untransformed) expression level to use in plotted the genes.
#' @param nrow the number of rows used when laying out the panels for each gene's expression
#' @param ncol the number of columns used when laying out the panels for each gene's expression
#' @param panel_order the order in which genes should be layed out (left-to-right, top-to-bottom)
#' @param plot_as_fraction whether to show the percent instead of the number of cells expressing each gene
#' @param label_by_short_name label figure panels by gene_short_name (TRUE) or feature id (FALSE)
#' @param relative_expr Whether to transform expression into relative values
#' @param plot_limits A pair of number specifying the limits of the y axis. If NULL, scale to the range of the data.
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom plyr ddply
#' @importFrom reshape2 melt
#' @importFrom BiocGenerics sizeFactors
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' MYOG_ID1 <- HSMM[row.names(subset(fData(HSMM), gene_short_name %in% c("MYOG", "ID1"))),]
#' plot_genes_positive_cells(MYOG_ID1, grouping="Media", ncol=2)
#' }
plot_genes_positive_cells <- function(cds_subset,
grouping = "State",
min_expr=0.1,
nrow=NULL,
ncol=1,
panel_order=NULL,
plot_as_fraction=TRUE,
label_by_short_name=TRUE,
relative_expr=TRUE,
plot_limits=c(0,100)){
percent <- NULL
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")){
integer_expression <- TRUE
}else{
integer_expression <- FALSE
relative_expr <- TRUE
}
if (integer_expression)
{
marker_exprs <- exprs(cds_subset)
if (relative_expr){
if (is.null(sizeFactors(cds_subset)))
{
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
marker_exprs <- Matrix::t(Matrix::t(marker_exprs) / sizeFactors(cds_subset))
}
marker_exprs_melted <- reshape2::melt(round(as.matrix(marker_exprs)))
}else{
marker_exprs_melted <- reshape2::melt(as.matrix(exprs(cds_subset)))
}
colnames(marker_exprs_melted) <- c("f_id", "Cell", "expression")
marker_exprs_melted <- merge(marker_exprs_melted, pData(cds_subset), by.x="Cell", by.y="row.names")
marker_exprs_melted <- merge(marker_exprs_melted, fData(cds_subset), by.x="f_id", by.y="row.names")
if (label_by_short_name == TRUE){
if (is.null(marker_exprs_melted$gene_short_name) == FALSE){
marker_exprs_melted$feature_label <- marker_exprs_melted$gene_short_name
marker_exprs_melted$feature_label[is.na(marker_exprs_melted$feature_label)] <- marker_exprs_melted$f_id
}else{
marker_exprs_melted$feature_label <- marker_exprs_melted$f_id
}
}else{
marker_exprs_melted$feature_label <- marker_exprs_melted$f_id
}
if (is.null(panel_order) == FALSE)
{
marker_exprs_melted$feature_label <- factor(marker_exprs_melted$feature_label, levels=panel_order)
}
marker_counts <- plyr::ddply(marker_exprs_melted, c("feature_label", grouping), function(x) {
data.frame(target=sum(x$expression > min_expr),
target_fraction=sum(x$expression > min_expr)/nrow(x)) } )
#print (head(marker_counts))
if (plot_as_fraction){
marker_counts$target_fraction <- marker_counts$target_fraction * 100
qp <- ggplot(aes_string(x=grouping, y="target_fraction", fill=grouping), data=marker_counts) +
ylab("Cells (percent)")
if (is.null(plot_limits) == FALSE)
qp <- qp + scale_y_continuous(limits=plot_limits)
}else{
qp <- ggplot(aes_string(x=grouping, y="target", fill=grouping), data=marker_counts) +
ylab("Cells")
}
qp <- qp + facet_wrap(~feature_label, nrow=nrow, ncol=ncol, scales="free_y")
qp <- qp + geom_bar(stat="identity") + monocle_theme_opts()
return(qp)
}
#' Plots expression for one or more genes as a function of pseudotime
#'
#' @description Plots expression for one or more genes as a function of pseudotime.
#' Plotting allows you determine if the ordering produced by orderCells() is correct
#' and it does not need to be flipped using the "reverse" flag in orderCells
#'
#' @param cds_subset CellDataSet for the experiment
#' @param min_expr the minimum (untransformed) expression level to use in plotted the genes.
#' @param cell_size the size (in points) of each cell used in the plot
#' @param nrow the number of rows used when laying out the panels for each gene's expression
#' @param ncol the number of columns used when laying out the panels for each gene's expression
#' @param panel_order the order in which genes should be layed out (left-to-right, top-to-bottom)
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to be used to color each cell
#' @param trend_formula the model formula to be used for fitting the expression trend over pseudotime
#' @param label_by_short_name label figure panels by gene_short_name (TRUE) or feature id (FALSE)
#' @param relative_expr Whether to transform expression into relative values
#' @param vertical_jitter A value passed to ggplot to jitter the points in the vertical dimension. Prevents overplotting, and is particularly helpful for rounded transcript count data.
#' @param horizontal_jitter A value passed to ggplot to jitter the points in the horizontal dimension. Prevents overplotting, and is particularly helpful for rounded transcript count data.
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom plyr ddply .
#' @importFrom reshape2 melt
#' @importFrom ggplot2 Position
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' my_genes <- row.names(subset(fData(HSMM), gene_short_name %in% c("CDK1", "MEF2C", "MYH3")))
#' cds_subset <- HSMM[my_genes,]
#' plot_genes_in_pseudotime(cds_subset, color_by="Time")
#' }
plot_genes_in_pseudotime <-function(cds_subset,
min_expr=NULL,
cell_size=0.75,
nrow=NULL,
ncol=1,
panel_order=NULL,
color_by="State",
trend_formula="~ sm.ns(Pseudotime, df=3)",
label_by_short_name=TRUE,
relative_expr=TRUE,
vertical_jitter=NULL,
horizontal_jitter=NULL){
f_id <- NA
Cell <- NA
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")) {
integer_expression <- TRUE
}
else {
integer_expression <- FALSE
relative_expr <- TRUE
}
if (integer_expression) {
cds_exprs <- exprs(cds_subset)
if (relative_expr) {
if (is.null(sizeFactors(cds_subset))) {
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
cds_exprs <- Matrix::t(Matrix::t(cds_exprs)/sizeFactors(cds_subset))
}
cds_exprs <- reshape2::melt(round(as.matrix(cds_exprs)))
}
else {
cds_exprs <- reshape2::melt(as.matrix(exprs(cds_subset)))
}
if (is.null(min_expr)) {
min_expr <- cds_subset@lowerDetectionLimit
}
colnames(cds_exprs) <- c("f_id", "Cell", "expression")
cds_pData <- pData(cds_subset)
cds_fData <- fData(cds_subset)
cds_exprs <- merge(cds_exprs, cds_fData, by.x = "f_id", by.y = "row.names")
cds_exprs <- merge(cds_exprs, cds_pData, by.x = "Cell", by.y = "row.names")
#cds_exprs$f_id <- as.character(cds_exprs$f_id)
#cds_exprs$Cell <- as.character(cds_exprs$Cell)
if (integer_expression) {
cds_exprs$adjusted_expression <- cds_exprs$expression
}
else {
cds_exprs$adjusted_expression <- log10(cds_exprs$expression)
}
# trend_formula <- paste("adjusted_expression", trend_formula,
# sep = "")
if (label_by_short_name == TRUE) {
if (is.null(cds_exprs$gene_short_name) == FALSE) {
cds_exprs$feature_label <- as.character(cds_exprs$gene_short_name)
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
cds_exprs$f_id <- as.character(cds_exprs$f_id)
cds_exprs$feature_label <- factor(cds_exprs$feature_label)
new_data <- data.frame(Pseudotime = pData(cds_subset)$Pseudotime)
model_expectation <- genSmoothCurves(cds_subset, cores=1, trend_formula = trend_formula,
relative_expr = T, new_data = new_data)
colnames(model_expectation) <- colnames(cds_subset)
expectation <- ddply(cds_exprs, .(f_id, Cell), function(x) data.frame("expectation"=model_expectation[x$f_id, x$Cell]))
cds_exprs <- merge(cds_exprs, expectation)
#cds_exprs$expectation <- expectation#apply(cds_exprs,1, function(x) model_expectation[x$f_id, x$Cell])
cds_exprs$expression[cds_exprs$expression < min_expr] <- min_expr
cds_exprs$expectation[cds_exprs$expectation < min_expr] <- min_expr
if (is.null(panel_order) == FALSE) {
cds_exprs$feature_label <- factor(cds_exprs$feature_label,
levels = panel_order)
}
q <- ggplot(aes(Pseudotime, expression), data = cds_exprs)
if (is.null(color_by) == FALSE) {
q <- q + geom_point(aes_string(color = color_by), size = I(cell_size), position=position_jitter(horizontal_jitter, vertical_jitter))
}
else {
q <- q + geom_point(size = I(cell_size), position=position_jitter(horizontal_jitter, vertical_jitter))
}
q <- q + geom_line(aes(x = Pseudotime, y = expectation), data = cds_exprs)
q <- q + scale_y_log10() + facet_wrap(~feature_label, nrow = nrow,
ncol = ncol, scales = "free_y")
if (min_expr < 1) {
q <- q + expand_limits(y = c(min_expr, 1))
}
if (relative_expr) {
q <- q + ylab("Relative Expression")
}
else {
q <- q + ylab("Absolute Expression")
}
q <- q + xlab("Pseudo-time")
q <- q + monocle_theme_opts()
q
}
#' Plots kinetic clusters of genes.
#'
#' @description returns a ggplot2 object showing the shapes of the
#' expression patterns followed by a set of pre-selected genes.
#' The topographic lines highlight the distributions of the kinetic patterns
#' relative to overall trend lines.
