R/pca2D.R
In joeburns06/hocuspocus: A Suite of Single-Cell RNA-Seq Analysis Tools Built for Dummy Biologists
#' Generate a two-dimensional PCA plot.
#'
#' Takes ExpressionSet object, performs PCA on the transposed expression matrix,
#' then plots the projections of the sample scores on the two specified PCs.
#'
#' @param cellData ExpressionSet object created with readCells (and preferably
#' transformed with prepCells). It is also helpful to first run
#' reduceGenes_var.
#' @param center Boolean specifying whether to the center the data prior to PCA.
#' This is generally recommended.
#' @param scale Boolean specifying whether the data should be scaled prior to
#' PCA. This is generally not recommended unless samples have different units
#' (e.g. some samples are counts and some are TPMs).
#' @param PCs Vector of two integers specifying the PCs to plot. E.g. c(1,3)
#' plots PC1 vs PC3.
#' @param groups Character string specifying the title of the column in pData
#' that contains the names of the groups to which each sample belongs. The
#' dots representing each sample in the plot will be colored by group. The
#' column length should be the same length as the number of samples.
#' @param values Character string specifying the title of the column in pData
#' that contains a vector of numeric values to be plotted as a color gradient
#' on the dots in the plot. The column length should be the same length as
#' the number of samples. Gene and values cannot be specified simultaneously.
#' If groups and values are both specified, gene will be used for coloring.
#' Set bubble to TRUE to display group information as well.
#' @param gene Character string specifying a gene whose expression values will
#' be plotted a color gradient on the dots in the plot. One gene name should
#' be specified, and the gene must be present within the expression table.
#' Gene and values cannot be specified simultaneously. If groups and gene are
#' both specified, gene will be used for coloring. Set bubble to TRUE to
#' display group information as well.
#' @param colors Vector of character strings of length 2 specifying the color range for values or gene. If
#' not specified, a default black-to-yellow color gradient will be used.
#' @param logNumeric Boolean specifying whether the numbers in values should be
#' transformed to log2 space.
#' @param alpha Numeric specifying the transparency (from 0 to 1) level of the
#' dot colors.
#' @param dotsize Numeric specifying the size of the dots in the plot.
#' @param bubble Boolean specifying whether dots of different sizes should be
#' plotted to display information about another variable on the plot.
#' @param bubbleSizes Vector of numerics specifying the dot sizes for each group
#' within groups. Length of bubbleSizes should be the same length as groups.
#' If bubbleSizes is not specified, default sizes will be generated according
#' to the levels in the groups column. Useful for displaying group
#' information along with values or gene. Additionally, can be used to
#' indicate additional information about groups. E.g. if the levels in groups
#' are 'A1', 'B1', 'C2', and 'D2', bubbleSizes could be c(3,3,6,6) to indicate
#' the '1' and '2' components of the groups with different dot sizes.
#' @param save Boolean specifying whether to save the resultant plot as a .tiff
#' file.
#' @return PCA plot for the two specified PCs.
