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clonalstatplot.R
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#' ClonalVolumePlot
#'
#' @param data The product of [scRepertoire::combineTCR], [scRepertoire::combineTCR], or
#' [scRepertoire::combineExpression].
#' @param clone_call How to call the clone - VDJC gene (gene), CDR3 nucleotide (nt),
#' CDR3 amino acid (aa), VDJC gene + CDR3 nucleotide (strict) or a custom variable
#' in the data
#' @param chain indicate if both or a specific chain should be used - e.g. "both",
#' "TRA", "TRG", "IGH", "IGL"
#' @param scale Whether to use clone proportion or clone size for the plot.
#' @param plot_type The type of plot to use. Default is "bar".
#' Possible values are "bar", "box", and "violin".
#' When "box" or "violin" is used, the data will be broken down by the Sample and plotted
#' for each group.
#' @param x The column name in the meta data to use as the x-axis. Default: "Sample"
#' @param ylab The y-axis label.
#' @param group_by The column name in the meta data to group the cells. Default: NULL
#' @param facet_by The column name in the meta data to facet the plots. Default: NULL
#' @param split_by The column name in the meta data to split the plots. Default: NULL
#' @param order The order of the x-axis items or groups. Default is an empty list.
#' It should be a list of values. The names are the column names, and the values are the order.
#' @param ... Other arguments passed to the specific plot function.
#' * For `bar` plot, see [plotthis::BarPlot()].
#' * For `box` plot, see [plotthis::BoxPlot()].
#' * For `violin` plot, see [plotthis::ViolinPlot()].
#' @return A ggplot object or a list if `combine` is FALSE
#' @importFrom tidyr separate
#' @importFrom plotthis BarPlot BoxPlot ViolinPlot
#' @importFrom scRepertoire clonalQuant
#' @export
#' @examples
#' set.seed(8525)
#' data(contig_list, package = "scRepertoire")
#' data <- scRepertoire::combineTCR(contig_list)
#' data <- scRepertoire::addVariable(data,
#' variable.name = "Type",
#' variables = sample(c("B", "L"), 8, replace = TRUE)
#' )
#' data <- scRepertoire::addVariable(data,
#' variable.name = "Sex",
#' variables = sample(c("M", "F"), 8, replace = TRUE)
#' )
#'
#' ClonalVolumePlot(data)
#' ClonalVolumePlot(data, x = "Type")
#' ClonalVolumePlot(data, x = "Type", order = list(Type = c("L", "B")))
#' ClonalVolumePlot(data, x = c("Type", "Sex"), scale = TRUE)
#' ClonalVolumePlot(data, x = "Type", group_by = "Sex", position = "stack")
#' ClonalVolumePlot(data,
#' plot_type = "box", x = "Type", comparisons = TRUE,
#' group_by = "Sex"
#' )
#' ClonalVolumePlot(data, plot_type = "violin", x = "Type", add_box = TRUE)
#'
#' # on a Seurat object
#' data(scRep_example, package = "scRepertoire")
#' data(contig_list, package = "scRepertoire")
#' combined <- scRepertoire::combineTCR(contig_list,
#' samples = c("P17B", "P17L", "P18B", "P18L", "P19B", "P19L", "P20B", "P20L")
#' )
#' sobj <- scRepertoire::combineExpression(combined, scRep_example)
#' ClonalVolumePlot(sobj)
#' ClonalVolumePlot(sobj, x = "seurat_clusters")
#' ClonalVolumePlot(sobj, group_by = "seurat_clusters")
#' ClonalVolumePlot(sobj, x = "seurat_clusters", plot_type = "box")
ClonalVolumePlot <- function(
data, clone_call = "aa", chain = "both", scale = FALSE,
plot_type = c("bar", "box", "violin"), x = "Sample", group_by = NULL,
facet_by = NULL, split_by = NULL, order = list(), ylab = NULL, ...) {
plot_type <- match.arg(plot_type)
if (plot_type %in% c("box", "violin")) {
all_groupings <- unique(c("Sample", x, group_by, facet_by, split_by))
} else {
all_groupings <- unique(c(x, group_by, facet_by, split_by))
}
data <- merge_clonal_groupings(data, all_groupings)
data <- clonalQuant(data,
cloneCall = clone_call, chain = chain, scale = scale,
group.by = ".group", exportTable = TRUE
)
# restore the groups
data <- separate(data, ".group", into = all_groupings, sep = " // ")
for (group in all_groupings) {
if (!is.null(order[[group]])) {
data[[group]] <- factor(data[[group]], levels = order[[group]])
}
}
if (scale) {
data$scaled <- data$scaled / 100
}
if (plot_type == "bar") {
BarPlot(data,
x = x, y = ifelse(scale, "scaled", "contigs"),
group_by = group_by, facet_by = facet_by, split_by = split_by,
ylab = ylab %||% ifelse(scale, "Fraction of Unique Clones", "Number of Unique Clones"),
...
)
} else if (plot_type == "box") {
BoxPlot(data,
x = x, y = ifelse(scale, "scaled", "contigs"), group_by = group_by,
facet_by = facet_by, split_by = split_by,
ylab = ylab %||% ifelse(scale, "Fraction of Unique Clones", "Number of Unique Clones"),
...
)
} else {
ViolinPlot(data,
x = x, y = ifelse(scale, "scaled", "contigs"),
group_by = group_by, facet_by = facet_by, split_by = split_by,
ylab = ylab %||% ifelse(scale, "Fraction of Unique Clones", "Number of Unique Clones"),
...
)
}
}
#' ClonalAbundancePlot
#'
#' @description Plot the count or density of the clones at different abundance levels.