#'
#' @param cds CellDataSet for the experiment
#' @param clustering a clustering object produced by clusterCells
#' @param drawSummary whether to draw the summary line for each cluster
#' @param sumFun whether the function used to generate the summary for each cluster
#' @param ncol number of columns used to layout the faceted cluster panels
#' @param nrow number of columns used to layout the faceted cluster panels
#' @param row_samples how many genes to randomly select from the data
#' @param callout_ids a vector of gene names or gene ids to manually render as part of the plot
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
#' @importFrom stringr str_c
#' @importFrom ggplot2 Position
#' @import grid
#' @export
#' @examples
#' \dontrun{
#' full_model_fits <- fitModel(HSMM_filtered[sample(nrow(fData(HSMM_filtered)), 100),],
#' modelFormulaStr="~VGAM::bs(Pseudotime)")
#' expression_curve_matrix <- responseMatrix(full_model_fits)
#' clusters <- clusterGenes(expression_curve_matrix, k=4)
#' plot_clusters(HSMM_filtered[ordering_genes,], clusters)
#' }
plot_clusters<-function(cds,
clustering,
drawSummary=TRUE,
sumFun=mean_cl_boot,
ncol=NULL,
nrow=NULL,
row_samples=NULL,
callout_ids=NULL){
.Deprecated("plot_genes_heatmap")
m <- as.data.frame(clustering$exprs)
m$ids <- rownames(clustering$exprs)
if (is.null(clustering$labels) == FALSE)
{
m$cluster = factor(clustering$labels[clustering$clustering], levels = levels(clustering$labels))
}else{
m$cluster <- factor(clustering$clustering)
}
cluster_sizes <- as.data.frame(table(m$cluster))
cluster_sizes$Freq <- paste("(", cluster_sizes$Freq, ")")
facet_labels <- str_c(cluster_sizes$Var1, cluster_sizes$Freq, sep=" ") #update the function
m.melt <- melt(m, id.vars = c("ids", "cluster"))
m.melt <- merge(m.melt, pData(cds), by.x="variable", by.y="row.names")
if (is.null(row_samples) == FALSE){
m.melt <- m.melt[sample(nrow(m.melt), row_samples),]
}
c <- ggplot(m.melt) + facet_wrap("cluster", ncol=ncol, nrow=nrow, scales="free_y")
#c <- c + stat_density2d(aes(x = Pseudotime, y = value), geom="polygon", fill="white", color="black", size=I(0.1)) + facet_wrap("cluster", ncol=ncol, nrow=nrow)
if (drawSummary) {
c <- c + stat_summary(aes(x = Pseudotime, y = value, group = 1),
fun.data = sumFun, color = "red",
alpha = 0.2, size = 0.5, geom = "smooth")
}
#cluster_medians <- subset(m.melt, ids %in% clustering$medoids)
#c <- c + geom_line()
#c <- c + geom_line(aes(x=Pseudotime, y=value), data=cluster_medians, color=I("red"))
c <- c + scale_color_hue(l = 50, h.start = 200) + theme(axis.text.x = element_text(angle = 0,
hjust = 0)) + xlab("Pseudo-time") + ylab("Expression")
c <- c + theme(strip.background = element_rect(colour = 'white', fill = 'white')) +
theme(panel.border = element_blank()) +
theme(legend.position="none") +
theme(panel.grid.minor.x = element_blank(), panel.grid.minor.y = element_blank()) +
theme(panel.grid.major.x = element_blank(), panel.grid.major.y = element_blank())
# if (draw_cluster_size){
# cluster_sizes <- as.data.frame(table(m$cluster))
# colnames(cluster_sizes) <- c("cluster", "Freq")
# cluster_sizes <- cbind (cluster_sizes, Pseudotime = cluster_label_text_x, value = cluster_label_text_y)
# c <- c + geom_text(aes(x=Pseudotime, y=value, label=Freq), data=cluster_sizes, size=cluster_label_text_size)
# }
if (is.null(callout_ids) == FALSE)
{
callout_melt <- subset(m.melt, ids %in% callout_ids)
c <- c + geom_line(aes(x=Pseudotime, y=value), data=callout_melt, color=I("steelblue"))
}
c <- c + monocle_theme_opts()
#c <- facet_wrap_labeller(c, facet_labels)
c
}
#
# #' Plots a pseudotime-ordered, row-centered heatmap
# #' @export
# plot_genes_heatmap <- function(cds,
# rescaling='row',
# clustering='row',
# labCol=FALSE,
# labRow=TRUE,
# logMode=TRUE,
# use_vst=TRUE,
# border=FALSE,
# heatscale=c(low='steelblue',mid='white',high='tomato'),
# heatMidpoint=0,
# method="none",
# scaleMax=2,
# scaleMin=-2,
# relative_expr=TRUE,
# ...){
#
# ## the function can be be viewed as a two step process
# ## 1. using the rehape package and other funcs the data is clustered, scaled, and reshaped
# ## using simple options or by a user supplied function
# ## 2. with the now resahped data the plot, the chosen labels and plot style are built
# FM <- exprs(cds)
#
# if (cds@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")){
# integer_expression <- TRUE
# }else{
# integer_expression <- FALSE
# relative_expr <- TRUE
# }
#
# if (integer_expression)
# {
# if (relative_expr){
# if (is.null(sizeFactors(cds)))
# {
# stop("Error: you must call estimateSizeFactors() first")
# }
# FM <- Matrix::t(Matrix::t(FM) / sizeFactors(cds))
# }
# FM <- round(FM)
# }
#
# m=FM
#
# if (is.null(fData(cds)$gene_short_name) == FALSE){
# feature_labels <- fData(cds)$gene_short_name
# feature_labels[is.na(feature_labels)] <- fData(cds)$f_id
# row.names(m) <- feature_labels
# }
#
# #remove genes with no expression in any condition
# m=m[!apply(m,1,sum)==0,]
#
# if (use_vst && is.null(cds@dispFitInfo[["blind"]]$disp_func) == FALSE){
# m = vstExprs(cds, expr_matrix=m)
# }else if(logMode){
# m = log10(m+pseudocount)
# }
#
# #remove genes with no sd
# #m=m[!apply(m,1,sd)==0,]
#
# ## you can either scale by row or column not both!
# ## if you wish to scale by both or use a different scale method then simply supply a scale
# ## function instead NB scale is a base funct
#
# ## I have supplied the default cluster and euclidean distance (JSdist) - and chose to cluster after scaling
# ## if you want a different distance/cluster method-- or to cluster and then scale
# ## then you can supply a custom function
#
# if(!is.function(method)){
# method = function(mat){as.dist((1 - cor(Matrix::t(mat)))/2)}
# }
#
# ## this is just reshaping into a ggplot format matrix and making a ggplot layer
#
# if(is.function(rescaling))
# {
# m=rescaling(m)
# } else {
# if(rescaling=='column'){
# m=m[!apply(m,2,sd)==0,]
# m=scale(m, center=TRUE)
# m[is.nan(m)] = 0
# m[m>scaleMax] = scaleMax
# m[m<scaleMin] = scaleMin
# }
# if(rescaling=='row'){
# m=m[!apply(m,1,sd)==0,]
# m=Matrix::t(scale(Matrix::t(m),center=TRUE))
# m[is.nan(m)] = 0
# m[m>scaleMax] = scaleMax
# m[m<scaleMin] = scaleMin
# }
# }
#
# # If we aren't going to re-ordering the columns, order them by Pseudotime
# if (clustering %in% c("row", "none"))
# m = m[,row.names(pData(cds)[order(-pData(cds)$Pseudotime),])]
#
# if(clustering=='row')
# m=m[hclust(method(m))$order, ]
# if(clustering=='column')
# m=m[,hclust(method(Matrix::t(m)))$order]
# if(clustering=='both')
# m=m[hclust(method(m))$order ,hclust(method(Matrix::t(m)))$order]
#
#
# rows=dim(m)[1]
# cols=dim(m)[2]
#
#
#
# # if(logMode) {
# # melt.m=cbind(rowInd=rep(1:rows, times=cols), colInd=rep(1:cols, each=rows), reshape2::melt( log10(m+pseudocount)))
# # }else{
# # melt.m=cbind(rowInd=rep(1:rows, times=cols), colInd=rep(1:cols, each=rows), reshape2::melt(m))
# # }
#
# melt.m=cbind(rowInd=rep(1:rows, times=cols), colInd=rep(1:cols, each=rows), reshape2::melt(m))
#
# g=ggplot(data=melt.m)
#
# ## add the heat tiles with or without a white border for clarity
#
# if(border==TRUE)
# g2=g+geom_raster(aes(x=colInd,y=rowInd, fill=value),colour='grey')
# if(border==FALSE)
# g2=g+geom_raster(aes(x=colInd,y=rowInd,ymax=rowInd, fill=value))
#
# ## add axis labels either supplied or from the colnames rownames of the matrix
#
# if(labCol==TRUE)
# {
# g2=g2+scale_x_continuous(breaks=(1:cols)-0.5, labels=colnames(m))
# }
# if(labCol==FALSE)
# {
# g2=g2+scale_x_continuous(breaks=(1:cols)-0.5, labels=rep('',cols))
# }
#
#
# if(labRow==TRUE)
# {
# g2=g2+scale_y_continuous(breaks=(1:rows)-0.5, labels=rownames(m))
# }
# if(labRow==FALSE)
# {
# g2=g2+scale_y_continuous(breaks=(1:rows)-0.5, labels=rep('',rows))
# }
#
# # Get rid of the ticks, they get way too dense with lots of rows
# g2 <- g2 + theme(axis.ticks = element_blank())
#
# ## get rid of grey panel background and gridlines
#
# g2=g2+theme(panel.grid.minor=element_line(colour=NA), panel.grid.major=element_line(colour=NA),
# panel.background=element_rect(fill=NA, colour=NA))
#
# ##adjust x-axis labels
# g2=g2+theme(axis.text.x=element_text(angle=-90, hjust=0))
#
# #write(paste(c("Length of heatscale is :", length(heatscale))), stderr())
#
# if(is.function(rescaling))
# {
#
# }else{
# if(rescaling=='row' || rescaling == 'column'){
# legendTitle <- "Relative\nexpression"
# }else{
# if (logMode)
# {
# legendTitle <- bquote(paste(log[10]," FPKM + ",.(pseudocount),sep=""))
# #legendTitle <- paste(expression(plain(log)[10])," FPKM + ",pseudocount,sep="")
# } else {
# legendTitle <- "FPKM"
# }
# }
# }
#
# if (length(heatscale) == 2){
# g2 <- g2 + scale_fill_gradient(low=heatscale[1], high=heatscale[2], name=legendTitle)
# } else if (length(heatscale) == 3) {
# if (is.null(heatMidpoint))
# {
# heatMidpoint = (max(m) + min(m)) / 2.0
# #write(heatMidpoint, stderr())
# }
# g2 <- g2 + theme(panel.border = element_blank())
# g2 <- g2 + scale_fill_gradient2(low=heatscale[1], mid=heatscale[2], high=heatscale[3], midpoint=heatMidpoint, name=legendTitle)
# }else {
# g2 <- g2 + scale_fill_gradientn(colours=heatscale, name=legendTitle)
# }
#
# #g2<-g2+scale_x_discrete("",breaks=tracking_ids,labels=gene_short_names)
#
# g2 <- g2 + theme(axis.title.x=element_blank(), axis.title.y=element_blank())
#
# ## finally add the fill colour ramp of your choice (default is blue to red)-- and return
# return (g2)
# }
plot_genes_heatmap <- function(...){
.Deprecated("plot_pseudotime_heatmap")
plot_pseudotime_heatmap(...)
}
#' Plots a pseudotime-ordered, row-centered heatmap
#'
#' @description The function plot_pseudotime_heatmap takes a CellDataSet object
#' (usually containing a only subset of significant genes) and generates smooth expression
#' curves much like plot_genes_in_pseudotime.
#' Then, it clusters these genes and plots them using the pheatmap package.
#' This allows you to visualize modules of genes that co-vary across pseudotime.
#'
#' @param cds_subset CellDataSet for the experiment (normally only the branching genes detected with branchTest)
#' @param cluster_rows Whether to cluster the rows of the heatmap.
#' @param hclust_method The method used by pheatmap to perform hirearchical clustering of the rows.
#' @param num_clusters Number of clusters for the heatmap of branch genes
#' @param hmcols The color scheme for drawing the heatmap.
#' @param add_annotation_row Additional annotations to show for each row in the heatmap. Must be a dataframe with one row for each row in the fData table of cds_subset, with matching IDs.
#' @param add_annotation_col Additional annotations to show for each column in the heatmap. Must be a dataframe with one row for each cell in the pData table of cds_subset, with matching IDs.
#' @param show_rownames Whether to show the names for each row in the table.
#' @param use_gene_short_name Whether to use the short names for each row. If FALSE, uses row IDs from the fData table.
#' @param scale_max The maximum value (in standard deviations) to show in the heatmap. Values larger than this are set to the max.
#' @param scale_min The minimum value (in standard deviations) to show in the heatmap. Values smaller than this are set to the min.
#' @param norm_method Determines how to transform expression values prior to rendering
#' @param trend_formula A formula string specifying the model used in fitting the spline curve for each gene/feature.
#' @param return_heatmap Whether to return the pheatmap object to the user.
#' @param cores Number of cores to use when smoothing the expression curves shown in the heatmap.
#' @return A list of heatmap_matrix (expression matrix for the branch committment), ph (pheatmap heatmap object),
#' annotation_row (annotation data.frame for the row), annotation_col (annotation data.frame for the column).
