R/Plotting_QC_Seq_10X.R
In samuel-marsh/scCustomize: Custom Visualizations & Functions for Streamlined Analyses of Single Cell Sequencing
Defines functions
Iterate_Barcode_Rank_Plot
Barcode_Plot
Seq_QC_Plot_Alignment_Combined
Seq_QC_Plot_Basic_Combined
Seq_QC_Plot_Antisense
Seq_QC_Plot_Exonic
Seq_QC_Plot_Intronic
Seq_QC_Plot_Intergenic
Seq_QC_Plot_Genome
Seq_QC_Plot_Transcriptome
Seq_QC_Plot_Reads_in_Cells
Seq_QC_Plot_Saturation
Seq_QC_Plot_Total_Genes
Seq_QC_Plot_UMIs
Seq_QC_Plot_Genes
Seq_QC_Plot_Number_Cells
Seq_QC_Plot_Reads_per_Cell
Documented in
Barcode_Plot
Iterate_Barcode_Rank_Plot
Seq_QC_Plot_Alignment_Combined
Seq_QC_Plot_Antisense
Seq_QC_Plot_Basic_Combined
Seq_QC_Plot_Exonic
Seq_QC_Plot_Genes
Seq_QC_Plot_Genome
Seq_QC_Plot_Intergenic
Seq_QC_Plot_Intronic
Seq_QC_Plot_Number_Cells
Seq_QC_Plot_Reads_in_Cells
Seq_QC_Plot_Reads_per_Cell
Seq_QC_Plot_Saturation
Seq_QC_Plot_Total_Genes
Seq_QC_Plot_Transcriptome
Seq_QC_Plot_UMIs
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#################### 10X SEQ QC ####################
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#' QC Plots Sequencing metrics
#'
#' Plot the mean number of reads per cell
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_basic
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Reads_per_Cell(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Reads_per_Cell <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = "{.val {plot_by}} is not a column in the provided {.code metrics_dataframe}.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Mean_Reads_per_Cell"]])) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45, hjust = 1,size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(face = "bold", size = 14),
plot.title = element_text(face = "bold", size = 18, hjust = 0.5)) +
ggtitle("Mean Reads per Cell per Sample") +
ylab('Mean Reads per Cell') +
xlab("") +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Mean_Reads_per_Cell"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45, hjust = 1,size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(face = "bold", size = 14),
plot.title = element_text(face = "bold", size = 18, hjust = 0.5)) +
scale_fill_manual(values = colors_use) +
ggtitle("Mean Reads per Cell per Sample") +
ylab('Mean Reads per Cell') +
xlab(plot_by) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics
#'
#' Plot the number of cells per sample
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_basic
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Number_Cells(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Number_Cells <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = "{.val {plot_by}} is not a column in the provided {.code metrics_dataframe}.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Estimated_Number_of_Cells"]])) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45, hjust = 1,size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(face = "bold", size = 14),
plot.title = element_text(face = "bold", size = 18, hjust = 0.5)) +
ggtitle("Cells per Sample") +
ylab('Cells') +
xlab("") +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Estimated_Number_of_Cells"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45, hjust = 1,size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(face = "bold", size = 14),
plot.title = element_text(face = "bold", size = 18, hjust = 0.5)) +
scale_fill_manual(values = colors_use) +
ggtitle("Cells per Sample") +
ylab('Cells') +
xlab(plot_by) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics
#'
#' Plot the median genes per cell per sample
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_basic
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Genes(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Genes <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = " is not a column in the provided `metrics_dataframe`.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Median_Genes_per_Cell"]])) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
ggtitle("Median Genes per Cell") +
ylab('Median Genes') +
xlab("") +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Median_Genes_per_Cell"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
scale_fill_manual(values = colors_use) +
ggtitle("Median Genes per Cell") +
ylab('Median Genes') +
xlab(plot_by) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics
#'
#' Plot the median UMIs per cell per sample
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_basic
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_UMIs(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_UMIs <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = "{.val {plot_by}} is not a column in the provided {.code metrics_dataframe}.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Median_UMI_Counts_per_Cell"]])) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
ggtitle("Median UMIs per Cell") +
ylab('Median UMIs') +
xlab("") +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Median_UMI_Counts_per_Cell"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
scale_fill_manual(values = colors_use) +
ggtitle("Median UMIs per Cell") +
ylab('Median UMIs') +
xlab(plot_by) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics
#'
#' Plot the total genes detected per sample
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_basic
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Total_Genes(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Total_Genes <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = "{.val {plot_by}} is not a column in the provided {.code metrics_dataframe}.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Total_Genes_Detected"]])) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
ggtitle("Total Genes Detected per Sample") +
ylab('Total Genes') +
xlab("") +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Total_Genes_Detected"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
scale_fill_manual(values = colors_use) +
ggtitle("Total Genes Detected per Sample") +
ylab('Total Genes') +
xlab(plot_by) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics
#'
#' Plot the sequencing saturation percentage per sample
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom scales label_percent
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_basic
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Saturation(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Saturation <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = "{.val {plot_by}} is not a column in the provided {.code metrics_dataframe}.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
# Modify dataframe
metrics_dataframe[,"Sequencing_Saturation"] <- as.numeric(gsub("%", "", metrics_dataframe[,"Sequencing_Saturation"]))
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Sequencing_Saturation"]]), color = .data[["samples_plotting"]]) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
ggtitle("Sequencing Saturation") +
ylab('Sequencing Saturation Percent') +
xlab("") +
scale_y_continuous(labels = label_percent(accuracy = 0.01, scale = 1)) +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Sequencing_Saturation"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
scale_fill_manual(values = colors_use) +
ggtitle("Sequencing Saturation") +
ylab('Sequencing Saturation Percent') +
xlab(plot_by)+
scale_y_continuous(labels = label_percent(accuracy = 1, scale = 1)) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics
#'
#' Plot the fraction of reads in cells per sample
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom scales label_percent
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_basic
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Reads_in_Cells(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Reads_in_Cells <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = "{.val {plot_by}} is not a column in the provided {.code metrics_dataframe}.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
# Modify dataframe
metrics_dataframe[,"Fraction_Reads_in_Cells"] <- as.numeric(gsub("%", "", metrics_dataframe[,"Fraction_Reads_in_Cells"]))
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Fraction_Reads_in_Cells"]]), color = .data[["samples_plotting"]]) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
ggtitle("Fraction of Reads in Cells per Sample") +
ylab('Fraction of Reads in Cells') +
xlab("") +
scale_y_continuous(labels = label_percent(accuracy = 0.01, scale = 1)) +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Fraction_Reads_in_Cells"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
scale_fill_manual(values = colors_use) +
ggtitle("Fraction of Reads in Cells per Sample") +
ylab('Fraction of Reads in Cells') +
xlab(plot_by)+
scale_y_continuous(labels = label_percent(accuracy = 1, scale = 1)) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics (Alignment)
#'
#' Plot the fraction of reads confidently mapped to transcriptome
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom scales label_percent
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_alignment
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Transcriptome(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Transcriptome <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = "{.val {plot_by}} is not a column in the provided {.code metrics_dataframe}.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