#'
#' @param data The product of [scRepertoire::combineTCR], [scRepertoire::combineTCR], or
#' [scRepertoire::combineExpression].
#' @param clone_call How to call the clone - VDJC gene (gene), CDR3 nucleotide (nt),
#' CDR3 amino acid (aa), VDJC gene + CDR3 nucleotide (strict) or a custom variable
#' in the data
#' @param chain indicate if both or a specific chain should be used - e.g. "both",
#' "TRA", "TRG", "IGH", "IGL"
#' @param plot_type The type of plot to use. Default is "trend".
#' Possible values are "trend", "histogram" and "density".
#' @param trend_skip_zero Whether to skip the zero values in the trend line. Default is TRUE.
#' @param binwidth The binwidth for the histogram plot. Default is 0.1.
#' @param group_by The column name in the meta data to group the cells. Default: "Sample"
#' @param group_by_sep The separator to use when combining the group_by columns. Default: "_"
#' @param facet_by The column name in the meta data to facet the plots. Default: NULL
#' @param split_by The column name in the meta data to split the plots. Default: NULL
#' @param order The order of the x-axis items or groups. Default is an empty list.
#' It should be a list of values. The names are the column names, and the values are the order.
#' @param bw The smoothing bandwidth to be used for density plots. Default is 0.5.
#' @param xlab The x-axis label. Default is "Abundance".
#' @param ylab The y-axis label. Default is "Number of Clones" for trend and histogram, and
#' "Density of Clones" for density.
#' @param xtrans The transformation to apply to the x-axis. Default is "log10".
#' @param ytrans The transformation to apply to the y-axis. Default is "identity".
#' @param theme_args The theme arguments to be passed to the plot function.
#' @param ... Other arguments passed to the specific plot function.
#' * For `trend` plot, see [plotthis::Histogram()].
#' * For `histogram` plot, see [plotthis::Histogram()].
#' * For `density` plot, see [plotthis::DensityPlot()].
#' @return A ggplot object or a list if `combine` is FALSE
#' @importFrom tidyr full_seq complete
#' @importFrom dplyr group_modify
#' @importFrom plotthis Histogram DensityPlot
#' @importFrom scRepertoire clonalAbundance
#' @export
#' @examples
#' \donttest{
#' set.seed(8525)
#' data(contig_list, package = "scRepertoire")
#' data <- scRepertoire::combineTCR(contig_list)
#' data <- scRepertoire::addVariable(data,
#' variable.name = "Type",
#' variables = sample(c("B", "L"), 8, replace = TRUE)
#' )
#' data <- scRepertoire::addVariable(data,
#' variable.name = "Sex",
#' variables = sample(c("M", "F"), 8, replace = TRUE)
#' )
#'
#' ClonalAbundancePlot(data)
#' ClonalAbundancePlot(data, ytrans = "log10")
#' ClonalAbundancePlot(data, plot_type = "histogram")
#' ClonalAbundancePlot(data, plot_type = "histogram", add_trend = TRUE, trend_skip_zero = TRUE)
#' ClonalAbundancePlot(data, plot_type = "density")
#' }
ClonalAbundancePlot <- function(
data, clone_call = "aa", chain = "both", xtrans = "log10", ytrans = "identity",
plot_type = c("trend", "histogram", "density"), binwidth = 0.1, trend_skip_zero = TRUE,
bw = 0.5, group_by = "Sample", group_by_sep = "_", facet_by = NULL, split_by = NULL,
order = list(), xlab = "Abundance", ylab = NULL, theme_args = list(), ...) {
plot_type <- match.arg(plot_type)
all_groupings <- unique(c(group_by, facet_by, split_by))
data <- merge_clonal_groupings(data, all_groupings)
if (length(all_groupings) > 0) {
data <- clonalAbundance(data,
cloneCall = clone_call, chain = chain,
group.by = ".group", exportTable = TRUE
)
# restore the groups
data <- separate(data, ".group", into = all_groupings, sep = " // ")
for (group in all_groupings) {
if (!is.null(order[[group]])) {
data[[group]] <- factor(data[[group]], levels = order[[group]])
}
}
} else {
data <- clonalAbundance(data,
cloneCall = clone_call, chain = chain,
group.by = group_by, exportTable = TRUE
)
}
if (plot_type == "trend") {
theme_args$panel.grid.major.y <- theme_args$panel.grid.major.y %||%
element_line(color = "grey", linetype = 2)
Histogram(data,
x = "Abundance", group_by = group_by, group_by_sep = group_by_sep,
facet_by = facet_by, split_by = split_by, xtrans = xtrans, xlab = xlab, binwidth = binwidth,
ytrans = ytrans, ylab = ylab %||% "Number of Clones", use_trend = TRUE,
trend_skip_zero = trend_skip_zero, theme_args = theme_args, ...
)
} else if (plot_type == "histogram") {
Histogram(data,
x = "Abundance", group_by = group_by, group_by_sep = group_by_sep,
facet_by = facet_by, split_by = split_by, xtrans = xtrans, xlab = xlab, binwidth = binwidth,
trend_skip_zero = trend_skip_zero, ytrans = ytrans, theme_args = theme_args,
ylab = ylab %||% "Number of Clones", ...
)
} else if (plot_type == "density") {
DensityPlot(data,
x = "Abundance", group_by = group_by, group_by_sep = group_by_sep,
facet_by = facet_by, split_by = split_by, xtrans = xtrans, xlab = xlab,
ytrans = ytrans, ylab = ylab %||% "Density of Clones", theme_args = theme_args,
bw = bw, ...