#' @export
pca2D <- function(cellData, center = TRUE, scale = FALSE, PCs = c(1, 2), ICA=FALSE, groups, values, gene, colors, logNumeric = TRUE, CR = FALSE, alpha = 0.7, dotsize = 5,
bubble = FALSE, bubbleSizes, save = FALSE) {
if (.Platform$OS.type == "windows") {
quartz <- function() windows()
}
if (cellData@logData$prepCells[1] == "No") {
warning("It would be wise to run prepCells prior to pca2D.", call. = FALSE)
}
if (!missing(groups) && !all((groups %in% colnames(pData(cellData))))) {
stop("The column name specified for groups is not found in phenoData. If absent, add a column to phenoData containing the group for each cell, even if it only consists of 1 group.",
call. = FALSE)
}
if (!missing(values) && !all((values %in% colnames(pData(cellData))))) {
stop("The column name specified for values is not found in phenoData.",
call. = FALSE)
}
if (!missing(groups) && !all((names(cData(cellData)) %in% colnames(pData(cellData))))) {
stop("Some or all of the column names in colorData do not have matching column names in phenoData.",
call. = FALSE)
}
samples <- pData(cellData)
exprs_table <- exprs(cellData)
# Obtain cell colors
if (ICA==FALSE){
pca1 <- prcomp(t(exprs_table), center = center, scale. = scale)
comp1 <- data.frame(pca1$x[, PCs])
comp.cell.type1 <- comp1
}
if (ICA==TRUE){
ica1 <- fastICA::fastICA(t(exprs_table), max(PCs))
comp1 <- data.frame(ica1$S[, PCs])
comp1["y"] <- seq(0, 0, length.out = length(samples[, groups]))
comp.cell.type1 <- comp1
}
if (missing(groups)) {
cell_colors <- "blue"
comp.cell.type1["Cell_Type"] <- rep("All", nrow(pData(cellData)))
}
if (!missing(groups)) {
cell_colors <- data.frame(cellData@colorData[[groups]], stringsAsFactors = FALSE)
cell_colors <- as.character(cell_colors[!apply(is.na(cell_colors) | cell_colors == "", 1, all), ])
groupz <- data.frame(pData(cellData)[, groups], stringsAsFactors = FALSE)
comp.cell.type1["Cell_Type"] <- factor(groupz[, colnames(groupz)], levels = unique(groupz[, colnames(groupz)]), ordered = FALSE)
}
if (bubble == TRUE) {
if (missing(bubbleSizes)) {
sizes <- 1.4^((4:(4 + length(levels(comp.cell.type1$Cell_Type)))))
}
if (!missing(bubbleSizes)) {
sizes <- bubbleSizes
}
}
if (bubble == FALSE) {
sizes <- vector(length = length(cell_colors))
sizes[1:length(cell_colors)] <- dotsize
}
if (missing(values) && missing(gene)) {
if (bubble == TRUE) {
warning("Bubble sizes currently reflect the number of levels in groups and are not adding additional information to the plot.", call. = FALSE)
}
if (CR == TRUE) {
samp_PCA <- ggplot(comp.cell.type1) + geom_point(aes_string(x = colnames(comp1)[1], y = colnames(comp1)[2], fill = "factor(Cell_Type,levels=unique(Cell_Type),ordered=FALSE)",
size = "factor(Cell_Type,levels=unique(Cell_Type),ordered=FALSE)"), shape = 21, color = "black", alpha = alpha) + scale_fill_manual(name = "Groups",
values = cell_colors) + scale_size_manual(name = "Groups", values = sizes) + guides(fill = guide_legend("Groups"), color = guide_legend("Groups"),
size = guide_legend("Groups")) + theme_bw() + theme(legend.key = element_rect(colour = F), legend.background = element_rect(fill = NA)) + theme(axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 15), axis.title.x = element_text(size = 25, vjust = -0.8), axis.title.y = element_text(size = 25, vjust = 1.4),
legend.text = element_text(size = 13), legend.title = element_text(size = 15, face = "bold"), plot.margin = grid::unit(c(2.1, 1.1, 1.1, 1.1),
"cm")) + stat_ellipse(aes_string(x = colnames(comp1)[1], y = colnames(comp1)[2], fill = "factor(Cell_Type)"), geom = "polygon", level = 0.95,
alpha = 0.3, show_guide = F)
quartz()
print(samp_PCA)
if (save == TRUE) {
ggsave(samp_PCA, file = paste("PCA Plot.tiff"), width = 7, height = 6)
}
}
if (CR == FALSE) {
samp_PCA <- ggplot(comp.cell.type1) + geom_point(aes_string(x = colnames(comp1)[1], y = colnames(comp1)[2], fill = "factor(Cell_Type,levels=unique(Cell_Type),ordered=FALSE)",
size = "factor(Cell_Type,levels=unique(Cell_Type),ordered=FALSE)"), shape = 21, color = "black", alpha = alpha) + scale_fill_manual(name = "Groups",