# Modify dataframe
metrics_dataframe[,"Reads_Mapped_Confidently_to_Transcriptome"] <- as.numeric(gsub("%", "", metrics_dataframe[,"Reads_Mapped_Confidently_to_Transcriptome"]))
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Reads_Mapped_Confidently_to_Transcriptome"]]), color = .data[["samples_plotting"]]) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
ggtitle("Percent of Reads Confidently Mapped to Transcriptome") +
ylab('Percent of Reads ') +
xlab("") +
scale_y_continuous(labels = label_percent(accuracy = 0.01, scale = 1)) +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Reads_Mapped_Confidently_to_Transcriptome"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
scale_fill_manual(values = colors_use) +
ggtitle("Percent of Reads Confidently Mapped to Transcriptome") +
ylab('Percent of Reads') +
xlab(plot_by) +
scale_y_continuous(labels = label_percent(accuracy = 1, scale = 1)) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics (Alignment)
#'
#' Plot the fraction of reads confidently mapped to genome
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom scales label_percent
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_alignment
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Genome(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Genome <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = "{.val {plot_by}} is not a column in the provided {.code metrics_dataframe}.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
# Modify dataframe
metrics_dataframe[,"Reads_Mapped_Confidently_to_Genome"] <- as.numeric(gsub("%", "", metrics_dataframe[,"Reads_Mapped_Confidently_to_Genome"]))
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Reads_Mapped_Confidently_to_Genome"]]), color = .data[["samples_plotting"]]) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
ggtitle("Percent of Reads Confidently Mapped to Genome") +
ylab('Percent of Reads ') +
xlab("") +
scale_y_continuous(labels = label_percent(accuracy = 0.01, scale = 1)) +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Reads_Mapped_Confidently_to_Genome"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
scale_fill_manual(values = colors_use) +
ggtitle("Percent of Reads Confidently Mapped to Genome") +
ylab('Percent of Reads') +
xlab(plot_by) +
scale_y_continuous(labels = label_percent(accuracy = 1, scale = 1)) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics (Alignment)
#'
#' Plot the fraction of reads confidently mapped to intergenic regions
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom scales label_percent
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_alignment
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Intergeneic(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Intergenic <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = "{.val {plot_by}} is not a column in the provided {.code metrics_dataframe}.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
# Modify dataframe
metrics_dataframe[,"Reads_Mapped_Confidently_to_Intergenic_Regions"] <- as.numeric(gsub("%", "", metrics_dataframe[,"Reads_Mapped_Confidently_to_Intergenic_Regions"]))
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Reads_Mapped_Confidently_to_Intergenic_Regions"]]), color = .data[["samples_plotting"]]) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
ggtitle("Percent of Reads Confidently Mapped to Intergenic Regions") +
ylab('Percent of Reads ') +
xlab("") +
scale_y_continuous(labels = label_percent(accuracy = 0.01, scale = 1)) +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Reads_Mapped_Confidently_to_Intergenic_Regions"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
scale_fill_manual(values = colors_use) +
ggtitle("Percent of Reads Confidently Mapped to Intergenic Regions") +
ylab('Percent of Reads') +
xlab(plot_by) +
scale_y_continuous(labels = label_percent(accuracy = 1, scale = 1)) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics (Alignment)
#'
#' Plot the fraction of reads confidently mapped to intronic regions
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom scales label_percent
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_alignment
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Intronic(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Intronic <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = "{.val {plot_by}} is not a column in the provided {.code metrics_dataframe}.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
# Modify dataframe
metrics_dataframe[,"Reads_Mapped_Confidently_to_Intronic_Regions"] <- as.numeric(gsub("%", "", metrics_dataframe[,"Reads_Mapped_Confidently_to_Intronic_Regions"]))
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Reads_Mapped_Confidently_to_Intronic_Regions"]]), color = .data[["samples_plotting"]]) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
ggtitle("Percent of Reads Confidently Mapped to Intronic Regions") +
ylab('Percent of Reads ') +
xlab("") +
scale_y_continuous(labels = label_percent(accuracy = 0.01, scale = 1)) +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Reads_Mapped_Confidently_to_Intronic_Regions"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
scale_fill_manual(values = colors_use) +
ggtitle("Percent of Reads Confidently Mapped to Intronic Regions") +
ylab('Percent of Reads') +
xlab(plot_by) +
scale_y_continuous(labels = label_percent(accuracy = 1, scale = 1)) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics (Alignment)
#'
#' Plot the fraction of reads confidently mapped to Exonic regions
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom scales label_percent
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_alignment
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Exonic(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Exonic <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = "{.val {plot_by}} is not a column in the provided {.code metrics_dataframe}.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
# Modify dataframe
metrics_dataframe[,"Reads_Mapped_Confidently_to_Exonic_Regions"] <- as.numeric(gsub("%", "", metrics_dataframe[,"Reads_Mapped_Confidently_to_Exonic_Regions"]))
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Reads_Mapped_Confidently_to_Exonic_Regions"]]), color = .data[["samples_plotting"]]) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
ggtitle("Percent of Reads Confidently Mapped to Exonic Regions") +
ylab('Percent of Reads ') +
xlab("") +
scale_y_continuous(labels = label_percent(accuracy = 0.01, scale = 1)) +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Reads_Mapped_Confidently_to_Exonic_Regions"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
scale_fill_manual(values = colors_use) +
ggtitle("Percent of Reads Confidently Mapped to Exonic Regions") +
ylab('Percent of Reads') +
xlab(plot_by) +
scale_y_continuous(labels = label_percent(accuracy = 1, scale = 1)) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics (Alignment)
#'
#' Plot the fraction of reads mapped Antisense to Gene
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom scales label_percent
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_alignment
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Antisense(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Antisense <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = "{.val {plot_by}} is not a column in the provided {.code metrics_dataframe}.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
# Modify dataframe
metrics_dataframe[,"Reads_Mapped_Antisense_to_Gene"] <- as.numeric(gsub("%", "", metrics_dataframe[,"Reads_Mapped_Antisense_to_Gene"]))
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Reads_Mapped_Antisense_to_Gene"]]), color = .data[["samples_plotting"]]) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
ggtitle("Percent of Reads Confidently Mapped to Antisense to Gene") +
ylab('Percent of Reads ') +
xlab("") +
scale_y_continuous(labels = label_percent(accuracy = 0.01, scale = 1)) +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Reads_Mapped_Antisense_to_Gene"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
scale_fill_manual(values = colors_use) +
ggtitle("Percent of Reads Confidently Mapped to Antisense to Gene") +
ylab('Percent of Reads') +
xlab(plot_by) +
scale_y_continuous(labels = label_percent(accuracy = 1, scale = 1)) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics (Layout)
#'
#' Plot a combined plot of the basic QC metrics from sequencing output.
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param patchwork_title Title to use for the patchworked plot output.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import ggplot2
#' @importFrom patchwork plot_layout plot_annotation
#' @importFrom rlang is_installed
#' @importFrom stringr str_wrap
#'
#' @export
#'
#' @concept seq_qc_plotting_layout
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Basic_Combined(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Basic_Combined <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
patchwork_title = "Sequencing QC Plots: Basic Cell Metrics",
significance = FALSE,
...