#' @import pheatmap
#' @importFrom stats sd as.dist cor cutree
#' @export
#'
plot_pseudotime_heatmap <- function(cds_subset,
cluster_rows = TRUE,
hclust_method = "ward.D2",
num_clusters = 6,
hmcols = NULL,
add_annotation_row = NULL,
add_annotation_col = NULL,
show_rownames = FALSE,
use_gene_short_name = TRUE,
norm_method = c("log", "vstExprs"),
scale_max=3,
scale_min=-3,
trend_formula = '~sm.ns(Pseudotime, df=3)',
return_heatmap=FALSE,
cores=1){
num_clusters <- min(num_clusters, nrow(cds_subset))
pseudocount <- 1
newdata <- data.frame(Pseudotime = seq(min(pData(cds_subset)$Pseudotime), max(pData(cds_subset)$Pseudotime),length.out = 100))
m <- genSmoothCurves(cds_subset, cores=cores, trend_formula = trend_formula,
relative_expr = T, new_data = newdata)
#remove genes with no expression in any condition
m=m[!apply(m,1,sum)==0,]
norm_method <- match.arg(norm_method)
# FIXME: this needs to check that vst values can even be computed. (They can only be if we're using NB as the expressionFamily)
if(norm_method == 'vstExprs' && is.null(cds_subset@dispFitInfo[["blind"]]$disp_func) == FALSE) {
m = vstExprs(cds_subset, expr_matrix=m)
}
else if(norm_method == 'log') {
m = log10(m+pseudocount)
}
# Row-center the data.
m=m[!apply(m,1,sd)==0,]
m=Matrix::t(scale(Matrix::t(m),center=TRUE))
m=m[is.na(row.names(m)) == FALSE,]
m[is.nan(m)] = 0
m[m>scale_max] = scale_max
m[m<scale_min] = scale_min
heatmap_matrix <- m
row_dist <- as.dist((1 - cor(Matrix::t(heatmap_matrix)))/2)
row_dist[is.na(row_dist)] <- 1
if(is.null(hmcols)) {
bks <- seq(-3.1,3.1, by = 0.1)
hmcols <- blue2green2red(length(bks) - 1)
}
else {
bks <- seq(-3.1,3.1, length.out = length(hmcols))
}
ph <- pheatmap(heatmap_matrix,
useRaster = T,
cluster_cols=FALSE,
cluster_rows=cluster_rows,
show_rownames=F,
show_colnames=F,
clustering_distance_rows=row_dist,
clustering_method = hclust_method,
cutree_rows=num_clusters,
silent=TRUE,
filename=NA,
breaks=bks,
border_color = NA,
color=hmcols)
if(cluster_rows) {
annotation_row <- data.frame(Cluster=factor(cutree(ph$tree_row, num_clusters)))
} else {
annotation_row <- NULL
}
if(!is.null(add_annotation_row)) {
old_colnames_length <- ncol(annotation_row)
annotation_row <- cbind(annotation_row, add_annotation_row[row.names(annotation_row), ])
colnames(annotation_row)[(old_colnames_length+1):ncol(annotation_row)] <- colnames(add_annotation_row)
# annotation_row$bif_time <- add_annotation_row[as.character(fData(absolute_cds[row.names(annotation_row), ])$gene_short_name), 1]
}
if(!is.null(add_annotation_col)) {
if(nrow(add_annotation_col) != 100) {
stop('add_annotation_col should have only 100 rows (check genSmoothCurves before you supply the annotation data)!')
}
annotation_col <- add_annotation_col
} else {
annotation_col <- NA
}
if (use_gene_short_name == TRUE) {
if (is.null(fData(cds_subset)$gene_short_name) == FALSE) {
feature_label <- as.character(fData(cds_subset)[row.names(heatmap_matrix), 'gene_short_name'])
feature_label[is.na(feature_label)] <- row.names(heatmap_matrix)
row_ann_labels <- as.character(fData(cds_subset)[row.names(annotation_row), 'gene_short_name'])
row_ann_labels[is.na(row_ann_labels)] <- row.names(annotation_row)
}
else {
feature_label <- row.names(heatmap_matrix)
row_ann_labels <- row.names(annotation_row)
}
}
else {
feature_label <- row.names(heatmap_matrix)
if(!is.null(annotation_row))
row_ann_labels <- row.names(annotation_row)
}
row.names(heatmap_matrix) <- feature_label
if(!is.null(annotation_row))
row.names(annotation_row) <- row_ann_labels
colnames(heatmap_matrix) <- c(1:ncol(heatmap_matrix))
ph_res <- pheatmap(heatmap_matrix[, ], #ph$tree_row$order
useRaster = T,
cluster_cols = FALSE,
cluster_rows = cluster_rows,
show_rownames=show_rownames,
show_colnames=F,
#scale="row",
clustering_distance_rows=row_dist, #row_dist
clustering_method = hclust_method, #ward.D2
cutree_rows=num_clusters,
# cutree_cols = 2,
annotation_row=annotation_row,
annotation_col=annotation_col,
treeheight_row = 20,
breaks=bks,
fontsize = 6,
color=hmcols,
border_color = NA,
silent=TRUE,
filename=NA
)
grid::grid.rect(gp=grid::gpar("fill", col=NA))
grid::grid.draw(ph_res$gtable)
if (return_heatmap){
return(ph_res)
}
}
#' Plot the branch genes in pseduotime with separate branch curves.
#'
#' @description Works similarly to plot_genes_in_psuedotime esceptit shows
#' one kinetic trend for each lineage.
#'
#' @details This plotting function is used to make the branching plots for a branch dependent gene goes through the progenitor state
#' and bifurcating into two distinct branchs (Similar to the pitch-fork bifurcation in dynamic systems). In order to make the
#' bifurcation plot, we first duplicated the progenitor states and by default stretch each branch into maturation level 0-100.
#' Then we fit two nature spline curves for each branchs using VGAM package.
#'
#' @param cds CellDataSet for the experiment
#' @param branch_states The states for two branching branchs
#' @param branch_point The ID of the branch point to analyze. Can only be used when reduceDimension is called with method = "DDRTree".
#' @param branch_labels The names for each branching branch
#' @param method The method to draw the curve for the gene expression branching pattern, either loess ('loess') or VGLM fitting ('fitting')
#' @param min_expr The minimum (untransformed) expression level to use in plotted the genes.
#' @param cell_size The size (in points) of each cell used in the plot
#' @param nrow Number of columns used to layout the faceted cluster panels
#' @param ncol Number of columns used to layout the faceted cluster panels
#' @param panel_order The a character vector of gene short names (or IDs, if that's what you're using), specifying order in which genes should be layed out (left-to-right, top-to-bottom)
#' @param color_by The cell attribute (e.g. the column of pData(cds)) to be used to color each cell
#' @param expression_curve_linetype_by The cell attribute (e.g. the column of pData(cds)) to be used for the linetype of each branch curve
#' @param trend_formula The model formula to be used for fitting the expression trend over pseudotime
#' @param reducedModelFormulaStr A formula specifying a null model. If used, the plot shows a p value from the likelihood ratio test that uses trend_formula as the full model
#' @param label_by_short_name Whether to label figure panels by gene_short_name (TRUE) or feature id (FALSE)
#' @param relative_expr Whether or not the plot should use relative expression values (only relevant for CellDataSets using transcript counts)
#' @param ... Additional arguments passed on to branchTest. Only used when reducedModelFormulaStr is not NULL.
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom plyr ddply
#' @importFrom reshape2 melt
#' @importFrom BiocGenerics sizeFactors
#' @export
plot_genes_branched_pseudotime <- function (cds,
branch_states = NULL,
branch_point=1,
branch_labels = NULL,
method = "fitting",
min_expr = NULL,
cell_size = 0.75,
nrow = NULL,
ncol = 1,
panel_order = NULL,
color_by = "State",
expression_curve_linetype_by = "Branch",
trend_formula = "~ sm.ns(Pseudotime, df=3) * Branch",
reducedModelFormulaStr = NULL,
label_by_short_name = TRUE,
relative_expr = TRUE,
#gene_pairs = NULL,
...)
{
Branch <- NA
if (is.null(reducedModelFormulaStr) == FALSE) {
pval_df <- branchTest(cds,
branch_states=branch_states,
branch_point=branch_point,
fullModelFormulaStr = trend_formula,
reducedModelFormulaStr = "~ sm.ns(Pseudotime, df=3)",
...)
fData(cds)[, "pval"] <- pval_df[row.names(cds), 'pval']
}
if("Branch" %in% all.vars(terms(as.formula(trend_formula)))) { #only when Branch is in the model formula we will duplicate the "progenitor" cells
cds_subset <- buildBranchCellDataSet(cds = cds,
branch_states = branch_states,
branch_point=branch_point,
branch_labels = branch_labels,
progenitor_method = 'duplicate',
...)
}
else {
cds_subset <- cds
pData(cds_subset)$Branch <- pData(cds_subset)$State
}
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")) {
integer_expression <- TRUE
}
else {
integer_expression <- FALSE
}
if (integer_expression) {
CM <- exprs(cds_subset)
if (relative_expr){
if (is.null(sizeFactors(cds_subset))) {
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
CM <- Matrix::t(Matrix::t(CM)/sizeFactors(cds_subset))
}
cds_exprs <- reshape2::melt(round(as.matrix(CM)))
}
else {
cds_exprs <- reshape2::melt(exprs(cds_subset))
}
if (is.null(min_expr)) {
min_expr <- cds_subset@lowerDetectionLimit
}
colnames(cds_exprs) <- c("f_id", "Cell", "expression")
cds_pData <- pData(cds_subset)
cds_fData <- fData(cds_subset)
cds_exprs <- merge(cds_exprs, cds_fData, by.x = "f_id", by.y = "row.names")
cds_exprs <- merge(cds_exprs, cds_pData, by.x = "Cell", by.y = "row.names")
if (integer_expression) {
cds_exprs$adjusted_expression <- round(cds_exprs$expression)
}
else {
cds_exprs$adjusted_expression <- log10(cds_exprs$expression)
}
if (label_by_short_name == TRUE) {
if (is.null(cds_exprs$gene_short_name) == FALSE) {
cds_exprs$feature_label <- as.character(cds_exprs$gene_short_name)
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
cds_exprs$feature_label <- as.factor(cds_exprs$feature_label)
# trend_formula <- paste("adjusted_expression", trend_formula,
# sep = "")
cds_exprs$Branch <- as.factor(cds_exprs$Branch)
new_data <- data.frame(Pseudotime = pData(cds_subset)$Pseudotime, Branch = pData(cds_subset)$Branch)
full_model_expectation <- genSmoothCurves(cds_subset, cores=1, trend_formula = trend_formula,
relative_expr = T, new_data = new_data)
colnames(full_model_expectation) <- colnames(cds_subset)
cds_exprs$full_model_expectation <- apply(cds_exprs,1, function(x) full_model_expectation[x[2], x[1]])
if(!is.null(reducedModelFormulaStr)){
reduced_model_expectation <- genSmoothCurves(cds_subset, cores=1, trend_formula = reducedModelFormulaStr,
relative_expr = T, new_data = new_data)
colnames(reduced_model_expectation) <- colnames(cds_subset)
cds_exprs$reduced_model_expectation <- apply(cds_exprs,1, function(x) reduced_model_expectation[x[2], x[1]])
}
# FIXME: If you want to show the bifurcation time for each gene, this function
# should just compute it. Passing it in as a dataframe is just too complicated
# and will be hard on the user.
# if(!is.null(bifurcation_time)){
# cds_exprs$bifurcation_time <- bifurcation_time[as.vector(cds_exprs$gene_short_name)]
# }
if (method == "loess")
cds_exprs$expression <- cds_exprs$expression + cds@lowerDetectionLimit
if (label_by_short_name == TRUE) {
if (is.null(cds_exprs$gene_short_name) == FALSE) {
cds_exprs$feature_label <- as.character(cds_exprs$gene_short_name)
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
cds_exprs$feature_label <- factor(cds_exprs$feature_label)
if (is.null(panel_order) == FALSE) {
cds_exprs$feature_label <- factor(cds_exprs$feature_label,
levels = panel_order)
}
cds_exprs$expression[is.na(cds_exprs$expression)] <- min_expr
cds_exprs$expression[cds_exprs$expression < min_expr] <- min_expr
cds_exprs$full_model_expectation[is.na(cds_exprs$full_model_expectation)] <- min_expr
cds_exprs$full_model_expectation[cds_exprs$full_model_expectation < min_expr] <- min_expr
if(!is.null(reducedModelFormulaStr)){
cds_exprs$reduced_model_expectation[is.na(cds_exprs$reduced_model_expectation)] <- min_expr
cds_exprs$reduced_model_expectation[cds_exprs$reduced_model_expectation < min_expr] <- min_expr
}
cds_exprs$State <- as.factor(cds_exprs$State)
cds_exprs$Branch <- as.factor(cds_exprs$Branch)
q <- ggplot(aes(Pseudotime, expression), data = cds_exprs)
# if (!is.null(bifurcation_time)) {
# q <- q + geom_vline(aes(xintercept = bifurcation_time),
# color = "black", linetype = "longdash")
# }
if (is.null(color_by) == FALSE) {
q <- q + geom_point(aes_string(color = color_by), size = I(cell_size))
}
if (is.null(reducedModelFormulaStr) == FALSE)
q <- q + scale_y_log10() + facet_wrap(~feature_label +
pval, nrow = nrow, ncol = ncol, scales = "free_y")
else q <- q + scale_y_log10() + facet_wrap(~feature_label,
nrow = nrow, ncol = ncol, scales = "free_y")
if (method == "loess")
q <- q + stat_smooth(aes(fill = Branch, color = Branch),
method = "loess")
else if (method == "fitting") {
q <- q + geom_line(aes_string(x = "Pseudotime", y = "full_model_expectation",
linetype = "Branch"), data = cds_exprs) #+ scale_color_manual(name = "Type", values = c(colour_cell, colour), labels = c("Pre-branch", "AT1", "AT2", "AT1", "AT2")
}
if(!is.null(reducedModelFormulaStr)) {
q <- q + geom_line(aes_string(x = "Pseudotime", y = "reduced_model_expectation"),
color = 'black', linetype = 2, data = cds_exprs)
}
q <- q + ylab("Expression") + xlab("Pseudotime (stretched)")
q <- q + monocle_theme_opts()
q + expand_limits(y = min_expr)
}
#' Not sure we're ready to release this one quite yet:
#' Plot the branch genes in pseduotime with separate branch curves
#' @param cds CellDataSet for the experiment
#' @param rowgenes Gene ids or short names to be arrayed on the vertical axis.