values = cell_colors) + scale_size_manual(name = "Groups", values = sizes) + guides(fill = guide_legend("Groups"), color = guide_legend("Groups"),
size = guide_legend("Groups")) + theme_bw() + theme(legend.key = element_rect(colour = F), legend.background = element_rect(fill = NA)) + theme(axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 15), axis.title.x = element_text(size = 25, vjust = -0.8), axis.title.y = element_text(size = 25, vjust = 1.4),
legend.text = element_text(size = 13), legend.title = element_text(size = 15, face = "bold"), plot.margin = grid::unit(c(2.1, 1.1, 1.1, 1.1),
"cm")) + scale_color_gradient2()
quartz()
print(samp_PCA)
if (save == TRUE) {
ggsave(samp_PCA, file = paste("PCA Plot.tiff"), width = 7, height = 6)
}
}
}
if (!missing(values)) {
if (!missing(gene)) {
stop("Cannot specify both values and gene.", call. = FALSE)
}
if (CR == TRUE) {
warning("Cannot have a confidence region with values or genes turned on.", call. = FALSE)
}
if (!missing(colors)){
cell_colors <- colors
}
if (length(cell_colors) != 2) {
warning("Specify a vector of two colors to control color, using default colors instead.", call. = FALSE)
cell_colors[1:2] <- c("black", "yellow")
}
valuez <- data.frame(pData(cellData)[, values], stringsAsFactors = FALSE)
if (logNumeric == TRUE) {
valuez <- log2(valuez)
}
comp.cell.type1["Values"] <- valuez
if (bubble == TRUE) {
samp_PCA <- ggplot(comp.cell.type1) + geom_point(aes_string(x = colnames(comp1)[1], y = colnames(comp1)[2], fill = "Values", size = "factor(Cell_Type,levels=unique(Cell_Type),ordered=FALSE)"),
shape = 21, color = "black", alpha = alpha) + scale_size_manual(name = "Groups", values = sizes) + guides(color = guide_legend("Values"), size = guide_legend("Groups")) +
theme_bw() + theme(legend.key = element_rect(colour = F), legend.background = element_rect(fill = NA)) + theme(axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 15), axis.title.x = element_text(size = 25, vjust = -0.8), axis.title.y = element_text(size = 25, vjust = 1.4),
legend.text = element_text(size = 13), legend.title = element_text(size = 15, face = "bold"), plot.margin = grid::unit(c(2.1, 1.1, 1.1, 1.1),
"cm")) + scale_fill_gradient(low = cell_colors[1], high = cell_colors[2])
quartz()
print(samp_PCA)
if (save == TRUE) {
ggsave(samp_PCA, file = paste("PCA Plot.tiff"), width = 7, height = 6)
}
}
if (bubble == FALSE) {
samp_PCA <- ggplot(comp.cell.type1) + geom_point(aes_string(x = colnames(comp1)[1], y = colnames(comp1)[2], fill = "Values"), size = dotsize, shape = 21,
color = "black", alpha = alpha) + guides(color = guide_legend(title = "Values")) + theme_bw() + theme(legend.key = element_rect(colour = F),
legend.background = element_rect(fill = NA)) + theme(axis.text.x = element_text(size = 15), axis.text.y = element_text(size = 15), axis.title.x = element_text(size = 25,
vjust = -0.8), axis.title.y = element_text(size = 25, vjust = 1.4), legend.text = element_text(size = 13), legend.title = element_text(size = 15,
face = "bold"), plot.margin = grid::unit(c(2.1, 1.1, 1.1, 1.1), "cm")) + scale_fill_gradient(low = cell_colors[1], high = cell_colors[2])
quartz()
print(samp_PCA)
if (save == TRUE) {
ggsave(samp_PCA, file = paste("PCA Plot.tiff"), width = 7, height = 6)
}
}
}
if (!missing(gene)) {
if (!missing(values)) {
stop("Cannot specify both values and gene.", call. = FALSE)
}
if (CR == TRUE) {
warning("Cannot have a confidence region with values or genes turned on.", call. = FALSE)
}
if (!missing(gene) && !gene %in% row.names(exprs_table)) {
stop("Specified gene is not present in expression table", call. = FALSE)
}
if (!missing(colors)){
cell_colors <- colors
}
if (length(cell_colors) != 2) {
warning("Specify a vector of two colors to control color, using default colors instead", call. = FALSE)
cell_colors[1:2] <- c("black", "yellow")
}
genez <- data.frame(exprs_table[gene, ], stringsAsFactors = FALSE)[, 1]
comp.cell.type1[gene] <- genez
if (bubble == TRUE) {
samp_PCA <- ggplot(comp.cell.type1) + geom_point(aes_string(x = colnames(comp1)[1], y = colnames(comp1)[2], fill = gene, size = "factor(Cell_Type,levels=unique(Cell_Type),ordered=FALSE)"),