) {
# Create rotated axis value
if (isTRUE(x = x_lab_rotate)) {
axis_angle <- 45
} else {
axis_angle <- 0
}
# Create Plots & modify for plotting together
p1 <- Seq_QC_Plot_Number_Cells(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p1 <- p1 +
labs(title = str_wrap(p1$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
p2 <- Seq_QC_Plot_Reads_per_Cell(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p2 <- p2 + labs(title = str_wrap(p2$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
p3 <- Seq_QC_Plot_Genes(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p3 <- p3 + labs(title = str_wrap(p3$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
p4 <- Seq_QC_Plot_UMIs(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p4 <- p4 + labs(title = str_wrap(p4$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
p5 <- Seq_QC_Plot_Total_Genes(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p5 <- p5 + labs(title = str_wrap(p5$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
p6 <- Seq_QC_Plot_Saturation(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p6 <- p6 + labs(title = str_wrap(p6$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
p7 <- Seq_QC_Plot_Reads_in_Cells(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p7 <- p7 + labs(title = str_wrap(p7$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
p8 <- Seq_QC_Plot_Transcriptome(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p8 <- p8 + labs(title = str_wrap(p8$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
# Assemble plots and unifying legends
plot <- (p1 | p3 | p5 | p7) /
(p2 | p4 | p6 | p8)
plot <- plot + plot_layout(guides = 'collect') + plot_annotation(title = patchwork_title, theme = theme(plot.title = element_text(hjust = 0.5, face = "bold", size = rel(1.5))))
# Print plots
suppressMessages(print(plot))
}
#' QC Plots Sequencing metrics (Alignment) (Layout)
#'
#' Plot a combined plot of the Alignment QC metrics from sequencing output.
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param patchwork_title Title to use for the patchworked plot output.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import ggplot2
#' @importFrom patchwork plot_layout plot_annotation
#' @importFrom rlang is_installed
#' @importFrom stringr str_wrap
#'
#' @export
#'
#' @concept seq_qc_plotting_layout
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Alignment_Combined(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Alignment_Combined <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
patchwork_title = "Sequencing QC Plots: Read Alignment Metrics",
significance = FALSE,
...
) {
# Create rotated axis value
if (isTRUE(x = x_lab_rotate)) {
axis_angle <- 45
} else {
axis_angle <- 0
}
# Create Plots & modify for plotting together
p1 <- Seq_QC_Plot_Genome(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size, ...)
p1 <- p1 +
labs(title = str_wrap(p1$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
p2 <- Seq_QC_Plot_Intergenic(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p2 <- p2 + labs(title = str_wrap(p2$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
p3 <- Seq_QC_Plot_Transcriptome(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p3 <- p3 + labs(title = str_wrap(p3$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
p4 <- Seq_QC_Plot_Exonic(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p4 <- p4 + labs(title = str_wrap(p4$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
p5 <- Seq_QC_Plot_Intronic(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p5 <- p5 + labs(title = str_wrap(p5$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
p6 <- Seq_QC_Plot_Antisense(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p6 <- p6 + labs(title = str_wrap(p6$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
# Assemble plots and unifying legends
plot <- (p1 | p3 | p5) /
(p2 | p4 | p6)
plot <- plot + plot_layout(guides = 'collect') + plot_annotation(title = patchwork_title, theme = theme(plot.title = element_text(hjust = 0.5, face = "bold", size = rel(1.5))))
# Print plots
suppressMessages(print(plot))
}
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#################### Barcode Rank QC ####################
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#' Create Barcode Rank Plot
#'
#' Plot UMI vs. Barcode Rank with inflection and knee. Requires input from DropletUtils package.
#'
#' @param br_out DFrame output from \code{\link[DropletUtils]{barcodeRanks}}.
#' @param pt.size point size for plotting, default is 6.
#' @param plot_title Title for plot, default is "Barcode Ranks".
#' @param raster_dpi Pixel resolution for rasterized plots, passed to geom_scattermore().
#' Default is c(1024, 1024).
#' @param plateau numerical value at which to add vertical line designating estimated
#' empty droplet plateau (default is NULL).
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom scattermore geom_scattermore
#' @importFrom cowplot theme_cowplot
#'
#' @export
#'
#' @concept seq_qc_plotting_basic
#'
#' @examples
#' \dontrun{
#' mat <- Read10X_h5(filename = "raw_feature_bc_matrix.h5")
#'
#' br_results <- DropletUtils::barcodeRanks(mat)
#'
#' Barcode_Plot(br_out = br_results)
#' }
#'
Barcode_Plot <- function(
br_out,
pt.size = 6,
plot_title = "Barcode Ranks",
raster_dpi = c(1024, 1024),
plateau = NULL
) {
# Check br_out is correct
if (!inherits(x = br_out, what = "DFrame")) {
cli_abort(message = c("{.code br_out} must be object of class {.field DFrame}.",
"i" = "Ensure {.code br_out} is output of {.code {.field DropletUtils::barcodeRanks}}."))
}
if (!all(c("knee", "inflection") %in% names(x = br_out@metadata)) && !all(c("rank", "total", "fitted") %in% names(x = br_out@listData))) {
cli_abort(message = c("{.code br_out} appears to be missing necessarily information.",
"i" = "Ensure {.code br_out} is output of {.code {.field DropletUtils::barcodeRanks}} and no errors occured when running code."))
}
plot <- ggplot(data = data.frame(br_out@listData), aes(x = .data[["rank"]], y = .data[["total"]])) +
geom_scattermore(pointsize = pt.size, pixels = raster_dpi) +
scale_y_log10() +
scale_x_log10() +
theme_cowplot() +
geom_line(mapping = aes(x = .data[["rank"]], y = .data[["fitted"]], color = "red"), show.legend = FALSE) +
geom_hline(yintercept = br_out@metadata$knee, linetype = "dashed", color = "dodgerblue") +
geom_hline(yintercept = br_out@metadata$inflection, linetype = "dashed", color = "forestgreen") +
annotate("text", x = 1, y = br_out@metadata$knee, label = paste0("Knee (", br_out@metadata$knee, ")"), vjust = -0.5, hjust = 0) +
annotate("text", x = 1, y = br_out@metadata$inflection, label = paste0("Inflection (", br_out@metadata$inflection, ")"), vjust = -0.5, hjust = 0) +
ylab("UMIs") +
xlab("Barcode Rank") +
ggtitle(plot_title) +
theme(plot.title = element_text(hjust = 0.5))
# Add plateau if specified
if (!is.null(x = plateau)) {
plot <- plot +
geom_vline(xintercept = plateau, linetype = "dashed", color = "dodgerblue") +
annotate("text", x = plateau, y = max(br_out$total), label = paste0("Plateau (", plateau, ")"), vjust = -0.5, hjust = -0.05)
}
# return plot
return(plot)
}
#' Iterative Barcode Rank Plots
#'
#' Read data, calculate `DropletUtils::barcodeRanks`, create barcode rank plots, and outout single PDF output.
#'
#' @param dir_path_h5 path to parent directory (if `multi_directory = TRUE`) or directory containing
#' all h5 files (if `multi_directory = FALSE`).
#' @param multi_directory logical, whether or not all h5 files are in their own subdirectories or in a
#' single directory (default is TRUE; each in own subdirectory (e.g. output from Cell Ranger)).