#' @param colgenes Gene ids or short names to be arrayed on the horizontal axis
#' @param relative_expr Whether to transform expression into relative values
#' @param min_expr The minimum level of expression to show in the plot
#' @param cell_size A number how large the cells should be in the plot
#' @param label_by_short_name a boolean that indicates whether cells should be labeled by their short name
#' @param show_density a boolean that indicates whether a 2D density estimation should be shown in the plot
#' @param round_expr a boolean that indicates whether cds_expr values should be rounded or not
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
plot_coexpression_matrix <- function(cds,
rowgenes,
colgenes,
relative_expr=TRUE,
min_expr=NULL,
cell_size=0.85,
label_by_short_name=TRUE,
show_density=TRUE,
round_expr=FALSE){
gene_short_name <- NA
f_id <- NA
adjusted_expression.x <- NULL
adjusted_expression.y <- NULL
..density.. <- NULL
row_gene_ids <- row.names(subset(fData(cds), gene_short_name %in% rowgenes))
row_gene_ids <- union(row_gene_ids, intersect(rowgenes, row.names(fData(cds))))
col_gene_ids <- row.names(subset(fData(cds), gene_short_name %in% colgenes))
col_gene_ids <- union(col_gene_ids, intersect(colgenes, row.names(fData(cds))))
cds_subset <- cds[union(row_gene_ids, col_gene_ids),]
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")){
integer_expression <- TRUE
}else{
integer_expression <- FALSE
relative_expr <- TRUE
}
if (integer_expression)
{
cds_exprs <- exprs(cds_subset)
if (relative_expr){
if (is.null(sizeFactors(cds_subset)))
{
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
cds_exprs <- Matrix::t(Matrix::t(cds_exprs) / sizeFactors(cds_subset))
}
if (round_expr){
cds_exprs <- reshape2::melt(round(as.matrix(cds_exprs)))
} else {
cds_exprs <- reshape2::melt(as.matrix(cds_exprs))
}
}else{
cds_exprs <- reshape2::melt(exprs(cds_subset))
}
if (is.null(min_expr)){
min_expr <- cds_subset@lowerDetectionLimit
}
colnames(cds_exprs) <- c("f_id", "Cell", "expression")
cds_exprs$expression[cds_exprs$expression < min_expr] <- min_expr
cds_pData <- pData(cds_subset)
cds_fData <- fData(cds_subset)
cds_exprs <- merge(cds_exprs, cds_fData, by.x="f_id", by.y="row.names")
cds_exprs$adjusted_expression <- cds_exprs$expression
#cds_exprs$adjusted_expression <- log10(cds_exprs$adjusted_expression + abs(rnorm(nrow(cds_exprs), min_expr, sqrt(min_expr))))
if (label_by_short_name == TRUE){
if (is.null(cds_exprs$gene_short_name) == FALSE){
cds_exprs$feature_label <- as.character(cds_exprs$gene_short_name)
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
}else{
cds_exprs$feature_label <- cds_exprs$f_id
}
}else{
cds_exprs$feature_label <- cds_exprs$f_id
}
cds_exprs$feature_label <- factor(cds_exprs$feature_label)
row_cds_exprs <- subset(cds_exprs, f_id %in% row_gene_ids)
col_cds_exprs <- subset(cds_exprs, f_id %in% col_gene_ids)
joined_exprs <- merge(row_cds_exprs, col_cds_exprs, by="Cell")
cds_exprs <- joined_exprs
cds_exprs <- merge(cds_exprs, cds_pData, by.x="Cell", by.y="row.names")
cds_exprs <- subset(cds_exprs, adjusted_expression.x > min_expr | adjusted_expression.y > min_expr)
q <- ggplot(aes(adjusted_expression.x, adjusted_expression.y), data=cds_exprs, size=I(1))
if (show_density){
q <- q + stat_density2d(geom="raster", aes(fill = ..density..), contour = FALSE) +
scale_fill_gradient(low="white", high="red")
}
q <- q + scale_x_log10() + scale_y_log10() +
geom_point(color=I("black"), size=I(cell_size * 1.50)) +
geom_point(color=I("white"), size=I(cell_size)) +
facet_grid(feature_label.x ~ feature_label.y, scales="free")
#scale_color_brewer(palette="Set1") +
if (min_expr < 1)
{
q <- q + expand_limits(y=c(min_expr, 1), x=c(min_expr, 1))
}
#q <- q + monocle_theme_opts()
q
}
#The following code is swipped from colorRamps package which is used to make the pallette
table.ramp <- function(n, mid = 0.5, sill = 0.5, base = 1, height = 1)
{
x <- seq(0, 1, length.out = n)
y <- rep(0, length(x))
sill.min <- max(c(1, round((n - 1) * (mid - sill / 2)) + 1))
sill.max <- min(c(n, round((n - 1) * (mid + sill / 2)) + 1))
y[sill.min:sill.max] <- 1
base.min <- round((n - 1) * (mid - base / 2)) + 1
base.max <- round((n - 1) * (mid + base / 2)) + 1
xi <- base.min:sill.min
yi <- seq(0, 1, length.out = length(xi))
i <- which(xi > 0 & xi <= n)
y[xi[i]] <- yi[i]
xi <- sill.max:base.max
yi <- seq(1, 0, length.out = length(xi))
i <- which(xi > 0 & xi <= n)
y[xi[i]] <- yi[i]
height * y
}
#' @importFrom grDevices rgb
rgb.tables <- function(n,
red = c(0.75, 0.25, 1),
green = c(0.5, 0.25, 1),
blue = c(0.25, 0.25, 1))
{
rr <- do.call("table.ramp", as.list(c(n, red)))
gr <- do.call("table.ramp", as.list(c(n, green)))
br <- do.call("table.ramp", as.list(c(n, blue)))
rgb(rr, gr, br)
}
matlab.like <- function(n) rgb.tables(n)
matlab.like2 <- function(n)
rgb.tables(n,
red = c(0.8, 0.2, 1),
green = c(0.5, 0.4, 0.8),
blue = c(0.2, 0.2, 1))
blue2green2red <- matlab.like2
#' Create a heatmap to demonstrate the bifurcation of gene expression along two branchs
#'
#' @description returns a heatmap that shows changes in both lineages at the same time.
#' It also requires that you choose a branch point to inspect.
#' Columns are points in pseudotime, rows are genes, and the beginning of pseudotime is in the middle of the heatmap.
#' As you read from the middle of the heatmap to the right, you are following one lineage through pseudotime. As you read left, the other.
#' The genes are clustered hierarchically, so you can visualize modules of genes that have similar lineage-dependent expression patterns.
#'
#' @param cds_subset CellDataSet for the experiment (normally only the branching genes detected with branchTest)
#' @param branch_point The ID of the branch point to visualize. Can only be used when reduceDimension is called with method = "DDRTree".
#' @param branch_states The two states to compare in the heatmap. Mutually exclusive with branch_point.
#' @param branch_labels The labels for the branchs.
#' @param cluster_rows Whether to cluster the rows of the heatmap.
#' @param hclust_method The method used by pheatmap to perform hirearchical clustering of the rows.
#' @param num_clusters Number of clusters for the heatmap of branch genes
#' @param hmcols The color scheme for drawing the heatmap.
#' @param branch_colors The colors used in the annotation strip indicating the pre- and post-branch cells.
#' @param add_annotation_row Additional annotations to show for each row in the heatmap. Must be a dataframe with one row for each row in the fData table of cds_subset, with matching IDs.
#' @param add_annotation_col Additional annotations to show for each column in the heatmap. Must be a dataframe with one row for each cell in the pData table of cds_subset, with matching IDs.
#' @param show_rownames Whether to show the names for each row in the table.
#' @param use_gene_short_name Whether to use the short names for each row. If FALSE, uses row IDs from the fData table.
#' @param scale_max The maximum value (in standard deviations) to show in the heatmap. Values larger than this are set to the max.
#' @param scale_min The minimum value (in standard deviations) to show in the heatmap. Values smaller than this are set to the min.
#' @param norm_method Determines how to transform expression values prior to rendering
#' @param trend_formula A formula string specifying the model used in fitting the spline curve for each gene/feature.
#' @param return_heatmap Whether to return the pheatmap object to the user.
#' @param cores Number of cores to use when smoothing the expression curves shown in the heatmap.
#' @param ... Additional arguments passed to buildBranchCellDataSet
#' @return A list of heatmap_matrix (expression matrix for the branch committment), ph (pheatmap heatmap object),
#' annotation_row (annotation data.frame for the row), annotation_col (annotation data.frame for the column).
#' @import pheatmap
#' @importFrom stats sd as.dist cor cutree
#' @export
#'
plot_genes_branched_heatmap <- function(cds_subset,
branch_point=1,
branch_states=NULL,
branch_labels = c("Cell fate 1", "Cell fate 2"),
cluster_rows = TRUE,
hclust_method = "ward.D2",
num_clusters = 6,
hmcols = NULL,
branch_colors = c('#979797', '#F05662', '#7990C8'),
add_annotation_row = NULL,
add_annotation_col = NULL,
show_rownames = FALSE,
use_gene_short_name = TRUE,
scale_max=3,
scale_min=-3,
norm_method = c("log", "vstExprs"),
trend_formula = '~sm.ns(Pseudotime, df=3) * Branch',
return_heatmap=FALSE,
cores = 1, ...) {
cds <- NA
new_cds <- buildBranchCellDataSet(cds_subset,
branch_states=branch_states,
branch_point=branch_point,
progenitor_method = 'duplicate',
...)