)
}
}
#' ClonalLengthPlot
#'
#' @description Plot the length distribution of the CDR3 sequences
#' @param data The product of [scRepertoire::combineTCR], [scRepertoire::combineTCR], or
#' [scRepertoire::combineExpression].
#' @param clone_call How to call the clone - only "nt" or "aa" is supported.
#' @param chain indicate if both or a specific chain should be used - e.g. "both",
#' "TRA", "TRB", "TRD", "TRG", "IGH", or "IGL" to specify a specific chain.
#' @param plot_type The type of plot to use. Default is "bar".
#' Possible values are "box", "violin" and "density".
#' @param x_nbreaks The number of breaks for the x-axis. Default is 10.
#' @param group_by The column name in the meta data to group the cells. Default: "Sample"
#' @param facet_by The column name in the meta data to facet the plots. Default: NULL
#' @param split_by The column name in the meta data to split the plots. Default: NULL
#' @param order The order of the groups. Default is an empty list.
#' It should be a list of values. The names are the column names, and the values are the order.
#' @param xlab The x-axis label.
#' @param ylab The y-axis label.
#' @param position The position of the bars for bar plot on the x-axis. Default is "dodge".
#' @param ... Other arguments passed to the specific plot function.
#' * For `bar` plot, see [plotthis::BarPlot()].
#' * For `box` plot, see [plotthis::BoxPlot()].
#' * For `violin` plot, see [plotthis::ViolinPlot()].
#' * For `density` plot, see [plotthis::DensityPlot()].
#' @return A ggplot object or a list if `combine` is FALSE
#' @importFrom stats quantile
#' @importFrom rlang syms
#' @importFrom dplyr summarise n
#' @importFrom tidyr separate
#' @importFrom ggplot2 element_blank scale_x_discrete element_line
#' @importFrom plotthis BarPlot
#' @importFrom scRepertoire clonalLength
#' @export
#' @examples
#' \donttest{
#' set.seed(8525)
#' data(contig_list, package = "scRepertoire")
#' data <- scRepertoire::combineTCR(contig_list)
#' data <- scRepertoire::addVariable(data, variable.name = "Type",
#' variables = sample(c("B", "L"), 8, replace = TRUE))
#' data <- scRepertoire::addVariable(data, variable.name = "Sex",
#' variables = sample(c("M", "F"), 8, replace = TRUE))
#'
#' ClonalLengthPlot(data)
#' ClonalLengthPlot(data, plot_type = "box")
#' ClonalLengthPlot(data, clone_call = "nt", plot_type = "violin", chain = "TRB",
#' group_by = "Type", comparisons = TRUE)
#' ClonalLengthPlot(data, plot_type = "density", chain = "TRA")
#' }
ClonalLengthPlot <- function(
data, clone_call = "aa", chain = "both", plot_type = c("bar", "box", "violin", "density"),
x_nbreaks = 10, group_by = "Sample", order = list(), xlab = "Length", ylab = NULL,
position = "dodge", facet_by = NULL, split_by = NULL, ...) {
plot_type <- match.arg(plot_type)
if (plot_type %in% c("box", "violin")) {
all_groupings <- unique(c("Sample", group_by, facet_by, split_by))
} else {
all_groupings <- unique(c(group_by, facet_by, split_by))
}
data <- merge_clonal_groupings(data, all_groupings)
if (identical(all_groupings, "Sample")) {
data <- clonalLength(data, cloneCall = clone_call, chain = chain, exportTable = TRUE)
data$Sample <- data$values
} else {
data <- clonalLength(data,
cloneCall = clone_call, chain = chain, group.by = ".group",
exportTable = TRUE
)
data <- separate(data, ".group", into = all_groupings, sep = " // ")
}
# restore the groups
for (group in all_groupings) {
if (!is.null(order[[group]])) {
data[[group]] <- factor(data[[group]], levels = order[[group]])
}
}
if (plot_type == "density") {
rawdata <- data
}
data <- data %>%
dplyr::group_by(!!!syms(c("length", all_groupings))) %>%
summarise(.n = n(), .groups = "drop")
default_ylab <- ifelse(clone_call == "aa", "Number of CDR3 (AA)", "Number of CDR3 (NT)")
if (plot_type == "bar") {
args <- list(...)
args$data <- data
args$x <- "length"
args$y <- ".n"
args$group_by <- group_by
args$facet_by <- facet_by
args$split_by <- split_by
args$position <- position
args$xlab <- "Length"
args$ylab <- ylab %||% default_ylab
args$theme_args <- args$theme_args %||% list()
args$theme_args$panel.grid.major.x <- element_blank()
breaks <- quantile(data$length, probs = seq(0, 1, length.out = x_nbreaks), type = 3)
if (is.null(args$split_by)) {
suppressMessages({
do.call(BarPlot, args) + scale_x_discrete(breaks = breaks)
})
} else {
suppressMessages({
do.call(BarPlot, args) & scale_x_discrete(breaks = breaks)
})
}
} else if (plot_type == "box") {
args <- list(...)
args$data <- data
args$x <- "length"
args$y <- ".n"
args$group_by <- if (identical(group_by, "Sample")) NULL else group_by
args$facet_by <- facet_by
args$split_by <- split_by
args$xlab <- "Length"
args$ylab <- ylab %||% default_ylab
args$theme_args <- args$theme_args %||% list()
if (clone_call == "nt" || chain == "both") {
args$theme_args$panel.grid.major.x <- args$theme_args$panel.grid.major.x %||% element_blank()
}
args$theme_args$panel.grid.major.y <- args$theme_args$panel.grid.major.y %||%
element_line(color = "grey", linetype = 2)
args$legend.position <- args$legend.position %||% ifelse(is.null(args$split_by), "none", "right")
do.call(BoxPlot, args)
} else if (plot_type == "violin") {
args <- list(...)