#' @param h5_filename Either the file name of h5 file (if `multi_directory = TRUE`) or the shared
#' suffix (if `multi_directory = FALSE`)
#' @param cellranger_multi logical, whether the outputs to be read are from Cell Ranger `multi` as opposed
#' to Cell Ranger `count` (default is FALSE). Only valid if `multi_directory = FALSE`.
#' @param parallel logical, should files be read in parallel (default is FALSE).
#' @param num_cores Number of cores to use in parallel if `parallel = TRUE`.
#' @param file_path file path to use for saving PDF output.
#' @param file_name Name of PDF output file.
#' @param pt.size point size for plotting, default is 6.
#' @param raster_dpi Pixel resolution for rasterized plots, passed to geom_scattermore().
#' Default is c(1024, 1024).
#' @param plateau numerical values at which to add vertical line designating estimated
#' empty droplet plateau (default is NULL). Must be vector equal in length to number of samples.
#' @param ... Additional parameters passed to `Read10X_h5_Multi_Directory` or `Read10X_h5_GEO`.
#'
#' @return pdf document
#'
#' @import cli
#' @import ggplot2
#' @importFrom grDevices dev.off pdf
#' @importFrom pbapply pblapply pboptions
#' @importFrom utils txtProgressBar setTxtProgressBar
#'
#' @export
#'
#' @concept seq_qc_plotting_basic
#'
#' @examples
#' \dontrun{
#' Iterate_Barcode_Rank_Plot(dir_path_h5 = "H5_PATH/", multi_directory = TRUE,
#' h5_filename = "raw_feature_bc_matrix", parallel = TRUE, num_cores = 12, file_path = "OUTPUT_PATH",
#' file_name = "Barcode_Rank_Plots")
#' }
#'
Iterate_Barcode_Rank_Plot <- function(
dir_path_h5,
multi_directory = TRUE,
h5_filename = "raw_feature_bc_matrix.h5",
cellranger_multi = FALSE,
parallel = FALSE,
num_cores = NULL,
file_path = NULL,
file_name = NULL,
pt.size = 6,
raster_dpi = c(1024, 1024),
plateau = NULL,
...
) {
DropletUtils_check <- is_installed(pkg = "DropletUtils")
if (!DropletUtils_check[1]) {
cli_abort(message = c(
"Please install the {.val DropletUtils} package to use {.code Create_10X_H5}",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}BiocManager{symbol$dquote_right})`}",
"{.field `BiocManager::install({symbol$dquote_left}DropletUtils{symbol$dquote_right})`}",
"----------------------------------------"
))
}
# Set file_path before path check if current dir specified as opposed to leaving set to NULL
if (!is.null(x = file_path) && file_path == "") {
file_path <- NULL
}
# Check file path is valid
if (!is.null(x = file_path)) {
if (!dir.exists(paths = file_path)) {
cli_abort(message = "Provided {.code file_path}: {symbol$dquote_left}{.field {file_path}}{symbol$dquote_right} does not exist.")
}
}
# Check if file name provided
if (is.null(x = file_name)) {
cli_abort(message = "No file name provided. Please provide a file name using {.code file_name}.")
}
# Set file type for single pdf option
file_type <- ".pdf"
# Read in data
if (multi_directory) {
all_mat <- Read10X_h5_Multi_Directory(base_path = dir_path_h5, h5_filename = h5_filename, parallel = parallel, num_cores = num_cores, ...)
} else {
all_mat <- Read10X_h5_GEO(data_dir = dir_path_h5, parallel = parallel, num_cores = num_cores, shared_suffix = h5_filename, ...)
}
cli_inform(message = "{.field Calculating Barcode Rank Statistics}")
pboptions(char = "=")
barcode_ranks_list <- pblapply(1:length(x = all_mat), function(x) {
br_file <- DropletUtils::barcodeRanks(m = all_mat[[x]])
})
sample_names <- names(x = all_mat)
rm(all_mat)
gc()
num_samples <- length(x = barcode_ranks_list)
if (!is.null(x = plateau) && length(x = plateau) != num_samples) {
cli_abort(message = "The number of values for plateau ({.field {length(x = plateau)}}) must be equal to the number of samples ({.field {num_samples}}).")
}
# Single PDF option
cli_inform(message = "{.field Generating plots}")
pboptions(char = "=")
all_plots <- pblapply(1:num_samples, function(j) {
Barcode_Plot(br_out = barcode_ranks_list[[j]], pt.size = pt.size, plot_title = sample_names[j], raster_dpi = raster_dpi, plateau = plateau[j])
})
cli_inform(message = "{.field Saving plots to file}")
# Save plots
pdf(paste(file_path, file_name, file_type, sep=""))
pb <- txtProgressBar(min = 0, max = length(all_plots), style = 3, file = stderr())
for (i in 1:length(all_plots)) {
print(all_plots[[i]])
setTxtProgressBar(pb = pb, value = i)
}
close(con = pb)
dev.off()
}
samuel-marsh/scCustomize documentation built on Dec. 20, 2024, 7:41 a.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions Iterate_Barcode_Rank_Plot Barcode_Plot Seq_QC_Plot_Alignment_Combined Seq_QC_Plot_Basic_Combined Seq_QC_Plot_Antisense Seq_QC_Plot_Exonic Seq_QC_Plot_Intronic Seq_QC_Plot_Intergenic Seq_QC_Plot_Genome Seq_QC_Plot_Transcriptome Seq_QC_Plot_Reads_in_Cells Seq_QC_Plot_Saturation Seq_QC_Plot_Total_Genes Seq_QC_Plot_UMIs Seq_QC_Plot_Genes Seq_QC_Plot_Number_Cells Seq_QC_Plot_Reads_per_Cell
Documented in Barcode_Plot Iterate_Barcode_Rank_Plot Seq_QC_Plot_Alignment_Combined Seq_QC_Plot_Antisense Seq_QC_Plot_Basic_Combined Seq_QC_Plot_Exonic Seq_QC_Plot_Genes Seq_QC_Plot_Genome Seq_QC_Plot_Intergenic Seq_QC_Plot_Intronic Seq_QC_Plot_Number_Cells Seq_QC_Plot_Reads_in_Cells Seq_QC_Plot_Reads_per_Cell Seq_QC_Plot_Saturation Seq_QC_Plot_Total_Genes Seq_QC_Plot_Transcriptome Seq_QC_Plot_UMIs
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#################### 10X SEQ QC ####################
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#' QC Plots Sequencing metrics
#'
#' Plot the mean number of reads per cell
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_basic
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Reads_per_Cell(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Reads_per_Cell <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = "{.val {plot_by}} is not a column in the provided {.code metrics_dataframe}.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Mean_Reads_per_Cell"]])) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45, hjust = 1,size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(face = "bold", size = 14),
plot.title = element_text(face = "bold", size = 18, hjust = 0.5)) +
ggtitle("Mean Reads per Cell per Sample") +
ylab('Mean Reads per Cell') +
xlab("") +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Mean_Reads_per_Cell"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45, hjust = 1,size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(face = "bold", size = 14),
plot.title = element_text(face = "bold", size = 18, hjust = 0.5)) +
scale_fill_manual(values = colors_use) +
ggtitle("Mean Reads per Cell per Sample") +
ylab('Mean Reads per Cell') +
xlab(plot_by) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics
#'
#' Plot the number of cells per sample
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_basic
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Number_Cells(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Number_Cells <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = "{.val {plot_by}} is not a column in the provided {.code metrics_dataframe}.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Estimated_Number_of_Cells"]])) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45, hjust = 1,size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(face = "bold", size = 14),
plot.title = element_text(face = "bold", size = 18, hjust = 0.5)) +
ggtitle("Cells per Sample") +
ylab('Cells') +
xlab("") +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Estimated_Number_of_Cells"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45, hjust = 1,size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(face = "bold", size = 14),
plot.title = element_text(face = "bold", size = 18, hjust = 0.5)) +
scale_fill_manual(values = colors_use) +
ggtitle("Cells per Sample") +
ylab('Cells') +
xlab(plot_by) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics
#'
#' Plot the median genes per cell per sample
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_basic
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Genes(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Genes <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = " is not a column in the provided `metrics_dataframe`.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Median_Genes_per_Cell"]])) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