shape = 21, color = "black", alpha = alpha) + scale_size_manual(name = "Groups", values = sizes) + guides(color = guide_legend(title = gene),
size = guide_legend("Groups")) + theme_bw() + theme(legend.key = element_rect(colour = F), legend.background = element_rect(fill = NA)) + theme(axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 15), axis.title.x = element_text(size = 25, vjust = -0.8), axis.title.y = element_text(size = 25, vjust = 1.4),
legend.text = element_text(size = 13), legend.title = element_text(size = 15, face = "bold"), plot.margin = grid::unit(c(2.1, 1.1, 1.1, 1.1),
"cm")) + scale_fill_gradient(low = cell_colors[1], high = cell_colors[2])
quartz()
print(samp_PCA)
if (save == TRUE) {
ggsave(samp_PCA, file = paste("PCA Plot.tiff"), width = 7, height = 6)
}
}
if (bubble == FALSE) {
samp_PCA <- ggplot(comp.cell.type1) + geom_point(aes_string(x = colnames(comp1)[1], y = colnames(comp1)[2], fill = gene), size = dotsize, shape = 21,
color = "black", alpha = alpha) + guides(color = guide_legend(title = gene)) + theme_bw() + theme(legend.key = element_rect(colour = F), legend.background = element_rect(fill = NA)) +
theme(axis.text.x = element_text(size = 15), axis.text.y = element_text(size = 15), axis.title.x = element_text(size = 25, vjust = -0.8), axis.title.y = element_text(size = 25,
vjust = 1.4), legend.text = element_text(size = 13), legend.title = element_text(size = 15, face = "bold"), plot.margin = grid::unit(c(2.1,
1.1, 1.1, 1.1), "cm")) + scale_fill_gradient(low = cell_colors[1], high = cell_colors[2])
quartz()
print(samp_PCA)
if (save == TRUE) {
ggsave(samp_PCA, file = paste("PCA Plot.tiff"), width = 7, height = 6)
}
}
}
samp_PCA
}
joeburns06/hocuspocus documentation built on May 19, 2019, 2:59 p.m.
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#' Generate a two-dimensional PCA plot.
#'
#' Takes ExpressionSet object, performs PCA on the transposed expression matrix,
#' then plots the projections of the sample scores on the two specified PCs.
#'
#' @param cellData ExpressionSet object created with readCells (and preferably
#' transformed with prepCells). It is also helpful to first run
#' reduceGenes_var.
#' @param center Boolean specifying whether to the center the data prior to PCA.
#' This is generally recommended.
#' @param scale Boolean specifying whether the data should be scaled prior to
#' PCA. This is generally not recommended unless samples have different units
#' (e.g. some samples are counts and some are TPMs).
#' @param PCs Vector of two integers specifying the PCs to plot. E.g. c(1,3)
#' plots PC1 vs PC3.
#' @param groups Character string specifying the title of the column in pData
#' that contains the names of the groups to which each sample belongs. The
#' dots representing each sample in the plot will be colored by group. The
#' column length should be the same length as the number of samples.
#' @param values Character string specifying the title of the column in pData
#' that contains a vector of numeric values to be plotted as a color gradient
#' on the dots in the plot. The column length should be the same length as
#' the number of samples. Gene and values cannot be specified simultaneously.
#' If groups and values are both specified, gene will be used for coloring.
#' Set bubble to TRUE to display group information as well.
#' @param gene Character string specifying a gene whose expression values will
#' be plotted a color gradient on the dots in the plot. One gene name should
#' be specified, and the gene must be present within the expression table.
#' Gene and values cannot be specified simultaneously. If groups and gene are
#' both specified, gene will be used for coloring. Set bubble to TRUE to
#' display group information as well.
#' @param colors Vector of character strings of length 2 specifying the color range for values or gene. If
#' not specified, a default black-to-yellow color gradient will be used.
#' @param logNumeric Boolean specifying whether the numbers in values should be
#' transformed to log2 space.
#' @param alpha Numeric specifying the transparency (from 0 to 1) level of the
#' dot colors.