args$data <- data
args$x <- "length"
args$y <- ".n"
args$group_by <- if (identical(group_by, "Sample")) NULL else group_by
args$facet_by <- facet_by
args$split_by <- split_by
args$xlab <- "Length"
args$ylab <- ylab %||% default_ylab
args$theme_args <- args$theme_args %||% list()
if (clone_call == "nt" || chain == "both") {
args$theme_args$panel.grid.major.x <- args$theme_args$panel.grid.major.x %||% element_blank()
}
args$theme_args$panel.grid.major.y <- args$theme_args$panel.grid.major.y %||%
element_line(color = "grey", linetype = 2)
args$legend.position <- args$legend.position %||% ifelse(is.null(args$split_by), "none", "right")
do.call(ViolinPlot, args)
} else if (plot_type == "density") {
DensityPlot(
rawdata,
x = "length",
group_by = group_by,
facet_by = facet_by,
split_by = split_by,
xlab = xlab %||% "Length",
ylab = ylab %||% ifelse(clone_call == "aa", "Density of CDR3 (AA)", "Density of CDR3 (NT)"),
...
)
}
}
#' DummyClonalScatterPlot
#'
#' @description Function to plot the scatter plot of the clonal data for a dummy group pair.
#' @param df The data frame with the clonal data.
#' The data frame should have the columns: group_by, 'count', 'fraction'.
#' @return A ggplot object
#' @importFrom rlang sym
#' @importFrom stats cor.test
#' @importFrom circlize colorRamp2
#' @importFrom dplyr case_when mutate distinct summarise group_by first
#' @importFrom ggplot2 geom_segment aes element_blank theme scale_x_continuous scale_y_continuous
#' @importFrom ggplot2 guides geom_point scale_fill_manual guide_legend
#' @importFrom ggnewscale new_scale_fill
#' @importFrom plotthis ScatterPlot palette_this
#' @keywords internal
DummyClonalScatterPlot <- function(df, title, group_by, scatter_cor, size_by, ...) {
if (nrow(df) == 0) {
stop("No data found for the group_by: ", group_by,
". Did you specify the correct 'group_by'/'groups' parameters?")
}
pair <- unique(as.character(df[[group_by]]))
if (length(pair) != 2) {
stop("The group_by should have exactly 2 unique values.")
}
exponent <- function(x) { floor(log10(abs(x))) }
mantissa <- function(x) {
mant <- log10(abs(x))
10 ^ (mant - floor(mant))
}
df <- pivot_wider(df, names_from = group_by, values_from = c("count", "fraction"), values_fill = 0)
suf1 <- paste0("count_", pair[1])
suf2 <- paste0("count_", pair[2])
df <- df[df[[suf1]] > 0 | df[[suf2]] > 0, , drop = FALSE]
N <- nrow(df)
dual <- which(df[[suf1]] > 0 & df[[suf2]] > 0)
if (length(dual) <= 2) {
test <- list(estimate = NA, p.value = NA)
} else {
test <- cor.test(log(df[[suf1]][dual]), log(df[[suf2]][dual]), method = scatter_cor)
}
sum_counts1 <- sum(df[[suf1]])
sum_counts2 <- sum(df[[suf2]])
counts1_norm <- jitter(1 + df[[suf1]], amount = 0.25) / sum_counts1
counts2_norm <- jitter(1 + df[[suf2]], amount = 0.25) / sum_counts2
# Avoid some points always overlaying each other
oo <- sample(length(counts1_norm))
plotdata <- data.frame(x = counts1_norm[oo], y = counts2_norm[oo])
names(plotdata) <- pair
n_singlet1 <- sum(df[[suf1]] == 1 & df[[suf2]] == 0)
n_singlet2 <- sum(df[[suf1]] == 0 & df[[suf2]] == 1)
n_expanded1 <- sum(df[[suf1]] > 1 & df[[suf2]] == 0)
n_expanded2 <- sum(df[[suf1]] == 0 & df[[suf2]] > 1)
n_dual1 <- sum(df[[suf1]] > 0 & df[[suf2]] > 0 & df[[suf1]] > df[[suf2]])
n_dual2 <- sum(df[[suf1]] > 0 & df[[suf2]] > 0 & df[[suf1]] < df[[suf2]])
n_dual <- sum(df[[suf1]] > 0 & df[[suf2]] > 0 & df[[suf1]] == df[[suf2]])
labels <- c(
paste0(pair[1], " Singlet (", n_singlet1, ")"),
paste0(pair[1], " Expanded (", n_expanded1, ")"),
paste0(pair[2], " Singlet (", n_singlet2, ")"),
paste0(pair[2], " Expanded (", n_expanded2, ")"),
paste0("Dual (", pair[2], " < ", pair[1], ") (", n_dual1, ")"),
paste0("Dual (", pair[2], " > ", pair[1], ") (", n_dual2, ")"),
paste0("Dual (Equal) (", n_dual, ")")
)
plotdata <- mutate(
plotdata,
Type = case_when(
df[[suf1]][oo] == 1 & df[[suf2]][oo] == 0 ~ 1,
df[[suf1]][oo] > 1 & df[[suf2]][oo] == 0 ~ 3,
df[[suf1]][oo] == 0 & df[[suf2]][oo] > 1 ~ 9,
df[[suf1]][oo] == 0 & df[[suf2]][oo] == 1 ~ 11,
df[[suf1]][oo] > df[[suf2]][oo] ~ 5,
df[[suf1]][oo] < df[[suf2]][oo] ~ 7,
TRUE ~ 6
),
TypeName = case_when(
Type == 1 ~ labels[1],
Type == 3 ~ labels[2],
Type == 9 ~ labels[4],