ggtitle("Median Genes per Cell") +
ylab('Median Genes') +
xlab("") +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Median_Genes_per_Cell"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
scale_fill_manual(values = colors_use) +
ggtitle("Median Genes per Cell") +
ylab('Median Genes') +
xlab(plot_by) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics
#'
#' Plot the median UMIs per cell per sample
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_basic
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_UMIs(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_UMIs <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = "{.val {plot_by}} is not a column in the provided {.code metrics_dataframe}.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Median_UMI_Counts_per_Cell"]])) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
ggtitle("Median UMIs per Cell") +
ylab('Median UMIs') +
xlab("") +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Median_UMI_Counts_per_Cell"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
scale_fill_manual(values = colors_use) +
ggtitle("Median UMIs per Cell") +
ylab('Median UMIs') +
xlab(plot_by) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics
#'
#' Plot the total genes detected per sample
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_basic
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Total_Genes(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Total_Genes <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = "{.val {plot_by}} is not a column in the provided {.code metrics_dataframe}.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Total_Genes_Detected"]])) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
ggtitle("Total Genes Detected per Sample") +
ylab('Total Genes') +
xlab("") +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Total_Genes_Detected"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
scale_fill_manual(values = colors_use) +
ggtitle("Total Genes Detected per Sample") +
ylab('Total Genes') +
xlab(plot_by) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics
#'
#' Plot the sequencing saturation percentage per sample
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom scales label_percent
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_basic
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Saturation(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Saturation <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = "{.val {plot_by}} is not a column in the provided {.code metrics_dataframe}.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
# Modify dataframe
metrics_dataframe[,"Sequencing_Saturation"] <- as.numeric(gsub("%", "", metrics_dataframe[,"Sequencing_Saturation"]))
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Sequencing_Saturation"]]), color = .data[["samples_plotting"]]) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
ggtitle("Sequencing Saturation") +
ylab('Sequencing Saturation Percent') +
xlab("") +
scale_y_continuous(labels = label_percent(accuracy = 0.01, scale = 1)) +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Sequencing_Saturation"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
scale_fill_manual(values = colors_use) +
ggtitle("Sequencing Saturation") +
ylab('Sequencing Saturation Percent') +
xlab(plot_by)+
scale_y_continuous(labels = label_percent(accuracy = 1, scale = 1)) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics
#'
#' Plot the fraction of reads in cells per sample
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom scales label_percent
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_basic
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Reads_in_Cells(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Reads_in_Cells <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = "{.val {plot_by}} is not a column in the provided {.code metrics_dataframe}.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
# Modify dataframe
metrics_dataframe[,"Fraction_Reads_in_Cells"] <- as.numeric(gsub("%", "", metrics_dataframe[,"Fraction_Reads_in_Cells"]))
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Fraction_Reads_in_Cells"]]), color = .data[["samples_plotting"]]) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
ggtitle("Fraction of Reads in Cells per Sample") +
ylab('Fraction of Reads in Cells') +
xlab("") +
scale_y_continuous(labels = label_percent(accuracy = 0.01, scale = 1)) +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Fraction_Reads_in_Cells"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
scale_fill_manual(values = colors_use) +
ggtitle("Fraction of Reads in Cells per Sample") +
ylab('Fraction of Reads in Cells') +
xlab(plot_by)+
scale_y_continuous(labels = label_percent(accuracy = 1, scale = 1)) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics (Alignment)
#'
#' Plot the fraction of reads confidently mapped to transcriptome
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom scales label_percent
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_alignment
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Transcriptome(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Transcriptome <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = "{.val {plot_by}} is not a column in the provided {.code metrics_dataframe}.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
# Modify dataframe
metrics_dataframe[,"Reads_Mapped_Confidently_to_Transcriptome"] <- as.numeric(gsub("%", "", metrics_dataframe[,"Reads_Mapped_Confidently_to_Transcriptome"]))
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Reads_Mapped_Confidently_to_Transcriptome"]]), color = .data[["samples_plotting"]]) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
ggtitle("Percent of Reads Confidently Mapped to Transcriptome") +
ylab('Percent of Reads ') +
xlab("") +
scale_y_continuous(labels = label_percent(accuracy = 0.01, scale = 1)) +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Reads_Mapped_Confidently_to_Transcriptome"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
scale_fill_manual(values = colors_use) +
ggtitle("Percent of Reads Confidently Mapped to Transcriptome") +
ylab('Percent of Reads') +
xlab(plot_by) +
scale_y_continuous(labels = label_percent(accuracy = 1, scale = 1)) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics (Alignment)
#'
#' Plot the fraction of reads confidently mapped to genome
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom scales label_percent
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_alignment
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Genome(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Genome <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = "{.val {plot_by}} is not a column in the provided {.code metrics_dataframe}.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
# Modify dataframe
metrics_dataframe[,"Reads_Mapped_Confidently_to_Genome"] <- as.numeric(gsub("%", "", metrics_dataframe[,"Reads_Mapped_Confidently_to_Genome"]))
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Reads_Mapped_Confidently_to_Genome"]]), color = .data[["samples_plotting"]]) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
ggtitle("Percent of Reads Confidently Mapped to Genome") +
ylab('Percent of Reads ') +
xlab("") +
scale_y_continuous(labels = label_percent(accuracy = 0.01, scale = 1)) +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Reads_Mapped_Confidently_to_Genome"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
scale_fill_manual(values = colors_use) +
ggtitle("Percent of Reads Confidently Mapped to Genome") +
ylab('Percent of Reads') +
xlab(plot_by) +
scale_y_continuous(labels = label_percent(accuracy = 1, scale = 1)) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics (Alignment)
#'
#' Plot the fraction of reads confidently mapped to intergenic regions
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom scales label_percent