#' @param dotsize Numeric specifying the size of the dots in the plot.
#' @param bubble Boolean specifying whether dots of different sizes should be
#' plotted to display information about another variable on the plot.
#' @param bubbleSizes Vector of numerics specifying the dot sizes for each group
#' within groups. Length of bubbleSizes should be the same length as groups.
#' If bubbleSizes is not specified, default sizes will be generated according
#' to the levels in the groups column. Useful for displaying group
#' information along with values or gene. Additionally, can be used to
#' indicate additional information about groups. E.g. if the levels in groups
#' are 'A1', 'B1', 'C2', and 'D2', bubbleSizes could be c(3,3,6,6) to indicate
#' the '1' and '2' components of the groups with different dot sizes.
#' @param save Boolean specifying whether to save the resultant plot as a .tiff
#' file.
#' @return PCA plot for the two specified PCs.
#' @export
pca2D <- function(cellData, center = TRUE, scale = FALSE, PCs = c(1, 2), ICA=FALSE, groups, values, gene, colors, logNumeric = TRUE, CR = FALSE, alpha = 0.7, dotsize = 5,
bubble = FALSE, bubbleSizes, save = FALSE) {
if (.Platform$OS.type == "windows") {
quartz <- function() windows()
}
if (cellData@logData$prepCells[1] == "No") {
warning("It would be wise to run prepCells prior to pca2D.", call. = FALSE)
}
if (!missing(groups) && !all((groups %in% colnames(pData(cellData))))) {
stop("The column name specified for groups is not found in phenoData. If absent, add a column to phenoData containing the group for each cell, even if it only consists of 1 group.",
call. = FALSE)
}
if (!missing(values) && !all((values %in% colnames(pData(cellData))))) {
stop("The column name specified for values is not found in phenoData.",
call. = FALSE)
}
if (!missing(groups) && !all((names(cData(cellData)) %in% colnames(pData(cellData))))) {
stop("Some or all of the column names in colorData do not have matching column names in phenoData.",
call. = FALSE)
}
samples <- pData(cellData)
exprs_table <- exprs(cellData)
# Obtain cell colors
if (ICA==FALSE){
pca1 <- prcomp(t(exprs_table), center = center, scale. = scale)
comp1 <- data.frame(pca1$x[, PCs])
comp.cell.type1 <- comp1
}
if (ICA==TRUE){
ica1 <- fastICA::fastICA(t(exprs_table), max(PCs))
comp1 <- data.frame(ica1$S[, PCs])
comp1["y"] <- seq(0, 0, length.out = length(samples[, groups]))
comp.cell.type1 <- comp1
}
if (missing(groups)) {
cell_colors <- "blue"
comp.cell.type1["Cell_Type"] <- rep("All", nrow(pData(cellData)))
}
if (!missing(groups)) {
cell_colors <- data.frame(cellData@colorData[[groups]], stringsAsFactors = FALSE)
cell_colors <- as.character(cell_colors[!apply(is.na(cell_colors) | cell_colors == "", 1, all), ])
groupz <- data.frame(pData(cellData)[, groups], stringsAsFactors = FALSE)
comp.cell.type1["Cell_Type"] <- factor(groupz[, colnames(groupz)], levels = unique(groupz[, colnames(groupz)]), ordered = FALSE)
}
if (bubble == TRUE) {
if (missing(bubbleSizes)) {
sizes <- 1.4^((4:(4 + length(levels(comp.cell.type1$Cell_Type)))))
}
if (!missing(bubbleSizes)) {
sizes <- bubbleSizes
}
}
if (bubble == FALSE) {
sizes <- vector(length = length(cell_colors))
sizes[1:length(cell_colors)] <- dotsize
}
if (missing(values) && missing(gene)) {
if (bubble == TRUE) {
warning("Bubble sizes currently reflect the number of levels in groups and are not adding additional information to the plot.", call. = FALSE)
}
if (CR == TRUE) {
samp_PCA <- ggplot(comp.cell.type1) + geom_point(aes_string(x = colnames(comp1)[1], y = colnames(comp1)[2], fill = "factor(Cell_Type,levels=unique(Cell_Type),ordered=FALSE)",