Type == 11 ~ labels[3],
Type == 5 ~ labels[5],
Type == 7 ~ labels[6],
TRUE ~ labels[7]
),
Max_Size = pmax(df[[suf1]][oo], df[[suf2]][oo]),
Total_Size = df[[suf1]][oo] + df[[suf2]][oo],
# Make sure color similar in each category
NumType = !!sym("Type") + scales::rescale(!!sym("Total_Size"), to = c(0, 1))
)
xbreaks <- c(
1 / sum_counts1,
0.001 + 1 / sum_counts1,
0.01 + 1 / sum_counts1,
0.1 + 1 / sum_counts1
)
ybreaks <- c(
1 / sum_counts2,
0.001 + 1 / sum_counts2,
0.01 + 1 / sum_counts2,
0.1 + 1 / sum_counts2
)
minx <- min(plotdata[[pair[1]]], na.rm = TRUE)
miny <- min(plotdata[[pair[1]]], na.rm = TRUE)
maxx <- max(plotdata[[pair[2]]], na.rm = TRUE)
maxy <- max(plotdata[[pair[2]]], na.rm = TRUE)
n_formatted <- formatC(length(oo), format = "f", big.mark = ",", digits = 0)
r_formatted <- format(test$estimate, digits = 2, scientific = F)
if (is.na(test$p.value)) {
subtitle <- bquote(
italic(N) == .(N) ~ ~ italic(N)[dual] == .(length(dual)) ~ ~ italic(r) ==
.(r_formatted) ~ ~ italic(p) == "NA"
)
} else if (test$p.value < 1e-4) {
P_mant <- format(mantissa(test$p.value), digits = 2)
P_exp <- exponent(test$p.value)
subtitle <- bquote(
italic(N) == .(N) ~ ~ italic(N)[dual] == .(length(dual)) ~ ~ italic(r) ==
.(r_formatted) ~ ~ italic(p) == .(P_mant) %*% 10^.(P_exp)
)
} else {
P_formatted <- format(test$p.value, digits = 2)
subtitle <- bquote(
italic(N) == .(N) ~ ~ italic(N)[dual] == .(length(dual)) ~ ~ italic(r) ==
.(r_formatted) ~ ~ italic(p) == .(P_formatted)
)
}
colfun <- colorRamp2(
seq(min(plotdata$NumType, na.rm = TRUE), max(plotdata$NumType, na.rm = TRUE), length.out = 100),
palette_this(palette = "Spectral", palcolor = NULL)
)
label_df <- plotdata %>% group_by(!!sym("TypeName")) %>%
summarise(
x = first(!!sym(pair[1])),
y = first(!!sym(pair[2])),
Type = mean(!!sym("Type"), na.rm = TRUE), .groups = "drop")
label_df$TypeName <- factor(label_df$TypeName, levels = labels)
label_df <- label_df[order(label_df$TypeName), , drop = FALSE]
p <- ScatterPlot(
plotdata, x = pair[1], y = pair[2], color_by = "NumType",
title = if (identical(title, "...")) NULL else title, subtitle = subtitle,
border_color = TRUE,
size_by = ifelse(size_by == "max", "Max_Size", "Total_Size"),
size_name = ifelse(size_by == "max", "Max Size", "Total Size"),
aspect.ratio = 1, ...) +
guides(color = "none") +
new_scale_fill() +
geom_point(data = label_df, aes(x = !!sym("x"), y = !!sym("y"), fill = !!sym("TypeName")), shape = 21, alpha = 0) +
scale_fill_manual(
values = colfun(label_df$Type),
breaks = label_df$TypeName,
guide = guide_legend(
title = "Category",
order = 9,
override.aes = list(alpha = 1, color = "grey90", size = 3)
)) +
theme(panel.grid.major = element_blank()) +
geom_segment(
data = data.frame(
# diagnal, horizontal, vertical, horizontal short, vertical short
x = c(1.5 / sum_counts1, minx, 1.5 / sum_counts1, minx, 2.5 / sum_counts1),
xend = c(maxx, maxx, 1.5 / sum_counts1, 1.5 / sum_counts1, 2.5 / sum_counts1),
y = c(1.5 / sum_counts2, 1.5 / sum_counts2, miny, 2.5 / sum_counts2, miny),
yend = c(maxy, 1.5 / sum_counts2, maxy, 2.5 / sum_counts2, 1.5 / sum_counts2)
),
aes(x = !!sym("x"), y = !!sym("y"), xend = !!sym("xend"), yend = !!sym("yend")), color = "gray90"
)
# remove the x, y scales
p$scales$scales[[4]] <- NULL
p$scales$scales[[4]] <- NULL # list shrinks after removing one element
p + scale_x_continuous(trans = "log2", limits = c(minx, maxx), breaks = xbreaks,
labels = c("0", "0.001", "0.01", "0.1")) +
scale_y_continuous(trans = "log2", limits = c(miny, maxy), breaks = ybreaks,
labels = c("0", "0.001", "0.01", "0.1"))
}
#' ClonalResidencyPlot
#'
#' @description Plot the residency of the clones in different samples.
#' @param data The product of [scRepertoire::combineTCR], [scRepertoire::combineTCR], or
#' [scRepertoire::combineExpression].
#' @param clone_call How to call the clone - VDJC gene (gene), CDR3 nucleotide (nt),
#' CDR3 amino acid (aa), VDJC gene + CDR3 nucleotide (strict) or a custom variable
#' in the data
#' @param chain indicate if both or a specific chain should be used - e.g. "both",
#' "TRA", "TRG", "IGH", "IGL"
#' @param plot_type The type of plot to use. Default is "scatter".