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_alignment
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Intergeneic(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Intergenic <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = "{.val {plot_by}} is not a column in the provided {.code metrics_dataframe}.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
# Modify dataframe
metrics_dataframe[,"Reads_Mapped_Confidently_to_Intergenic_Regions"] <- as.numeric(gsub("%", "", metrics_dataframe[,"Reads_Mapped_Confidently_to_Intergenic_Regions"]))
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Reads_Mapped_Confidently_to_Intergenic_Regions"]]), color = .data[["samples_plotting"]]) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
ggtitle("Percent of Reads Confidently Mapped to Intergenic Regions") +
ylab('Percent of Reads ') +
xlab("") +
scale_y_continuous(labels = label_percent(accuracy = 0.01, scale = 1)) +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Reads_Mapped_Confidently_to_Intergenic_Regions"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
scale_fill_manual(values = colors_use) +
ggtitle("Percent of Reads Confidently Mapped to Intergenic Regions") +
ylab('Percent of Reads') +
xlab(plot_by) +
scale_y_continuous(labels = label_percent(accuracy = 1, scale = 1)) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics (Alignment)
#'
#' Plot the fraction of reads confidently mapped to intronic regions
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom scales label_percent
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_alignment
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Intronic(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Intronic <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = "{.val {plot_by}} is not a column in the provided {.code metrics_dataframe}.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
# Modify dataframe
metrics_dataframe[,"Reads_Mapped_Confidently_to_Intronic_Regions"] <- as.numeric(gsub("%", "", metrics_dataframe[,"Reads_Mapped_Confidently_to_Intronic_Regions"]))
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Reads_Mapped_Confidently_to_Intronic_Regions"]]), color = .data[["samples_plotting"]]) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
ggtitle("Percent of Reads Confidently Mapped to Intronic Regions") +
ylab('Percent of Reads ') +
xlab("") +
scale_y_continuous(labels = label_percent(accuracy = 0.01, scale = 1)) +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Reads_Mapped_Confidently_to_Intronic_Regions"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
scale_fill_manual(values = colors_use) +
ggtitle("Percent of Reads Confidently Mapped to Intronic Regions") +
ylab('Percent of Reads') +
xlab(plot_by) +
scale_y_continuous(labels = label_percent(accuracy = 1, scale = 1)) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics (Alignment)
#'
#' Plot the fraction of reads confidently mapped to Exonic regions
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom scales label_percent
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_alignment
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Exonic(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Exonic <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = "{.val {plot_by}} is not a column in the provided {.code metrics_dataframe}.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
# Modify dataframe
metrics_dataframe[,"Reads_Mapped_Confidently_to_Exonic_Regions"] <- as.numeric(gsub("%", "", metrics_dataframe[,"Reads_Mapped_Confidently_to_Exonic_Regions"]))
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Reads_Mapped_Confidently_to_Exonic_Regions"]]), color = .data[["samples_plotting"]]) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
ggtitle("Percent of Reads Confidently Mapped to Exonic Regions") +
ylab('Percent of Reads ') +
xlab("") +
scale_y_continuous(labels = label_percent(accuracy = 0.01, scale = 1)) +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Reads_Mapped_Confidently_to_Exonic_Regions"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
scale_fill_manual(values = colors_use) +
ggtitle("Percent of Reads Confidently Mapped to Exonic Regions") +
ylab('Percent of Reads') +
xlab(plot_by) +
scale_y_continuous(labels = label_percent(accuracy = 1, scale = 1)) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics (Alignment)
#'
#' Plot the fraction of reads mapped Antisense to Gene
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom ggbeeswarm geom_quasirandom
#' @importFrom scales label_percent
#' @importFrom rlang is_installed
#' @importFrom utils combn
#'
#' @export
#'
#' @concept seq_qc_plotting_alignment
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Antisense(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Antisense <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
significance = FALSE,
...
) {
if (!plot_by %in% colnames(x = metrics_dataframe)) {
cli_abort(message = "{.val {plot_by}} is not a column in the provided {.code metrics_dataframe}.")
}
# Change plot_by to character vector to make significance functions show all comparisons
if (inherits(x = metrics_dataframe[[plot_by]], what = "factor")) {
stats_dataframe <- metrics_dataframe
stats_dataframe[[plot_by]] <- as.character(stats_dataframe[[plot_by]])
} else {
stats_dataframe <- metrics_dataframe
}
# Create color palette if null and check valid if provided
length_plotby <- length(x = unique(x = metrics_dataframe[[plot_by]]))
if (is.null(x = colors_use) && !plot_by == "sample_id") {
if (length_plotby <= 8) {
colors_use <- Dark2_Pal()
} else {
colors_use <- DiscretePalette_scCustomize(num_colors = length_plotby, palette = "polychrome")
}
} else {
if (length(x = colors_use) < length_plotby && !plot_by == "sample_id") {
cli_abort(message = c("Not enough colors provided.",
"i" = "The number of colors supplied: {.field {length(x = colors_use)}}, is less than the number of groups in {.val {plot_by}} column: {.field {length_plotby}}.")
)
} else {
colors_use <- colors_use
}
}
# Modify dataframe
metrics_dataframe[,"Reads_Mapped_Antisense_to_Gene"] <- as.numeric(gsub("%", "", metrics_dataframe[,"Reads_Mapped_Antisense_to_Gene"]))
if (plot_by == "sample_id") {
metrics_dataframe$samples_plotting <- "Samples"
plot <- ggplot(metrics_dataframe, aes(x = .data[["samples_plotting"]], y = .data[["Reads_Mapped_Antisense_to_Gene"]]), color = .data[["samples_plotting"]]) +
geom_boxplot(fill = "white", outlier.color = NA) +
geom_quasirandom() +
ggtitle("Percent of Reads Confidently Mapped to Antisense to Gene") +
ylab('Percent of Reads ') +
xlab("") +
scale_y_continuous(labels = label_percent(accuracy = 0.01, scale = 1)) +
theme_ggprism_mod()
} else {
plot <- ggplot(metrics_dataframe, aes(x=.data[[plot_by]], y = .data[["Reads_Mapped_Antisense_to_Gene"]], fill = .data[[plot_by]])) +
geom_boxplot(fill = "white") +
geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = dot_size) +
scale_fill_manual(values = colors_use) +
ggtitle("Percent of Reads Confidently Mapped to Antisense to Gene") +
ylab('Percent of Reads') +
xlab(plot_by) +
scale_y_continuous(labels = label_percent(accuracy = 1, scale = 1)) +
theme_ggprism_mod()
}
if (isTRUE(x = x_lab_rotate)) {
plot <- plot + theme_ggprism_mod(axis_text_angle = 45)
}
if (isTRUE(x = significance)) {
ggpubr_check <- is_installed(pkg = "ggpubr")
if (isFALSE(x = ggpubr_check)) {
cli_abort(message = c(
"Please install the {.val ggpubr} package to calculate/plot significance values.",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}ggpubr{symbol$dquote_right})`}",
"----------------------------------------"
))
}
if (length(x = unique(x = stats_dataframe[[plot_by]])) < 2) {
cli_abort(message = "Cannot calculate statistics when {.val {plot_by}} column contains less than 2 groups.")
}
groups <- unique(x = stats_dataframe[[plot_by]])
comparisons <- combn(groups, 2)
comparisons <- data.frame(comparisons, stringsAsFactors = FALSE)
comparisons <- lapply(1:length(x = colnames(x = comparisons)), function(x){
indiv_comp <- as.character(x = comparisons[[x]])
})
plot <- plot + ggpubr::stat_compare_means(comparisons = comparisons, ...)