size = "factor(Cell_Type,levels=unique(Cell_Type),ordered=FALSE)"), shape = 21, color = "black", alpha = alpha) + scale_fill_manual(name = "Groups",
values = cell_colors) + scale_size_manual(name = "Groups", values = sizes) + guides(fill = guide_legend("Groups"), color = guide_legend("Groups"),
size = guide_legend("Groups")) + theme_bw() + theme(legend.key = element_rect(colour = F), legend.background = element_rect(fill = NA)) + theme(axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 15), axis.title.x = element_text(size = 25, vjust = -0.8), axis.title.y = element_text(size = 25, vjust = 1.4),
legend.text = element_text(size = 13), legend.title = element_text(size = 15, face = "bold"), plot.margin = grid::unit(c(2.1, 1.1, 1.1, 1.1),
"cm")) + stat_ellipse(aes_string(x = colnames(comp1)[1], y = colnames(comp1)[2], fill = "factor(Cell_Type)"), geom = "polygon", level = 0.95,
alpha = 0.3, show_guide = F)
quartz()
print(samp_PCA)
if (save == TRUE) {
ggsave(samp_PCA, file = paste("PCA Plot.tiff"), width = 7, height = 6)
}
}
if (CR == FALSE) {
samp_PCA <- ggplot(comp.cell.type1) + geom_point(aes_string(x = colnames(comp1)[1], y = colnames(comp1)[2], fill = "factor(Cell_Type,levels=unique(Cell_Type),ordered=FALSE)",
size = "factor(Cell_Type,levels=unique(Cell_Type),ordered=FALSE)"), shape = 21, color = "black", alpha = alpha) + scale_fill_manual(name = "Groups",
values = cell_colors) + scale_size_manual(name = "Groups", values = sizes) + guides(fill = guide_legend("Groups"), color = guide_legend("Groups"),
size = guide_legend("Groups")) + theme_bw() + theme(legend.key = element_rect(colour = F), legend.background = element_rect(fill = NA)) + theme(axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 15), axis.title.x = element_text(size = 25, vjust = -0.8), axis.title.y = element_text(size = 25, vjust = 1.4),
legend.text = element_text(size = 13), legend.title = element_text(size = 15, face = "bold"), plot.margin = grid::unit(c(2.1, 1.1, 1.1, 1.1),
"cm")) + scale_color_gradient2()
quartz()
print(samp_PCA)
if (save == TRUE) {
ggsave(samp_PCA, file = paste("PCA Plot.tiff"), width = 7, height = 6)
}
}
}
if (!missing(values)) {
if (!missing(gene)) {
stop("Cannot specify both values and gene.", call. = FALSE)
}
if (CR == TRUE) {
warning("Cannot have a confidence region with values or genes turned on.", call. = FALSE)
}
if (!missing(colors)){
cell_colors <- colors
}
if (length(cell_colors) != 2) {
warning("Specify a vector of two colors to control color, using default colors instead.", call. = FALSE)
cell_colors[1:2] <- c("black", "yellow")
}
valuez <- data.frame(pData(cellData)[, values], stringsAsFactors = FALSE)
if (logNumeric == TRUE) {
valuez <- log2(valuez)
}
comp.cell.type1["Values"] <- valuez
if (bubble == TRUE) {
samp_PCA <- ggplot(comp.cell.type1) + geom_point(aes_string(x = colnames(comp1)[1], y = colnames(comp1)[2], fill = "Values", size = "factor(Cell_Type,levels=unique(Cell_Type),ordered=FALSE)"),
shape = 21, color = "black", alpha = alpha) + scale_size_manual(name = "Groups", values = sizes) + guides(color = guide_legend("Values"), size = guide_legend("Groups")) +
theme_bw() + theme(legend.key = element_rect(colour = F), legend.background = element_rect(fill = NA)) + theme(axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 15), axis.title.x = element_text(size = 25, vjust = -0.8), axis.title.y = element_text(size = 25, vjust = 1.4),
legend.text = element_text(size = 13), legend.title = element_text(size = 15, face = "bold"), plot.margin = grid::unit(c(2.1, 1.1, 1.1, 1.1),
"cm")) + scale_fill_gradient(low = cell_colors[1], high = cell_colors[2])
quartz()