#' Possible values are "scatter", "venn", and "upset".
#' @param group_by The column name in the meta data to group the cells. Default: "Sample"
#' @param groups The groups to compare. Default is NULL.
#' If NULL, all the groups in `group_by` will be compared.
#' Note that for "scatter" plot, only two groups can be compared.
#' @param facet_by The column name in the meta data to facet the plots. Default: NULL
#' @param split_by The column name in the meta data to split the plots. Default: NULL
#' @param split_by_sep The separator used to concatenate the split_by when multiple columns are used.
#' @param scatter_cor The correlation method to use for the scatter plot. Default is "pearson".
#' @param scatter_size_by The size of the points in the scatter plot. Default is "max".
#' Possible values are "max" and "total".
#' * "max" - The max size of the clone in the two groups.
#' * "total" - The total size of the clone in the two groups.
#' @param order The order of the x-axis items or groups. Default is an empty list.
#' It should be a list of values. The names are the column names, and the values are the order.
#' @param combine Whether to combine the plots into a single plot. Default is TRUE.
#' @param nrow The number of rows in the combined plot. Default is NULL.
#' @param ncol The number of columns in the combined plot. Default is NULL.
#' @param byrow Whether to fill the combined plot by row. Default is TRUE.
#' @param ... Other arguments passed to the specific plot function.
#' * For `scatter` plot, see [plotthis::ScatterPlot()].
#' * For `venn` plot, see [plotthis::VennDiagram()].
#' * For `upset` plot, see [plotthis::UpsetPlot()].
#' @return A ggplot object or a list if `combine` is FALSE
#' @export
#' @importFrom utils combn
#' @importFrom dplyr distinct rename_with select across starts_with ends_with
#' @importFrom tidyr pivot_longer separate pivot_wider unite
#' @importFrom scRepertoire clonalScatter
#' @importFrom plotthis ScatterPlot VennDiagram UpsetPlot
#' @examples
#' \donttest{
#' set.seed(8525)
#' data(contig_list, package = "scRepertoire")
#' data <- scRepertoire::combineTCR(contig_list,
#' samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L"))
#' data <- scRepertoire::addVariable(data,
#' variable.name = "Type",
#' variables = rep(c("B", "L"), 4)
#' )
#' data <- scRepertoire::addVariable(data,
#' variable.name = "Subject",
#' variables = rep(c("P17", "P18", "P19", "P20"), each = 2)
#' )
#'
#' ClonalResidencyPlot(data, groups = c("P18B", "P18L"))
#' ClonalResidencyPlot(data, group_by = "Type", split_by = "Subject")
#' ClonalResidencyPlot(data, plot_type = "venn", groups = c("B", "L"), group_by = "Type",
#' split_by = "Subject")
#' ClonalResidencyPlot(data, plot_type = "upset", groups = c("P18B", "P18L"))
#' }
ClonalResidencyPlot <- function(
data, clone_call = "aa", chain = "both", plot_type = c("scatter", "venn", "upset"),
group_by = "Sample", groups = NULL, facet_by = NULL, split_by = NULL, split_by_sep = "_",
scatter_cor = "pearson", scatter_size_by = c("max", "total"),
order = list(), combine = TRUE, nrow = NULL, ncol = NULL, byrow = TRUE, ...
) {
stopifnot("ClonalResidencyPlot supports only a single group_by column" = length(group_by) == 1)
stopifnot("'facet_by' is not supported for 'ClonalResidencyPlot'" = is.null(facet_by))
plot_type <- match.arg(plot_type)
scatter_size_by <- match.arg(scatter_size_by)
all_groupings <- unique(c(group_by, facet_by, split_by))
data <- clonal_size_data(data, clone_call, chain, all_groupings)
# restore the groups
for (group in all_groupings) {
if (!is.null(order[[group]])) {
data[[group]] <- factor(data[[group]], levels = order[[group]])
}
}
groups <- groups %||% unique(data[[group_by]])
if (plot_type == "scatter") {
if (!is.null(split_by)) {
data <- unite(data, ".split", split_by, sep = split_by_sep, remove = FALSE)
} else {
data$.split <- "..."
}
data <- split(data, data$.split)
spdata <- list()
if (!is.list(groups)) {
groups <- as.list(as.data.frame(combn(groups, 2, simplify = TRUE)))
}
for (spname in names(data)) {
sdata <- data[[spname]]
for (gp in groups) {
gname <- paste(gp, collapse = " - ")
gname <- ifelse(identical(spname, "..."), gname, paste(spname, gname, sep = ": "))
spdata[[gname]] <- sdata[sdata[[group_by]] %in% gp, , drop = FALSE]
}
}
if (is.null(nrow) && is.null(ncol) && isTRUE(byrow) && length(groups) > 1 && length(data) > 1) {
nrow <- length(data)
}
data <- spdata
rm(spdata)
} else {
groups <- unique(unlist(groups))
}
if (plot_type == "scatter") {
plots <- lapply(names(data), function(nm) {
DummyClonalScatterPlot(data[[nm]], nm, group_by, scatter_cor, scatter_size_by, ...)
})
combine_plots(plots, combine = combine, nrow = nrow, ncol = ncol, byrow = byrow)
} else if (plot_type == "venn") {
if (length(groups) > 4) {
stop("Too many groups for venn plot. Please use 'upset' plot instead.")