}
return(plot)
}
#' QC Plots Sequencing metrics (Layout)
#'
#' Plot a combined plot of the basic QC metrics from sequencing output.
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param patchwork_title Title to use for the patchworked plot output.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import ggplot2
#' @importFrom patchwork plot_layout plot_annotation
#' @importFrom rlang is_installed
#' @importFrom stringr str_wrap
#'
#' @export
#'
#' @concept seq_qc_plotting_layout
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Basic_Combined(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Basic_Combined <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
patchwork_title = "Sequencing QC Plots: Basic Cell Metrics",
significance = FALSE,
...
) {
# Create rotated axis value
if (isTRUE(x = x_lab_rotate)) {
axis_angle <- 45
} else {
axis_angle <- 0
}
# Create Plots & modify for plotting together
p1 <- Seq_QC_Plot_Number_Cells(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p1 <- p1 +
labs(title = str_wrap(p1$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
p2 <- Seq_QC_Plot_Reads_per_Cell(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p2 <- p2 + labs(title = str_wrap(p2$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
p3 <- Seq_QC_Plot_Genes(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p3 <- p3 + labs(title = str_wrap(p3$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
p4 <- Seq_QC_Plot_UMIs(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p4 <- p4 + labs(title = str_wrap(p4$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
p5 <- Seq_QC_Plot_Total_Genes(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p5 <- p5 + labs(title = str_wrap(p5$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
p6 <- Seq_QC_Plot_Saturation(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p6 <- p6 + labs(title = str_wrap(p6$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
p7 <- Seq_QC_Plot_Reads_in_Cells(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p7 <- p7 + labs(title = str_wrap(p7$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
p8 <- Seq_QC_Plot_Transcriptome(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p8 <- p8 + labs(title = str_wrap(p8$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
# Assemble plots and unifying legends
plot <- (p1 | p3 | p5 | p7) /
(p2 | p4 | p6 | p8)
plot <- plot + plot_layout(guides = 'collect') + plot_annotation(title = patchwork_title, theme = theme(plot.title = element_text(hjust = 0.5, face = "bold", size = rel(1.5))))
# Print plots
suppressMessages(print(plot))
}
#' QC Plots Sequencing metrics (Alignment) (Layout)
#'
#' Plot a combined plot of the Alignment QC metrics from sequencing output.
#'
#' @param metrics_dataframe data.frame contain Cell Ranger QC Metrics (see \code{\link{Read_Metrics_10X}}).
#' @param plot_by Grouping factor for the plot. Default is to plot as single group with single point per sample.
#' @param colors_use colors to use for plot if plotting by group. Defaults to RColorBrewer Dark2 palette if
#' less than 8 groups and `DiscretePalette_scCustomize(palette = "polychrome")` if more than 8.
#' @param dot_size size of the dots plotted if `plot_by` is not `sample_id` Default is 1.
#' @param x_lab_rotate logical. Whether to rotate the axes labels on the x-axis. Default is FALSE.
#' @param patchwork_title Title to use for the patchworked plot output.
#' @param significance logical. Whether to calculate and plot p-value comparisons when plotting by
#' grouping factor. Default is FALSE.
#' @param ... Other variables to pass to `ggpubr::stat_compare_means` when doing significance testing.
#'
#' @return A ggplot object
#'
#' @import ggplot2
#' @importFrom patchwork plot_layout plot_annotation
#' @importFrom rlang is_installed
#' @importFrom stringr str_wrap
#'
#' @export
#'
#' @concept seq_qc_plotting_layout
#'
#' @examples
#' \dontrun{
#' Seq_QC_Plot_Alignment_Combined(metrics_dataframe = metrics)
#' }
#'
Seq_QC_Plot_Alignment_Combined <- function(
metrics_dataframe,
plot_by = "sample_id",
colors_use = NULL,
dot_size = 1,
x_lab_rotate = FALSE,
patchwork_title = "Sequencing QC Plots: Read Alignment Metrics",
significance = FALSE,
...
) {
# Create rotated axis value
if (isTRUE(x = x_lab_rotate)) {
axis_angle <- 45
} else {
axis_angle <- 0
}
# Create Plots & modify for plotting together
p1 <- Seq_QC_Plot_Genome(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size, ...)
p1 <- p1 +
labs(title = str_wrap(p1$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
p2 <- Seq_QC_Plot_Intergenic(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p2 <- p2 + labs(title = str_wrap(p2$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
p3 <- Seq_QC_Plot_Transcriptome(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p3 <- p3 + labs(title = str_wrap(p3$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
p4 <- Seq_QC_Plot_Exonic(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p4 <- p4 + labs(title = str_wrap(p4$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
p5 <- Seq_QC_Plot_Intronic(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p5 <- p5 + labs(title = str_wrap(p5$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
p6 <- Seq_QC_Plot_Antisense(metrics_dataframe = metrics_dataframe, plot_by = plot_by, colors_use = colors_use, significance = significance, dot_size = dot_size,)
p6 <- p6 + labs(title = str_wrap(p6$labels$title, 18)) +
theme_ggprism_mod(base_size = 10, axis_text_angle = axis_angle)
# Assemble plots and unifying legends
plot <- (p1 | p3 | p5) /
(p2 | p4 | p6)
plot <- plot + plot_layout(guides = 'collect') + plot_annotation(title = patchwork_title, theme = theme(plot.title = element_text(hjust = 0.5, face = "bold", size = rel(1.5))))
# Print plots
suppressMessages(print(plot))
}
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#################### Barcode Rank QC ####################
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#' Create Barcode Rank Plot
#'
#' Plot UMI vs. Barcode Rank with inflection and knee. Requires input from DropletUtils package.
#'
#' @param br_out DFrame output from \code{\link[DropletUtils]{barcodeRanks}}.
#' @param pt.size point size for plotting, default is 6.
#' @param plot_title Title for plot, default is "Barcode Ranks".
#' @param raster_dpi Pixel resolution for rasterized plots, passed to geom_scattermore().
#' Default is c(1024, 1024).
#' @param plateau numerical value at which to add vertical line designating estimated
#' empty droplet plateau (default is NULL).
#'
#' @return A ggplot object
#'
#' @import cli
#' @import ggplot2
#' @importFrom scattermore geom_scattermore
#' @importFrom cowplot theme_cowplot
#'
#' @export
#'
#' @concept seq_qc_plotting_basic
#'
#' @examples
#' \dontrun{
#' mat <- Read10X_h5(filename = "raw_feature_bc_matrix.h5")
#'
#' br_results <- DropletUtils::barcodeRanks(mat)
#'
#' Barcode_Plot(br_out = br_results)
#' }
#'
Barcode_Plot <- function(
br_out,
pt.size = 6,
plot_title = "Barcode Ranks",
raster_dpi = c(1024, 1024),
plateau = NULL
) {
# Check br_out is correct
if (!inherits(x = br_out, what = "DFrame")) {
cli_abort(message = c("{.code br_out} must be object of class {.field DFrame}.",
"i" = "Ensure {.code br_out} is output of {.code {.field DropletUtils::barcodeRanks}}."))
}
if (!all(c("knee", "inflection") %in% names(x = br_out@metadata)) && !all(c("rank", "total", "fitted") %in% names(x = br_out@listData))) {
cli_abort(message = c("{.code br_out} appears to be missing necessarily information.",
"i" = "Ensure {.code br_out} is output of {.code {.field DropletUtils::barcodeRanks}} and no errors occured when running code."))