new_cds@dispFitInfo <- cds_subset@dispFitInfo
if(is.null(branch_states)) {
progenitor_state <- subset(pData(cds_subset), Pseudotime == 0)[, 'State']
branch_states <- setdiff(pData(cds_subset)$State, progenitor_state)
}
col_gap_ind <- 101
# newdataA <- data.frame(Pseudotime = seq(0, 100, length.out = 100))
# newdataB <- data.frame(Pseudotime = seq(0, 100, length.out = 100))
newdataA <- data.frame(Pseudotime = seq(0, 100,
length.out = 100), Branch = as.factor(unique(as.character(pData(new_cds)$Branch))[1]))
newdataB <- data.frame(Pseudotime = seq(0, 100,
length.out = 100), Branch = as.factor(unique(as.character(pData(new_cds)$Branch))[2]))
BranchAB_exprs <- genSmoothCurves(new_cds[, ], cores=cores, trend_formula = trend_formula,
relative_expr = T, new_data = rbind(newdataA, newdataB))
BranchA_exprs <- BranchAB_exprs[, 1:100]
BranchB_exprs <- BranchAB_exprs[, 101:200]
#common_ancestor_cells <- row.names(pData(new_cds)[duplicated(pData(new_cds)$original_cell_id),])
common_ancestor_cells <- row.names(pData(new_cds)[pData(new_cds)$State == setdiff(pData(new_cds)$State, branch_states),])
BranchP_num <- (100 - floor(max(pData(new_cds)[common_ancestor_cells, 'Pseudotime'])))
BranchA_num <- floor(max(pData(new_cds)[common_ancestor_cells, 'Pseudotime']))
BranchB_num <- BranchA_num
norm_method <- match.arg(norm_method)
# FIXME: this needs to check that vst values can even be computed. (They can only be if we're using NB as the expressionFamily)
if(norm_method == 'vstExprs') {
BranchA_exprs <- vstExprs(new_cds, expr_matrix=BranchA_exprs)
BranchB_exprs <- vstExprs(new_cds, expr_matrix=BranchB_exprs)
}
else if(norm_method == 'log') {
BranchA_exprs <- log10(BranchA_exprs + 1)
BranchB_exprs <- log10(BranchB_exprs + 1)
}
heatmap_matrix <- cbind(BranchA_exprs[, (col_gap_ind - 1):1], BranchB_exprs)
heatmap_matrix=heatmap_matrix[!apply(heatmap_matrix, 1, sd)==0,]
heatmap_matrix=Matrix::t(scale(Matrix::t(heatmap_matrix),center=TRUE))
heatmap_matrix=heatmap_matrix[is.na(row.names(heatmap_matrix)) == FALSE,]
heatmap_matrix[is.nan(heatmap_matrix)] = 0
heatmap_matrix[heatmap_matrix>scale_max] = scale_max
heatmap_matrix[heatmap_matrix<scale_min] = scale_min
heatmap_matrix_ori <- heatmap_matrix
heatmap_matrix <- heatmap_matrix[is.finite(heatmap_matrix[, 1]) & is.finite(heatmap_matrix[, col_gap_ind]), ] #remove the NA fitting failure genes for each branch
row_dist <- as.dist((1 - cor(Matrix::t(heatmap_matrix)))/2)
row_dist[is.na(row_dist)] <- 1
exp_rng <- range(heatmap_matrix) #bks is based on the expression range
bks <- seq(exp_rng[1] - 0.1, exp_rng[2] + 0.1, by=0.1)
if(is.null(hmcols)) {
hmcols <- blue2green2red(length(bks) - 1)
}
# prin t(hmcols)
ph <- pheatmap(heatmap_matrix,
useRaster = T,
cluster_cols=FALSE,
cluster_rows=TRUE,
show_rownames=F,
show_colnames=F,
#scale="row",
clustering_distance_rows=row_dist,
clustering_method = hclust_method,
cutree_rows=num_clusters,
silent=TRUE,
filename=NA,
breaks=bks,
color=hmcols
#color=hmcols#,
# filename="expression_pseudotime_pheatmap.pdf",
)
#save(heatmap_matrix, row_dist, num_clusters, hmcols, ph, branchTest_df, qval_lowest_thrsd, branch_labels, BranchA_num, BranchP_num, BranchB_num, file = 'heatmap_matrix')
annotation_row <- data.frame(Cluster=factor(cutree(ph$tree_row, num_clusters)))
if(!is.null(add_annotation_row)) {
annotation_row <- cbind(annotation_row, add_annotation_row[row.names(annotation_row), ])
# annotation_row$bif_time <- add_annotation_row[as.character(fData(absolute_cds[row.names(annotation_row), ])$gene_short_name), 1]
}
colnames(heatmap_matrix) <- c(1:ncol(heatmap_matrix))
annotation_col <- data.frame(row.names = c(1:ncol(heatmap_matrix)), "Cell Type" = c(rep(branch_labels[1], BranchA_num),
rep("Pre-branch", 2 * BranchP_num),
rep(branch_labels[2], BranchB_num)))
colnames(annotation_col) <- "Cell Type"
if(!is.null(add_annotation_col)) {
annotation_col <- cbind(annotation_col, add_annotation_col[fData(cds[row.names(annotation_col), ])$gene_short_name, 1])
}
names(branch_colors) <- c("Pre-branch", branch_labels[1], branch_labels[2])
annotation_colors=list("Cell Type"=branch_colors)
names(annotation_colors$`Cell Type`) = c('Pre-branch', branch_labels)
if (use_gene_short_name == TRUE) {
if (is.null(fData(cds_subset)$gene_short_name) == FALSE) {
feature_label <- as.character(fData(cds_subset)[row.names(heatmap_matrix), 'gene_short_name'])
feature_label[is.na(feature_label)] <- row.names(heatmap_matrix)
row_ann_labels <- as.character(fData(cds_subset)[row.names(annotation_row), 'gene_short_name'])
row_ann_labels[is.na(row_ann_labels)] <- row.names(annotation_row)
}
else {
feature_label <- row.names(heatmap_matrix)
row_ann_labels <- row.names(annotation_row)
}
}
else {
feature_label <- row.names(heatmap_matrix)
row_ann_labels <- row.names(annotation_row)
}
row.names(heatmap_matrix) <- feature_label
row.names(annotation_row) <- row_ann_labels
ph_res <- pheatmap(heatmap_matrix[, ], #ph$tree_row$order
useRaster = T,
cluster_cols=FALSE,
cluster_rows=TRUE,
show_rownames=show_rownames,
show_colnames=F,
#scale="row",
clustering_distance_rows=row_dist, #row_dist
clustering_method = hclust_method, #ward.D2
cutree_rows=num_clusters,
# cutree_cols = 2,
annotation_row=annotation_row,
annotation_col=annotation_col,
annotation_colors=annotation_colors,
gaps_col = col_gap_ind,
treeheight_row = 20,
breaks=bks,
fontsize = 6,
color=hmcols,
border_color = NA,
silent=TRUE)
grid::grid.rect(gp=grid::gpar("fill", col=NA))
grid::grid.draw(ph_res$gtable)
if (return_heatmap){
return(list(BranchA_exprs = BranchA_exprs, BranchB_exprs = BranchB_exprs, heatmap_matrix = heatmap_matrix,
heatmap_matrix_ori = heatmap_matrix_ori, ph = ph, col_gap_ind = col_gap_ind, row_dist = row_dist, hmcols = hmcols,
annotation_colors = annotation_colors, annotation_row = annotation_row, annotation_col = annotation_col,
ph_res = ph_res))
}
}
#' Plots genes by mean vs. dispersion, highlighting those selected for ordering
#'
#' Each gray point in the plot is a gene. The black dots are those that were included
#' in the last call to setOrderingFilter. The red curve shows the mean-variance
#' model learning by estimateDispersions().
#'
#' @param cds The CellDataSet to be used for the plot.
#' @export
plot_ordering_genes <- function(cds){
if(class(cds)[1] != "CellDataSet") {
stop("Error input object is not of type 'CellDataSet'")
}
disp_table <- dispersionTable(cds)
use_for_ordering <- NA
mean_expression <- NA
dispersion_empirical <- NA
dispersion_fit <- NA
gene_id <- NA
ordering_genes <- row.names(subset(fData(cds), use_for_ordering == TRUE))
g <- qplot(mean_expression, dispersion_empirical, data=disp_table, log="xy", color=I("darkgrey")) +
geom_line(aes(y=dispersion_fit), color="red")
if (length(ordering_genes) > 0){
g <- g + geom_point(aes(mean_expression, dispersion_empirical),
data=subset(disp_table, gene_id %in% ordering_genes), color="black")
}
g <- g + monocle_theme_opts()
g
}
#' Plots clusters of cells .
#'
#' @param cds CellDataSet for the experiment
#' @param x the column of reducedDimS(cds) to plot on the horizontal axis
#' @param y the column of reducedDimS(cds) to plot on the vertical axis
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to map to each cell's color
#' @param markers a gene name or gene id to use for setting the size of each cell in the plot
#' @param show_cell_names draw the name of each cell in the plot
#' @param cell_size The size of the point for each cell
#' @param cell_name_size the size of cell name labels
#' @param ... additional arguments passed into the scale_color_viridis function
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
#' @importFrom viridis scale_color_viridis
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' HSMM <- reduceD
#' plot_cell_clusters(HSMM)
#' plot_cell_clusters(HSMM, color_by="Pseudotime")
#' plot_cell_clusters(HSMM, markers="MYH3")
#' }
plot_cell_clusters <- function(cds,
x=1,
y=2,
color_by="Cluster",
markers=NULL,
show_cell_names=FALSE,
cell_size=1.5,
cell_name_size=2,
...){
if (is.null(cds@reducedDimA) | length(pData(cds)$Cluster) == 0){
stop("Error: Clustering is not performed yet. Please call clusterCells() before calling this function.")
}
gene_short_name <- NULL
sample_name <- NULL
data_dim_1 <- NULL
data_dim_2 <- NULL
#TODO: need to validate cds as ready for this plot (need mst, pseudotime, etc)
lib_info <- pData(cds)
tSNE_dim_coords <- reducedDimA(cds)
data_df <- data.frame(t(tSNE_dim_coords[c(x,y),]))
colnames(data_df) <- c("data_dim_1", "data_dim_2")
data_df$sample_name <- colnames(cds)
data_df <- merge(data_df, lib_info, by.x="sample_name", by.y="row.names")
markers_exprs <- NULL
if (is.null(markers) == FALSE){
markers_fData <- subset(fData(cds), gene_short_name %in% markers)
if (nrow(markers_fData) >= 1){
cds_subset <- cds[row.names(markers_fData),]
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")) {
integer_expression <- TRUE
}
else {
integer_expression <- FALSE
}
if (integer_expression) {
cds_exprs <- exprs(cds_subset)
if (is.null(sizeFactors(cds_subset))) {
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
cds_exprs <- Matrix::t(Matrix::t(cds_exprs)/sizeFactors(cds_subset))
cds_exprs <- reshape2::melt(round(as.matrix(cds_exprs)))
}
else {
cds_exprs <- reshape2::melt(as.matrix(exprs(cds_subset)))
}
markers_exprs <- cds_exprs
#markers_exprs <- reshape2::melt(as.matrix(cds_exprs))
colnames(markers_exprs)[1:2] <- c('feature_id','cell_id')
markers_exprs <- merge(markers_exprs, markers_fData, by.x = "feature_id", by.y="row.names")
#print (head( markers_exprs[is.na(markers_exprs$gene_short_name) == FALSE,]))
markers_exprs$feature_label <- as.character(markers_exprs$gene_short_name)
markers_exprs$feature_label[is.na(markers_exprs$feature_label)] <- markers_exprs$Var1
}
}
if (is.null(markers_exprs) == FALSE && nrow(markers_exprs) > 0){
data_df <- merge(data_df, markers_exprs, by.x="sample_name", by.y="cell_id")
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2)) + facet_wrap(~feature_label)
}else{
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2))
}
# FIXME: setting size here overrides the marker expression funtionality.
# Don't do it!
if (is.null(markers_exprs) == FALSE && nrow(markers_exprs) > 0){
g <- g + geom_point(aes(color=log10(value + 0.1)), size=I(cell_size), na.rm = TRUE) +
scale_color_viridis(name = paste0("log10(value + 0.1)"), ...)
}else {
g <- g + geom_point(aes_string(color = color_by), size=I(cell_size), na.rm = TRUE)
}
g <- g +
#scale_color_brewer(palette="Set1") +
monocle_theme_opts() +
xlab(paste("Component", x)) +
ylab(paste("Component", y)) +
theme(legend.position="top", legend.key.height=grid::unit(0.35, "in")) +
#guides(color = guide_legend(label.position = "top")) +
theme(legend.key = element_blank()) +
theme(panel.background = element_rect(fill='white')) +
theme(text = element_text(size = 15))
g
}
#' Plots the decision map of density clusters .