}
data <- data[data[[group_by]] %in% groups, , drop = FALSE]
data <- data[data$count > 0, , drop = FALSE]
if (!is.null(split_by)) {
data <- unite(data, ".split", split_by, sep = split_by_sep, remove = FALSE)
}
# Calculate the # singlets for each group
label_fun <- function(df) {
label <- c()
for (i in 1:nrow(df)) {
if (grepl("/", df$id[i], fixed = TRUE)) {
label <- c(label, df$count[i])
} else {
indicator <- data$CloneID %in% df$item[[i]] &
data[[group_by]] == df$name[i] &
data$count == 1
if (!is.null(split_by)) {
indicator <- indicator & data$.split == df$.split[i]
}
ns <- data[indicator, , drop = FALSE]
label <- c(label, paste0(df$count[i], "\n(singlets: ", nrow(ns), ")"))
}
}
label
}
VennDiagram(data,
in_form = "long", id_by = "CloneID", group_by = group_by, label = label_fun,
split_by = split_by, split_by_sep = split_by_sep, ...
)
} else if (plot_type == "upset") {
data$fraction <- NULL
data <- data[data[[group_by]] %in% groups, , drop = FALSE]
data <- data[data$count > 0, , drop = FALSE]
data <- data %>%
pivot_wider(names_from = !!sym("group_by"), names_prefix = "count_", values_from = !!sym("count"), values_fill = 0) %>%
mutate(across(
.cols = starts_with("count_"),
.names = "{.col} Singlet",
.fns = ~ .x == 1
)) %>%
mutate(across(
.cols = starts_with("count_") & !ends_with(" Singlet"),
.names = "{.col} Expanded",
.fns = ~ .x > 1
)) %>%
dplyr::select(!starts_with("count_") | ends_with(" Singlet") | ends_with(" Expanded")) %>%
rename_with(
function(x) substring(x, 7),
.cols = starts_with("count_")
)
UpsetPlot(data, in_form = "wide", id_by = "CloneID", split_by = split_by, split_by_sep = split_by_sep, ...)
}
}
#' ClonalCompositionPlot
#'
#' @description Plot the composition of the clones in different samples/groups.
#' @param data The product of [scRepertoire::combineTCR], [scRepertoire::combineTCR], or
#' [scRepertoire::combineExpression].
#' @param clone_call How to call the clone - VDJC gene (gene), CDR3 nucleotide (nt),
#' CDR3 amino acid (aa), VDJC gene + CDR3 nucleotide (strict) or a custom variable
#' in the data
#' @param chain indicate if both or a specific chain should be used - e.g. "both",
#' "TRA", "TRG", "IGH", "IGL"
#' @param method The method of plot to use. Default is "homeostasis".
#' Possible values are "homeostasis", "homeo", "rel", "top", and "rare".
#' * "homeostasis" - Plot the homeostasis/relative abundance of the clones. The `clone_split` will
#' be the fraction of the clones in each sample.
#' * "homeo" - Same as "homeostasis".
#' * "rel" - Same as "homeostasis".
#' * "top" - Plot the top clones. The `clone_split` will be indexes to cut the clones.
#' * "rare" - Plot the rare clones. The `clone_split` will be the clone sizes.
#' @param clone_split The split for the clones. Default is NULL.
#' * For "homeostasis", "homeo", "rel" - Default is `list(Rare = 1e-04, Small = 0.001, Medium = 0.01, Large = 0.1, Hyperexpanded = 1)`.
#' * For "top" - Default is `c(10, 100, 1000, 10000, 30000, 100000)`.
#' * For "rare" - Default is `c(1, 3, 10, 30, 100)`.
#' @param scale Whether to scale the values on the y-axis. Default is TRUE.
#' * TRUE: The values of each group (on the x-axis) will be scaled to 1.
#' * FALSE: No scaling.
#' * "sample"/"Sample": The values in each sample will be scaled to 1.
#' @param facet_by The column name in the meta data to facet the plots. Default: NULL
#' @param group_by The column name in the meta data to group the cells. Default: NULL
#' @param split_by The column name in the meta data to split the plots. Default: NULL
#' @param xlab The x-axis label. Default is NULL.
#' @param ylab The y-axis label. Default is NULL.
#' @param plot_type The type of plot to use. Default is "bar".
#' Possible values are "bar", "ring", "box", and "violin".
#' @param order The order of the x-axis items or groups. Default is an empty list.
#' It should be a list of values. The names are the column names, and the values are the order.
#' @param ... Other arguments passed to the specific plot function.
#' * For `bar` plot, see [plotthis::BarPlot()].
#' * For `ring` plot, see [plotthis::RingPlot()].
#' * For `box` plot, see [plotthis::BoxPlot()].
#' * For `violin` plot, see [plotthis::ViolinPlot()].
#' @return A ggplot object or a list if `combine` is FALSE
#' @importFrom dplyr %>% mutate summarise ungroup n
#' @importFrom tidyr pivot_longer separate
#' @importFrom plotthis BarPlot RingPlot BoxPlot ViolinPlot
#' @importFrom scRepertoire clonalHomeostasis clonalProportion
#' @export
#' @examples
#' \donttest{
#' set.seed(8525)
#' data(contig_list, package = "scRepertoire")
#' data <- scRepertoire::combineTCR(contig_list,
#' samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L"))
#' data <- scRepertoire::addVariable(data,
#' variable.name = "Type",
#' variables = rep(c("B", "L"), 4)
#' )
#' data <- scRepertoire::addVariable(data,
#' variable.name = "Subject",
#' variables = rep(c("P17", "P18", "P19", "P20"), each = 2)
#' )
#'
#' ClonalCompositionPlot(data)
#' ClonalCompositionPlot(data, method = "top")
#' ClonalCompositionPlot(data, plot_type = "ring")
#' ClonalCompositionPlot(data, group_by = "Type", plot_type = "box", comparison = TRUE)
#' ClonalCompositionPlot(data, group_by = "Type", plot_type = "violin", add_box = TRUE,
#' add_bg = TRUE)
#' ClonalCompositionPlot(data, method = "rare")
#' }
ClonalCompositionPlot <- function(
data, clone_call = "aa", chain = "both", method = c("homeostasis", "homeo", "rel", "top", "rare"),
clone_split = NULL, scale = TRUE, facet_by = NULL, group_by = NULL, split_by = NULL,
xlab = NULL, ylab = NULL, plot_type = c("bar", "ring", "box", "violin"), order = list(), ...