}
plot <- ggplot(data = data.frame(br_out@listData), aes(x = .data[["rank"]], y = .data[["total"]])) +
geom_scattermore(pointsize = pt.size, pixels = raster_dpi) +
scale_y_log10() +
scale_x_log10() +
theme_cowplot() +
geom_line(mapping = aes(x = .data[["rank"]], y = .data[["fitted"]], color = "red"), show.legend = FALSE) +
geom_hline(yintercept = br_out@metadata$knee, linetype = "dashed", color = "dodgerblue") +
geom_hline(yintercept = br_out@metadata$inflection, linetype = "dashed", color = "forestgreen") +
annotate("text", x = 1, y = br_out@metadata$knee, label = paste0("Knee (", br_out@metadata$knee, ")"), vjust = -0.5, hjust = 0) +
annotate("text", x = 1, y = br_out@metadata$inflection, label = paste0("Inflection (", br_out@metadata$inflection, ")"), vjust = -0.5, hjust = 0) +
ylab("UMIs") +
xlab("Barcode Rank") +
ggtitle(plot_title) +
theme(plot.title = element_text(hjust = 0.5))
# Add plateau if specified
if (!is.null(x = plateau)) {
plot <- plot +
geom_vline(xintercept = plateau, linetype = "dashed", color = "dodgerblue") +
annotate("text", x = plateau, y = max(br_out$total), label = paste0("Plateau (", plateau, ")"), vjust = -0.5, hjust = -0.05)
}
# return plot
return(plot)
}
#' Iterative Barcode Rank Plots
#'
#' Read data, calculate `DropletUtils::barcodeRanks`, create barcode rank plots, and outout single PDF output.
#'
#' @param dir_path_h5 path to parent directory (if `multi_directory = TRUE`) or directory containing
#' all h5 files (if `multi_directory = FALSE`).
#' @param multi_directory logical, whether or not all h5 files are in their own subdirectories or in a
#' single directory (default is TRUE; each in own subdirectory (e.g. output from Cell Ranger)).
#' @param h5_filename Either the file name of h5 file (if `multi_directory = TRUE`) or the shared
#' suffix (if `multi_directory = FALSE`)
#' @param cellranger_multi logical, whether the outputs to be read are from Cell Ranger `multi` as opposed
#' to Cell Ranger `count` (default is FALSE). Only valid if `multi_directory = FALSE`.
#' @param parallel logical, should files be read in parallel (default is FALSE).
#' @param num_cores Number of cores to use in parallel if `parallel = TRUE`.
#' @param file_path file path to use for saving PDF output.
#' @param file_name Name of PDF output file.
#' @param pt.size point size for plotting, default is 6.
#' @param raster_dpi Pixel resolution for rasterized plots, passed to geom_scattermore().
#' Default is c(1024, 1024).
#' @param plateau numerical values at which to add vertical line designating estimated
#' empty droplet plateau (default is NULL). Must be vector equal in length to number of samples.
#' @param ... Additional parameters passed to `Read10X_h5_Multi_Directory` or `Read10X_h5_GEO`.
#'
#' @return pdf document
#'
#' @import cli
#' @import ggplot2
#' @importFrom grDevices dev.off pdf
#' @importFrom pbapply pblapply pboptions
#' @importFrom utils txtProgressBar setTxtProgressBar
#'
#' @export
#'
#' @concept seq_qc_plotting_basic
#'
#' @examples
#' \dontrun{
#' Iterate_Barcode_Rank_Plot(dir_path_h5 = "H5_PATH/", multi_directory = TRUE,
#' h5_filename = "raw_feature_bc_matrix", parallel = TRUE, num_cores = 12, file_path = "OUTPUT_PATH",
#' file_name = "Barcode_Rank_Plots")
#' }
#'
Iterate_Barcode_Rank_Plot <- function(
dir_path_h5,
multi_directory = TRUE,
h5_filename = "raw_feature_bc_matrix.h5",
cellranger_multi = FALSE,
parallel = FALSE,
num_cores = NULL,
file_path = NULL,
file_name = NULL,
pt.size = 6,
raster_dpi = c(1024, 1024),
plateau = NULL,
...
) {
DropletUtils_check <- is_installed(pkg = "DropletUtils")
if (!DropletUtils_check[1]) {
cli_abort(message = c(
"Please install the {.val DropletUtils} package to use {.code Create_10X_H5}",
"i" = "This can be accomplished with the following commands: ",
"----------------------------------------",
"{.field `install.packages({symbol$dquote_left}BiocManager{symbol$dquote_right})`}",
"{.field `BiocManager::install({symbol$dquote_left}DropletUtils{symbol$dquote_right})`}",
"----------------------------------------"
))
}
# Set file_path before path check if current dir specified as opposed to leaving set to NULL
if (!is.null(x = file_path) && file_path == "") {
file_path <- NULL
}
# Check file path is valid
if (!is.null(x = file_path)) {
if (!dir.exists(paths = file_path)) {
cli_abort(message = "Provided {.code file_path}: {symbol$dquote_left}{.field {file_path}}{symbol$dquote_right} does not exist.")
}
}
# Check if file name provided
if (is.null(x = file_name)) {
cli_abort(message = "No file name provided. Please provide a file name using {.code file_name}.")
}
# Set file type for single pdf option
file_type <- ".pdf"
# Read in data
if (multi_directory) {
all_mat <- Read10X_h5_Multi_Directory(base_path = dir_path_h5, h5_filename = h5_filename, parallel = parallel, num_cores = num_cores, ...)
} else {
all_mat <- Read10X_h5_GEO(data_dir = dir_path_h5, parallel = parallel, num_cores = num_cores, shared_suffix = h5_filename, ...)
}
cli_inform(message = "{.field Calculating Barcode Rank Statistics}")
pboptions(char = "=")
barcode_ranks_list <- pblapply(1:length(x = all_mat), function(x) {
br_file <- DropletUtils::barcodeRanks(m = all_mat[[x]])
})
sample_names <- names(x = all_mat)
rm(all_mat)
gc()
num_samples <- length(x = barcode_ranks_list)
if (!is.null(x = plateau) && length(x = plateau) != num_samples) {
cli_abort(message = "The number of values for plateau ({.field {length(x = plateau)}}) must be equal to the number of samples ({.field {num_samples}}).")
}
# Single PDF option
cli_inform(message = "{.field Generating plots}")
pboptions(char = "=")
all_plots <- pblapply(1:num_samples, function(j) {
Barcode_Plot(br_out = barcode_ranks_list[[j]], pt.size = pt.size, plot_title = sample_names[j], raster_dpi = raster_dpi, plateau = plateau[j])
})
cli_inform(message = "{.field Saving plots to file}")
# Save plots
pdf(paste(file_path, file_name, file_type, sep=""))
pb <- txtProgressBar(min = 0, max = length(all_plots), style = 3, file = stderr())
for (i in 1:length(all_plots)) {
print(all_plots[[i]])
setTxtProgressBar(pb = pb, value = i)
}
close(con = pb)
dev.off()
}
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