#'
#' @param cds CellDataSet for the experiment after running clusterCells_Density_Peak
#' @param rho_threshold The threshold of local density (rho) used to select the density peaks for plotting
#' @param delta_threshold The threshold of local distance (delta) used to select the density peaks for plotting
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' plot_rho_delta(HSMM)
#' }
plot_rho_delta <- function(cds, rho_threshold = NULL, delta_threshold = NULL){
if(!is.null(cds@auxClusteringData[["tSNE"]]$densityPeak)
& !is.null(pData(cds)$Cluster)
& !is.null(pData(cds)$peaks)
& !is.null(pData(cds)$halo)
& !is.null(pData(cds)$delta)
& !is.null(pData(cds)$rho)) {
rho <- NULL
delta <- NULL
# df <- data.frame(rho = as.numeric(levels(pData(cds)$rho))[pData(cds)$rho],
# delta = as.numeric(levels(pData(cds)$delta))[pData(cds)$delta])
if(!is.null(rho_threshold) & !is.null(delta_threshold)){
peaks <- pData(cds)$rho >= rho_threshold & pData(cds)$delta >= delta_threshold
}
else
peaks <- pData(cds)$peaks
df <- data.frame(rho = pData(cds)$rho, delta = pData(cds)$delta, peaks = peaks)
g <- qplot(rho, delta, data = df, alpha = I(0.5), color = peaks) + monocle_theme_opts() +
theme(legend.position="top", legend.key.height=grid::unit(0.35, "in")) +
scale_color_manual(values=c("grey","black")) +
theme(legend.key = element_blank()) +
theme(panel.background = element_rect(fill='white'))
}
else {
stop('Please run clusterCells_Density_Peak before using this plotting function')
}
g
}
#' Plots the percentage of variance explained by the each component based on PCA from the normalized expression
#' data using the same procedure used in reduceDimension function.
#'
#' @param cds CellDataSet for the experiment after running reduceDimension with reduction_method as tSNE
#' @param max_components Maximum number of components shown in the scree plot (variance explained by each component)
#' @param norm_method Determines how to transform expression values prior to reducing dimensionality
#' @param residualModelFormulaStr A model formula specifying the effects to subtract from the data before clustering.
#' @param pseudo_expr amount to increase expression values before dimensionality reduction
#' @param return_all A logical argument to determine whether or not the variance of each component is returned
#' @param use_existing_pc_variance Whether to plot existing results for variance explained by each PC
#' @param verbose Whether to emit verbose output during dimensionality reduction
#' @param ... additional arguments to pass to the dimensionality reduction function
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' plot_pc_variance_explained(HSMM)
#' }
plot_pc_variance_explained <- function(cds,
max_components=100,
# reduction_method=c("DDRTree", "ICA", 'tSNE'),
norm_method = c("log", "vstExprs", "none"),
residualModelFormulaStr=NULL,
pseudo_expr=NULL,
return_all = F,
use_existing_pc_variance=FALSE,
verbose=FALSE,
...){
set.seed(2016)
if(!is.null(cds@auxClusteringData[["tSNE"]]$variance_explained) & use_existing_pc_variance == T){
prop_varex <- cds@auxClusteringData[["tSNE"]]$variance_explained
}
else{
FM <- normalize_expr_data(cds, norm_method, pseudo_expr)
#FM <- FM[unlist(sparseApply(FM, 1, sd, convert_to_dense=TRUE)) > 0, ]
xm <- Matrix::rowMeans(FM)
xsd <- sqrt(Matrix::rowMeans((FM - xm)^2))
FM <- FM[xsd > 0,]
if (is.null(residualModelFormulaStr) == FALSE) {
if (verbose)
message("Removing batch effects")
X.model_mat <- sparse.model.matrix(as.formula(residualModelFormulaStr),
data = pData(cds), drop.unused.levels = TRUE)
fit <- limma::lmFit(FM, X.model_mat, ...)
beta <- fit$coefficients[, -1, drop = FALSE]
beta[is.na(beta)] <- 0
FM <- as.matrix(FM) - beta %*% t(X.model_mat[, -1])
}else{
X.model_mat <- NULL
}
if (nrow(FM) == 0) {
stop("Error: all rows have standard deviation zero")
}
# FM <- convert2DRData(cds, norm_method = 'log')
# FM <- FM[rowSums(is.na(FM)) == 0, ]
irlba_res <- prcomp_irlba(t(FM), n = min(max_components, min(dim(FM)) - 1),
center = TRUE, scale. = TRUE)
prop_varex <- irlba_res$sdev^2 / sum(irlba_res$sdev^2)
#
# cell_means <- Matrix::rowMeans(FM_t)
# cell_vars <- Matrix::rowMeans((FM_t - cell_means)^2)
#
# irlba_res <- irlba(FM,
# nv= min(max_components, min(dim(FM)) - 1), #avoid calculating components in the tail
# nu=0,
# center=cell_means,
# scale=sqrt(cell_vars),
# right_only=TRUE)
# prop_varex <- irlba_res$d / sum(irlba_res$d)
#
# # pca_res <- prcomp(t(FM), center = T, scale = T)
# # std_dev <- pca_res$sdev
# # pr_var <- std_dev^2
# prop_varex <- pr_var/sum(pr_var)
}
p <- qplot(1:length(prop_varex), prop_varex, alpha = I(0.5)) + monocle_theme_opts() +
theme(legend.position="top", legend.key.height=grid::unit(0.35, "in")) +
theme(panel.background = element_rect(fill='white')) + xlab('components') +
ylab('Variance explained \n by each component')
cds@auxClusteringData[["tSNE"]]$variance_explained <- prop_varex # update CDS slot for variance_explained
if(return_all) {
return(list(variance_explained = prop_varex, p = p))
}
else
return(p)
}
#' @importFrom igraph shortest_paths degree shortest.paths
traverseTree <- function(g, starting_cell, end_cells){
distance <- shortest.paths(g, v=starting_cell, to=end_cells)
branchPoints <- which(degree(g) == 3)
path <- shortest_paths(g, from = starting_cell, end_cells)
return(list(shortest_path = path$vpath, distance = distance, branch_points = intersect(branchPoints, unlist(path$vpath))))
}
#' Plots the minimum spanning tree on cells.
#' @description Plots the minimum spanning tree on cells.
#' @param cds CellDataSet for the experiment
#' @param x the column of reducedDimS(cds) to plot on the horizontal axis
#' @param y the column of reducedDimS(cds) to plot on the vertical axis
#' @param root_states the state used to set as the root of the graph
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to map to each cell's color
#' @param show_tree whether to show the links between cells connected in the minimum spanning tree
#' @param show_backbone whether to show the diameter path of the MST used to order the cells
#' @param backbone_color the color used to render the backbone.
#' @param markers a gene name or gene id to use for setting the size of each cell in the plot
#' @param show_cell_names draw the name of each cell in the plot
#' @param cell_size The size of the point for each cell
#' @param cell_link_size The size of the line segments connecting cells (when used with ICA) or the principal graph (when used with DDRTree)
#' @param cell_name_size the size of cell name labels
#' @param show_branch_points Whether to show icons for each branch point (only available when reduceDimension was called with DDRTree)
#' @param ... Additional arguments passed to the scale_color_viridis function
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom igraph V get.edgelist layout_as_tree
#' @importFrom reshape2 melt
#' @importFrom viridis scale_color_viridis
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' plot_complex_cell_trajectory(HSMM)
#' plot_complex_cell_trajectory(HSMM, color_by="Pseudotime", show_backbone=FALSE)
#' plot_complex_cell_trajectory(HSMM, markers="MYH3")
#' }
plot_complex_cell_trajectory <- function(cds,
x=1,
y=2,
root_states = NULL,
color_by="State",
show_tree=TRUE,
show_backbone=TRUE,
backbone_color="black",
markers=NULL,
show_cell_names=FALSE,
cell_size=1.5,
cell_link_size=0.75,
cell_name_size=2,
show_branch_points=TRUE,
...){
gene_short_name <- NA
sample_name <- NA
data_dim_1 <- NA
data_dim_2 <- NA
#TODO: need to validate cds as ready for this plot (need mst, pseudotime, etc)
lib_info_with_pseudo <- pData(cds)
if (is.null(cds@dim_reduce_type)){
stop("Error: dimensionality not yet reduced. Please call reduceDimension() before calling this function.")
}
if (cds@dim_reduce_type == "ICA"){
reduced_dim_coords <- reducedDimS(cds)
}else if (cds@dim_reduce_type %in% c("SimplePPT", "DDRTree", "SGL-tree") ){
reduced_dim_coords <- reducedDimK(cds)
closest_vertex <- cds@auxOrderingData[["DDRTree"]]$pr_graph_cell_proj_closest_vertex
}else {
stop("Error: unrecognized dimensionality reduction method.")
}
if (is.null(reduced_dim_coords)){
stop("You must first call reduceDimension() before using this function")
}
dp_mst <- minSpanningTree(cds)
if(is.null(root_states)) {
if(is.null(lib_info_with_pseudo$Pseudotime)){
root_cell <- row.names(lib_info_with_pseudo)[degree(dp_mst) == 1][1]
}
else
root_cell <- row.names(subset(lib_info_with_pseudo, Pseudotime == 0))
if(cds@dim_reduce_type != "ICA")
root_cell <- V(dp_mst)$name[cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex[root_cell, ]]
}
else {
candidate_root_cells <- row.names(subset(pData(cds), State %in% root_states))
if(cds@dim_reduce_type == "ICA") {
root_cell <- candidate_root_cells[which(degree(dp_mst, candidate_root_cells) == 1)]
} else {
Y_candidate_root_cells <- V(dp_mst)$name[cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex[candidate_root_cells, ]]
root_cell <- Y_candidate_root_cells[which(degree(dp_mst, Y_candidate_root_cells) == 1)]
}
}
# #root_cell <- cds@auxOrderingData[[cds@dim_reduce_type]]$root_cell
# root_state <- pData(cds)[root_cell,]$State
# #root_state <- V(pr_graph_cell_proj_mst)[root_cell,]$State
# pr_graph_root <- subset(pData(cds), State == root_state)
# closest_vertex <- cds@auxOrderingData[["DDRTree"]]$pr_graph_cell_proj_closest_vertex
# root_cell_point_in_Y <- closest_vertex[row.names(pr_graph_root),]
tree_coords <- layout_as_tree(dp_mst, root=root_cell)
#ica_space_df <- data.frame(Matrix::t(reduced_dim_coords[c(x,y),]))
ica_space_df <- data.frame(tree_coords)
row.names(ica_space_df) <- colnames(reduced_dim_coords)
colnames(ica_space_df) <- c("prin_graph_dim_1", "prin_graph_dim_2")
ica_space_df$sample_name <- row.names(ica_space_df)
#ica_space_with_state_df <- merge(ica_space_df, lib_info_with_pseudo, by.x="sample_name", by.y="row.names")
#print(ica_space_with_state_df)
if (is.null(dp_mst)){
stop("You must first call orderCells() before using this function")
}
edge_list <- as.data.frame(get.edgelist(dp_mst))
colnames(edge_list) <- c("source", "target")
edge_df <- merge(ica_space_df, edge_list, by.x="sample_name", by.y="source", all=TRUE)
#edge_df <- ica_space_df
edge_df <- plyr::rename(edge_df, c("prin_graph_dim_1"="source_prin_graph_dim_1", "prin_graph_dim_2"="source_prin_graph_dim_2"))
edge_df <- merge(edge_df, ica_space_df[,c("sample_name", "prin_graph_dim_1", "prin_graph_dim_2")], by.x="target", by.y="sample_name", all=TRUE)
edge_df <- plyr::rename(edge_df, c("prin_graph_dim_1"="target_prin_graph_dim_1", "prin_graph_dim_2"="target_prin_graph_dim_2"))
#S_matrix <- reducedDimS(cds)
#data_df <- data.frame(t(S_matrix[c(x,y),]))
if(cds@dim_reduce_type == "ICA"){
S_matrix <- tree_coords[,] #colnames(cds)
} else if(cds@dim_reduce_type %in% c("DDRTree", "SimplePPT", "SGL-tree")){
S_matrix <- tree_coords[closest_vertex,]
closest_vertex <- cds@auxOrderingData[["DDRTree"]]$pr_graph_cell_proj_closest_vertex
}
data_df <- data.frame(S_matrix)
row.names(data_df) <- colnames(reducedDimS(cds))
colnames(data_df) <- c("data_dim_1", "data_dim_2")
data_df$sample_name <- row.names(data_df)
data_df <- merge(data_df, lib_info_with_pseudo, by.x="sample_name", by.y="row.names")
markers_exprs <- NULL
if (is.null(markers) == FALSE){
markers_fData <- subset(fData(cds), gene_short_name %in% markers)
if (nrow(markers_fData) >= 1){
markers_exprs <- reshape2::melt(as.matrix(exprs(cds[row.names(markers_fData),])))
colnames(markers_exprs)[1:2] <- c('feature_id','cell_id')
markers_exprs <- merge(markers_exprs, markers_fData, by.x = "feature_id", by.y="row.names")
#print (head( markers_exprs[is.na(markers_exprs$gene_short_name) == FALSE,]))
markers_exprs$feature_label <- as.character(markers_exprs$gene_short_name)
markers_exprs$feature_label[is.na(markers_exprs$feature_label)] <- markers_exprs$Var1
}
}
if (is.null(markers_exprs) == FALSE && nrow(markers_exprs) > 0){
data_df <- merge(data_df, markers_exprs, by.x="sample_name", by.y="cell_id")
#print (head(edge_df))
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2, I(cell_size))) + facet_wrap(~feature_label)
}else{
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2))
}
if (show_tree){
g <- g + geom_segment(aes_string(x="source_prin_graph_dim_1", y="source_prin_graph_dim_2", xend="target_prin_graph_dim_1", yend="target_prin_graph_dim_2"), size=cell_link_size, linetype="solid", na.rm=TRUE, data=edge_df)