print(samp_PCA)
if (save == TRUE) {
ggsave(samp_PCA, file = paste("PCA Plot.tiff"), width = 7, height = 6)
}
}
if (bubble == FALSE) {
samp_PCA <- ggplot(comp.cell.type1) + geom_point(aes_string(x = colnames(comp1)[1], y = colnames(comp1)[2], fill = "Values"), size = dotsize, shape = 21,
color = "black", alpha = alpha) + guides(color = guide_legend(title = "Values")) + theme_bw() + theme(legend.key = element_rect(colour = F),
legend.background = element_rect(fill = NA)) + theme(axis.text.x = element_text(size = 15), axis.text.y = element_text(size = 15), axis.title.x = element_text(size = 25,
vjust = -0.8), axis.title.y = element_text(size = 25, vjust = 1.4), legend.text = element_text(size = 13), legend.title = element_text(size = 15,
face = "bold"), plot.margin = grid::unit(c(2.1, 1.1, 1.1, 1.1), "cm")) + scale_fill_gradient(low = cell_colors[1], high = cell_colors[2])
quartz()
print(samp_PCA)
if (save == TRUE) {
ggsave(samp_PCA, file = paste("PCA Plot.tiff"), width = 7, height = 6)
}
}
}
if (!missing(gene)) {
if (!missing(values)) {
stop("Cannot specify both values and gene.", call. = FALSE)
}
if (CR == TRUE) {
warning("Cannot have a confidence region with values or genes turned on.", call. = FALSE)
}
if (!missing(gene) && !gene %in% row.names(exprs_table)) {
stop("Specified gene is not present in expression table", call. = FALSE)
}
if (!missing(colors)){
cell_colors <- colors
}
if (length(cell_colors) != 2) {
warning("Specify a vector of two colors to control color, using default colors instead", call. = FALSE)
cell_colors[1:2] <- c("black", "yellow")
}
genez <- data.frame(exprs_table[gene, ], stringsAsFactors = FALSE)[, 1]
comp.cell.type1[gene] <- genez
if (bubble == TRUE) {
samp_PCA <- ggplot(comp.cell.type1) + geom_point(aes_string(x = colnames(comp1)[1], y = colnames(comp1)[2], fill = gene, size = "factor(Cell_Type,levels=unique(Cell_Type),ordered=FALSE)"),
shape = 21, color = "black", alpha = alpha) + scale_size_manual(name = "Groups", values = sizes) + guides(color = guide_legend(title = gene),
size = guide_legend("Groups")) + theme_bw() + theme(legend.key = element_rect(colour = F), legend.background = element_rect(fill = NA)) + theme(axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 15), axis.title.x = element_text(size = 25, vjust = -0.8), axis.title.y = element_text(size = 25, vjust = 1.4),
legend.text = element_text(size = 13), legend.title = element_text(size = 15, face = "bold"), plot.margin = grid::unit(c(2.1, 1.1, 1.1, 1.1),
"cm")) + scale_fill_gradient(low = cell_colors[1], high = cell_colors[2])
quartz()
print(samp_PCA)
if (save == TRUE) {
ggsave(samp_PCA, file = paste("PCA Plot.tiff"), width = 7, height = 6)
}
}
if (bubble == FALSE) {
samp_PCA <- ggplot(comp.cell.type1) + geom_point(aes_string(x = colnames(comp1)[1], y = colnames(comp1)[2], fill = gene), size = dotsize, shape = 21,
color = "black", alpha = alpha) + guides(color = guide_legend(title = gene)) + theme_bw() + theme(legend.key = element_rect(colour = F), legend.background = element_rect(fill = NA)) +
theme(axis.text.x = element_text(size = 15), axis.text.y = element_text(size = 15), axis.title.x = element_text(size = 25, vjust = -0.8), axis.title.y = element_text(size = 25,
vjust = 1.4), legend.text = element_text(size = 13), legend.title = element_text(size = 15, face = "bold"), plot.margin = grid::unit(c(2.1,
1.1, 1.1, 1.1), "cm")) + scale_fill_gradient(low = cell_colors[1], high = cell_colors[2])
quartz()
print(samp_PCA)
if (save == TRUE) {
ggsave(samp_PCA, file = paste("PCA Plot.tiff"), width = 7, height = 6)
}
}
}
samp_PCA
}
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