}
# FIXME: setting size here overrides the marker expression funtionality.
# Don't do it!
if (is.null(markers_exprs) == FALSE && nrow(markers_exprs) > 0){
if(class(data_df[, color_by]) == 'numeric') {
g <- g + geom_jitter(aes_string(color = paste0("log10(", color_by, " + 0.1)")), size=I(cell_size), na.rm = TRUE, height=5) +
scale_color_viridis(name = paste0("log10(", color_by, ")"), ...)
} else {
g <- g + geom_jitter(aes_string(color = color_by), size=I(cell_size), na.rm = TRUE, height=5)
}
}else {
if(class(data_df[, color_by]) == 'numeric') {
g <- g + geom_jitter(aes_string(color = paste0("log10(", color_by, " + 0.1)")), size=I(cell_size), na.rm = TRUE, height=5) +
scale_color_viridis(name = paste0("log10(", color_by, " + 0.1)"), ...)
} else {
g <- g + geom_jitter(aes_string(color = color_by), size=I(cell_size), na.rm = TRUE, height=5)
}
}
if (show_branch_points && cds@dim_reduce_type == 'DDRTree'){
mst_branch_nodes <- cds@auxOrderingData[[cds@dim_reduce_type]]$branch_points
branch_point_df <- subset(edge_df, sample_name %in% mst_branch_nodes)[,c("sample_name", "source_prin_graph_dim_1", "source_prin_graph_dim_2")]
branch_point_df$branch_point_idx <- match(branch_point_df$sample_name, mst_branch_nodes)
branch_point_df <- branch_point_df[!duplicated(branch_point_df$branch_point_idx), ]
g <- g + geom_point(aes_string(x="source_prin_graph_dim_1", y="source_prin_graph_dim_2"),
size=2 * cell_size, na.rm=TRUE, data=branch_point_df) +
geom_text(aes_string(x="source_prin_graph_dim_1", y="source_prin_graph_dim_2", label="branch_point_idx"),
size=1.5 * cell_size, color="white", na.rm=TRUE, data=branch_point_df)
}
if (show_cell_names){
g <- g +geom_text(aes(label=sample_name), size=cell_name_size)
}
g <- g +
#scale_color_brewer(palette="Set1") +
theme(strip.background = element_rect(colour = 'white', fill = 'white')) +
theme(panel.border = element_blank()) +
# theme(axis.line.x = element_line(size=0.25, color="black")) +
# theme(axis.line.y = element_line(size=0.25, color="black")) +
theme(panel.grid.minor.x = element_blank(), panel.grid.minor.y = element_blank()) +
theme(panel.grid.major.x = element_blank(), panel.grid.major.y = element_blank()) +
theme(panel.background = element_rect(fill='white')) +
theme(legend.key=element_blank()) +
xlab('') +
ylab('') +
theme(legend.position="top", legend.key.height=grid::unit(0.35, "in")) +
#guides(color = guide_legend(label.position = "top")) +
theme(legend.key = element_blank()) +
theme(panel.background = element_rect(fill='white')) +
theme(line = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank())
g
}
# Modified function: Plot heatmap of 3 branches with the same coloring. Each CDS subset has to have the same set of genes.
#' Create a heatmap to demonstrate the bifurcation of gene expression along multiple branches
#'
#' @param cds CellDataSet for the experiment (normally only the branching genes detected with BEAM)
#' @param branches The terminal branches (states) on the developmental tree you want to investigate.
#' @param branches_name Name (for example, cell type) of branches you believe the cells on the branches are associated with.
#' @param cluster_rows Whether to cluster the rows of the heatmap.
#' @param hclust_method The method used by pheatmap to perform hirearchical clustering of the rows.
#' @param num_clusters Number of clusters for the heatmap of branch genes
#' @param hmcols The color scheme for drawing the heatmap.
#' @param add_annotation_row Additional annotations to show for each row in the heatmap. Must be a dataframe with one row for each row in the fData table of cds_subset, with matching IDs.
#' @param add_annotation_col Additional annotations to show for each column in the heatmap. Must be a dataframe with one row for each cell in the pData table of cds_subset, with matching IDs.
#' @param show_rownames Whether to show the names for each row in the table.
#' @param use_gene_short_name Whether to use the short names for each row. If FALSE, uses row IDs from the fData table.
#' @param norm_method Determines how to transform expression values prior to rendering
#' @param scale_max The maximum value (in standard deviations) to show in the heatmap. Values larger than this are set to the max.
#' @param scale_min The minimum value (in standard deviations) to show in the heatmap. Values smaller than this are set to the min.
#' @param trend_formula A formula string specifying the model used in fitting the spline curve for each gene/feature.
#' @param return_heatmap Whether to return the pheatmap object to the user.
#' @param cores Number of cores to use when smoothing the expression curves shown in the heatmap.
#' @return A list of heatmap_matrix (expression matrix for the branch committment), ph (pheatmap heatmap object),
#' annotation_row (annotation data.frame for the row), annotation_col (annotation data.frame for the column).
#' @import pheatmap
#' @export
#'
plot_multiple_branches_heatmap <- function(cds,
branches,
branches_name = NULL,
cluster_rows = TRUE,
hclust_method = "ward.D2",
num_clusters = 6,
hmcols = NULL,
add_annotation_row = NULL,
add_annotation_col = NULL,
show_rownames = FALSE,
use_gene_short_name = TRUE,
norm_method = c("vstExprs", "log"),
scale_max=3,
scale_min=-3,
trend_formula = '~sm.ns(Pseudotime, df=3)',
return_heatmap=FALSE,
cores=1){
pseudocount <- 1
if(!(all(branches %in% pData(cds)$State)) & length(branches) == 1){
stop('This function only allows to make multiple branch plots where branches is included in the pData')
}
branch_label <- branches
if(!is.null(branches_name)){
if(length(branches) != length(branches_name)){
stop('branches_name should have the same length as branches')
}
branch_label <- branches_name
}
#test whether or not the states passed to branches are true branches (not truncks) or there are terminal cells
g <- cds@minSpanningTree
m <- NULL
# branche_cell_num <- c()
for(branch_in in branches) {
branches_cells <- row.names(subset(pData(cds), State == branch_in))
root_state <- subset(pData(cds), Pseudotime == 0)[, 'State']
root_state_cells <- row.names(subset(pData(cds), State == root_state))
if(cds@dim_reduce_type != 'ICA') {
root_state_cells <- unique(paste('Y_', cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex[root_state_cells, ], sep = ''))
branches_cells <- unique(paste('Y_', cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex[branches_cells, ], sep = ''))
}
root_cell <- root_state_cells[which(degree(g, v = root_state_cells) == 1)]
tip_cell <- branches_cells[which(degree(g, v = branches_cells) == 1)]
traverse_res <- traverseTree(g, root_cell, tip_cell)
path_cells <- names(traverse_res$shortest_path[[1]])
if(cds@dim_reduce_type != 'ICA') {
pc_ind <- cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex
path_cells <- row.names(pc_ind)[paste('Y_', pc_ind[, 1], sep = '') %in% path_cells]
}
cds_subset <- cds[, path_cells]
newdata <- data.frame(Pseudotime = seq(0, max(pData(cds_subset)$Pseudotime),length.out = 100))
tmp <- genSmoothCurves(cds_subset, cores=cores, trend_formula = trend_formula,
relative_expr = T, new_data = newdata)
if(is.null(m))
m <- tmp
else
m <- cbind(m, tmp)
}
#remove genes with no expression in any condition
m=m[!apply(m,1,sum)==0,]
norm_method <- match.arg(norm_method)
# FIXME: this needs to check that vst values can even be computed. (They can only be if we're using NB as the expressionFamily)
if(norm_method == 'vstExprs' && is.null(cds@dispFitInfo[["blind"]]$disp_func) == FALSE) {
m = vstExprs(cds, expr_matrix=m)
}
else if(norm_method == 'log') {
m = log10(m+pseudocount)
}
# Row-center the data.
m=m[!apply(m,1,sd)==0,]
m=Matrix::t(scale(Matrix::t(m),center=TRUE))
m=m[is.na(row.names(m)) == FALSE,]
m[is.nan(m)] = 0
m[m>scale_max] = scale_max
m[m<scale_min] = scale_min
heatmap_matrix <- m
row_dist <- as.dist((1 - cor(Matrix::t(heatmap_matrix)))/2)
row_dist[is.na(row_dist)] <- 1
if(is.null(hmcols)) {
bks <- seq(-3.1,3.1, by = 0.1)
hmcols <- blue2green2red(length(bks) - 1)
}
else {
bks <- seq(-3.1,3.1, length.out = length(hmcols))
}
ph <- pheatmap(heatmap_matrix,
useRaster = T,
cluster_cols=FALSE,
cluster_rows=T,
show_rownames=F,
show_colnames=F,
clustering_distance_rows=row_dist,
clustering_method = hclust_method,
cutree_rows=num_clusters,
silent=TRUE,
filename=NA,
breaks=bks,
color=hmcols)
annotation_col <- data.frame(Branch=factor(rep(rep(branch_label, each = 100))))
annotation_row <- data.frame(Cluster=factor(cutree(ph$tree_row, num_clusters)))
col_gaps_ind <- c(1:(length(branches) - 1)) * 100
if(!is.null(add_annotation_row)) {
old_colnames_length <- ncol(annotation_row)
annotation_row <- cbind(annotation_row, add_annotation_row[row.names(annotation_row), ])
colnames(annotation_row)[(old_colnames_length+1):ncol(annotation_row)] <- colnames(add_annotation_row)
# annotation_row$bif_time <- add_annotation_row[as.character(fData(absolute_cds[row.names(annotation_row), ])$gene_short_name), 1]
}
if (use_gene_short_name == TRUE) {
if (is.null(fData(cds)$gene_short_name) == FALSE) {
feature_label <- as.character(fData(cds)[row.names(heatmap_matrix), 'gene_short_name'])
feature_label[is.na(feature_label)] <- row.names(heatmap_matrix)
row_ann_labels <- as.character(fData(cds)[row.names(annotation_row), 'gene_short_name'])
row_ann_labels[is.na(row_ann_labels)] <- row.names(annotation_row)
}
else {
feature_label <- row.names(heatmap_matrix)
row_ann_labels <- row.names(annotation_row)
}
}
else {
feature_label <- row.names(heatmap_matrix)
row_ann_labels <- row.names(annotation_row)
}
row.names(heatmap_matrix) <- feature_label
row.names(annotation_row) <- row_ann_labels
colnames(heatmap_matrix) <- c(1:ncol(heatmap_matrix))
if(!(cluster_rows)) {
annotation_row <- NA
}
ph_res <- pheatmap(heatmap_matrix[, ], #ph$tree_row$order
useRaster = T,
cluster_cols = FALSE,
cluster_rows = cluster_rows,
show_rownames=show_rownames,
show_colnames=F,
#scale="row",
clustering_distance_rows=row_dist, #row_dist
clustering_method = hclust_method, #ward.D2
cutree_rows=num_clusters,
# cutree_cols = 2,
annotation_row=annotation_row,
annotation_col=annotation_col,
gaps_col = col_gaps_ind,
treeheight_row = 20,
breaks=bks,
fontsize = 12,
color=hmcols,
silent=TRUE,
border_color = NA,
filename=NA
)
grid::grid.rect(gp=grid::gpar("fill", col=NA))
grid::grid.draw(ph_res$gtable)
if (return_heatmap){
return(ph_res)
}
}
#' Create a kinetic curves to demonstrate the bifurcation of gene expression along multiple branches
#'
#' @param cds CellDataSet for the experiment (normally only the branching genes detected with BEAM)
#' @param branches The terminal branches (states) on the developmental tree you want to investigate.
#' @param branches_name Name (for example, cell type) of branches you believe the cells on the branches are associated with.
#' @param min_expr The minimum level of expression to show in the plot
#' @param cell_size A number how large the cells should be in the plot
#' @param norm_method Determines how to transform expression values prior to rendering
#' @param nrow the number of rows used when laying out the panels for each gene's expression
#' @param ncol the number of columns used when laying out the panels for each gene's expression
#' @param panel_order the order in which genes should be layed out (left-to-right, top-to-bottom)
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to be used to color each cell
#' @param trend_formula the model formula to be used for fitting the expression trend over pseudotime
#' @param label_by_short_name label figure panels by gene_short_name (TRUE) or feature id (FALSE)
#' @param TPM Whether to convert the expression value into TPM values.
#' @param cores Number of cores to use when smoothing the expression curves shown in the heatmap.
#' @return a ggplot2 plot object
#'
#' @importFrom Biobase esApply exprs<-
#' @importFrom stats lowess
#'
#' @export
#'
plot_multiple_branches_pseudotime <- function(cds,
branches,
branches_name = NULL,
min_expr = NULL,
cell_size = 0.75,
norm_method = c("vstExprs", "log"),
nrow = NULL,
ncol = 1,
panel_order = NULL,
color_by = "Branch",
trend_formula = '~sm.ns(Pseudotime, df=3)',
label_by_short_name = TRUE,
TPM = FALSE,
cores=1){
if(TPM) {
exprs(cds) <- esApply(cds, 2, function(x) x / sum(x) * 1e6)
}
if(!(all(branches %in% pData(cds)$State)) & length(branches) == 1){
stop('This function only allows to make multiple branch plots where branches is included in the pData')
}
branch_label <- branches
if(!is.null(branches_name)){
if(length(branches) != length(branches_name)){
stop('branches_name should have the same length as branches')
}
branch_label <- branches_name
}
#test whether or not the states passed to branches are true branches (not truncks) or there are terminal cells
g <- cds@minSpanningTree
m <- NULL
cds_exprs <- NULL
# branche_cell_num <- c()
for(branch_in in branches) {
branches_cells <- row.names(subset(pData(cds), State == branch_in))
root_state <- subset(pData(cds), Pseudotime == 0)[, 'State']
root_state_cells <- row.names(subset(pData(cds), State == root_state))
if(cds@dim_reduce_type != 'ICA') {
root_state_cells <- unique(paste('Y_', cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex[root_state_cells, ], sep = ''))
branches_cells <- unique(paste('Y_', cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex[branches_cells, ], sep = ''))
}
root_cell <- root_state_cells[which(degree(g, v = root_state_cells) == 1)]
tip_cell <- branches_cells[which(degree(g, v = branches_cells) == 1)]
traverse_res <- traverseTree(g, root_cell, tip_cell)
path_cells <- names(traverse_res$shortest_path[[1]])
if(cds@dim_reduce_type != 'ICA') {
pc_ind <- cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex
path_cells <- row.names(pc_ind)[paste('Y_', pc_ind[, 1], sep = '') %in% path_cells]
}
#if(is.null(pData(cds)$no_expression)) {
cds_subset <- cds[, path_cells]
# } else {
# cds_subset <- cds[, path_cells %in% colnames(cds)[!pData(cds)$no_expression]]
# }
newdata <- data.frame(Pseudotime = pData(cds_subset)$Pseudotime, row.names = colnames(cds_subset))
tmp <- t(esApply(cds_subset, 1, function(x) lowess(x[order(pData(cds_subset)$Pseudotime)])$y))
# tmp <- t(esApply(cds_subset, 1, function(x) {
# x <- x[order(pData(cds_subset)$Pseudotime)]
# c(smooth::sma(x, order = 100, h = 1, silent="all")$fitted)}) #, x[length(x)]
# )
colnames(tmp) <- colnames(cds_subset)[order(pData(cds_subset)$Pseudotime)]
# tmp <- genSmoothCurves(cds_subset, cores=cores, trend_formula = trend_formula,
# relative_expr = T, new_data = newdata)
cds_exprs_tmp <- reshape2::melt(log2(tmp + 1))
cds_exprs_tmp <- reshape2::melt(tmp)
colnames(cds_exprs_tmp) <- c("f_id", "Cell", "expression")
cds_exprs_tmp$Branch <- branch_label[which(branches == branch_in)]
if(is.null(cds_exprs))
cds_exprs <- cds_exprs_tmp
else
cds_exprs <- rbind(cds_exprs, cds_exprs_tmp)
if(is.null(m))
m <- tmp
else
m <- cbind(m, tmp)
}
#remove genes with no expression in any condition
m=m[!apply(m,1,sum)==0,]
norm_method <- match.arg(norm_method)
# FIXME: this needs to check that vst values can even be computed. (They can only be if we're using NB as the expressionFamily)
if(norm_method == 'vstExprs' && is.null(cds@dispFitInfo[["blind"]]$disp_func) == FALSE) {
m = vstExprs(cds, expr_matrix=m)
}
else if(norm_method == 'log') {
m = log10(m+pseudocount)
}
if (is.null(min_expr)) {
min_expr <- cds@lowerDetectionLimit
}
cds_pData <- pData(cds)
cds_fData <- fData(cds)
cds_exprs <- merge(cds_exprs, cds_fData, by.x = "f_id", by.y = "row.names")
cds_exprs <- merge(cds_exprs, cds_pData, by.x = "Cell", by.y = "row.names")
cds_exprs <- plyr::ddply(cds_exprs, .(Branch), mutate, Pseudotime = (Pseudotime - min(Pseudotime)) * 100 / (max(Pseudotime) - min(Pseudotime)) )
# if (integer_expression) {
# cds_exprs$adjusted_expression <- round(cds_exprs$expression)
# }
# else {
# cds_exprs$adjusted_expression <- log10(cds_exprs$expression)
# }
if (label_by_short_name == TRUE) {
if (is.null(cds_exprs$gene_short_name) == FALSE) {
cds_exprs$feature_label <- as.character(cds_exprs$gene_short_name)
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
cds_exprs$feature_label <- as.factor(cds_exprs$feature_label)
# trend_formula <- paste("adjusted_expression", trend_formula,
# sep = "")
cds_exprs$Branch <- as.factor(cds_exprs$Branch)
# new_data <- data.frame(Pseudotime = pData(cds_subset)$Pseudotime, Branch = pData(cds_subset)$Branch)
# full_model_expectation <- genSmoothCurves(cds_subset, cores=1, trend_formula = trend_formula,
# relative_expr = T, new_data = new_data)
# colnames(full_model_expectation) <- colnames(cds_subset)
# cds_exprs$full_model_expectation <- apply(cds_exprs,1, function(x) full_model_expectation[x[2], x[1]])
# if(!is.null(reducedModelFormulaStr)){
# reduced_model_expectation <- genSmoothCurves(cds_subset, cores=1, trend_formula = reducedModelFormulaStr,
# relative_expr = T, new_data = new_data)
# colnames(reduced_model_expectation) <- colnames(cds_subset)
# cds_exprs$reduced_model_expectation <- apply(cds_exprs,1, function(x) reduced_model_expectation[x[2], x[1]])
# }
# # FIXME: If you want to show the bifurcation time for each gene, this function
# # should just compute it. Passing it in as a dataframe is just too complicated
# # and will be hard on the user.
# # if(!is.null(bifurcation_time)){
# # cds_exprs$bifurcation_time <- bifurcation_time[as.vector(cds_exprs$gene_short_name)]
# # }
# if (method == "loess")
# cds_exprs$expression <- cds_exprs$expression + cds@lowerDetectionLimit
# if (label_by_short_name == TRUE) {
# if (is.null(cds_exprs$gene_short_name) == FALSE) {
# cds_exprs$feature_label <- as.character(cds_exprs$gene_short_name)
# cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
# }
# else {
# cds_exprs$feature_label <- cds_exprs$f_id
# }
# }
# else {
# cds_exprs$feature_label <- cds_exprs$f_id
# }
# cds_exprs$feature_label <- factor(cds_exprs$feature_label)
# if (is.null(panel_order) == FALSE) {
# cds_exprs$feature_label <- factor(cds_exprs$feature_label,
# levels = panel_order)
# }
# cds_exprs$expression[is.na(cds_exprs$expression)] <- min_expr
# cds_exprs$expression[cds_exprs$expression < min_expr] <- min_expr
# cds_exprs$full_model_expectation[is.na(cds_exprs$full_model_expectation)] <- min_expr
# cds_exprs$full_model_expectation[cds_exprs$full_model_expectation < min_expr] <- min_expr
# if(!is.null(reducedModelFormulaStr)){
# cds_exprs$reduced_model_expectation[is.na(cds_exprs$reduced_model_expectation)] <- min_expr
# cds_exprs$reduced_model_expectation[cds_exprs$reduced_model_expectation < min_expr] <- min_expr
# }
cds_exprs$State <- as.factor(cds_exprs$State)
cds_exprs$Branch <- as.factor(cds_exprs$Branch)
q <- ggplot(aes(Pseudotime, expression), data = cds_exprs)
# if (!is.null(bifurcation_time)) {
# q <- q + geom_vline(aes(xintercept = bifurcation_time),
# color = "black", linetype = "longdash")
# }
if (is.null(color_by) == FALSE) {
q <- q + geom_line(aes_string(color = color_by), size = I(cell_size))
}
#if (is.null(reducedModelFormulaStr) == FALSE)
q <- q + facet_wrap(~feature_label, nrow = nrow, ncol = ncol, scales = "free_y") #+ scale_y_log10()
#else q <- q + scale_y_log10() + facet_wrap(~feature_label,
# nrow = nrow, ncol = ncol, scales = "free_y")
#if (method == "loess")
# q <- q + stat_smooth(aes(fill = Branch, color = Branch),
# method = "loess")
#else if (method == "fitting") {
# q <- q + geom_line(aes_string(x = "Pseudotime", y = "full_model_expectation",
# linetype = "Branch"), data = cds_exprs) #+ scale_color_manual(name = "Type", values = c(colour_cell, colour), labels = c("Pre-branch", "AT1", "AT2", "AT1", "AT2")
#}
#if(!is.null(reducedModelFormulaStr)) {
# q <- q + geom_line(aes_string(x = "Pseudotime", y = "reduced_model_expectation"),
# color = 'black', linetype = 2, data = cds_exprs)
#}
q <- q + ylab("Expression") + xlab("Pseudotime (stretched)")
q <- q + monocle_theme_opts()
q + expand_limits(y = min_expr)
}
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