) {
plot_type <- match.arg(plot_type)
method <- match.arg(method)
if (plot_type %in% c("box", "violin") && is.null(group_by)) {
stop("'group_by' must be provided for box/violin ClonalCompositionPlot")
}
if (is.null(clone_split)) {
if (method %in% c("homeostasis", "homeo", "rel")) {
clone_split <- list(Rare = 1e-04, Small = 0.001, Medium = 0.01, Large = 0.1, Hyperexpanded = 1)
} else if (method == "top") {
# clone indexes
clone_split <- c(10, 100, 1000, 10000, 30000, 100000)
} else { # rare
# clone sizes
clone_split <- c(1, 3, 10, 30, 100)
}
}
all_groupings <- unique(c("Sample", group_by, facet_by, split_by))
if (method == "homeostasis" || method == "homeo" || method == "rel" || method == "top") {
data <- merge_clonal_groupings(data, all_groupings)
if (method == "top") {
data <- clonalProportion(data, cloneCall = clone_call, chain = chain,
clonalSplit = clone_split, group.by = ".group", exportTable = TRUE)
} else {
data <- clonalHomeostasis(data, cloneCall = clone_call, chain = chain,
cloneSize = clone_split, group.by = ".group", exportTable = TRUE)
}
data <- as.data.frame(data)
data$.group <- rownames(data)
# restore the groups
data <- separate(data, ".group", into = all_groupings, sep = " // ")
data <- pivot_longer(data, cols = -all_groupings, names_to = ".names", values_to = ".values")
# Sample Type .names .values
name_levels <- unique(data$.names)
} else { # rare
data <- clonal_size_data(data, clone_call, chain, all_groupings)
# CloneID Sample Type count fraction
clone_split <- sort(clone_split)
clone_split <- c(-Inf, clone_split, Inf)
labels <- sapply(1:(length(clone_split) - 1), function(i) {
if (clone_split[i] == -Inf && clone_split[i + 1] == 1) {
"1"
} else if (clone_split[i] == -Inf) {
paste0("[1:", clone_split[i + 1], "]")
} else if (i == length(clone_split) - 1) {
paste0("[", clone_split[i], ":MAX]")
} else {
paste0("[", clone_split[i] + 1, ":", clone_split[i + 1], "]")
}
})
data$.names <- cut(data$count, breaks = clone_split, labels = labels)
name_levels <- levels(data$.names)
data <- data %>%
dplyr::group_by(!!!syms(setdiff(colnames(data), c("CloneID", "count", "fraction")))) %>%
summarise(.values = n(), .groups = "drop")
}
for (group in all_groupings) {
if (!is.null(order[[group]])) {
data[[group]] <- factor(data[[group]], levels = order[[group]])
}
}
if (isTRUE(scale)) {
data <- data %>%
dplyr::group_by(!!!syms(c(group_by, facet_by, split_by))) %>%
mutate(.values = !!sym(".values") / sum(!!sym(".values")))
ylab <- ylab %||% "Relative Abundance"
} else if (identical(scale, "sample") || identical(scale, "Sample")) {
data <- data %>%
dplyr::group_by(!!!syms(c("Sample", group_by, facet_by, split_by))) %>%
mutate(.values = !!sym(".values") / sum(!!sym(".values")))
ylab <- ylab %||% "Relative Abundance"
} else {
ylab <- ylab %||% "Abundance"
}
if (method == "homeostasis" || method == "homeo" || method == "rel") {
if (plot_type %in% c("bar", "ring") && !is.null(group_by)) {
data <- data %>%
dplyr::group_by(!!!syms(setdiff(colnames(data), c("Sample", ".values")))) %>%
summarise(.values = sum(!!sym(".values")), .groups = "drop")
}
data$.names <- factor(data$.names, levels = name_levels)
if (plot_type == "bar") {
BarPlot(data, x = group_by %||% "Sample", y = ".values", group_by = ".names",
group_name = "Clonal Group", position = "stack", facet_by = facet_by, split_by = split_by,
xlab = xlab %||% group_by %||% "Sample", ylab = ylab %||% "Relative Abundance", ...)
} else if (plot_type == "ring") {
RingPlot(data, x = group_by %||% "Sample", y = ".values", group_by = ".names",
group_name = "Clonal Group", facet_by = facet_by, split_by = split_by,
xlab = xlab %||% group_by %||% "Sample", ylab = ylab %||% "Relative Abundance", ...)
} else if (plot_type == "box") {
BoxPlot(data, x = ".names", y = ".values", group_by = group_by,
facet_by = facet_by, split_by = split_by,
xlab = xlab %||% "Clonal Group", ylab = ylab %||% "Relative Abundance", ...)
} else { # violin
ViolinPlot(data, x = ".names", y = ".values", group_by = group_by,
facet_by = facet_by, split_by = split_by,
xlab = xlab %||% "Clonal Group", ylab = ylab %||% "Relative Abundance", ...)
}
} else {
group_name <- ifelse(method == "top", "Clonal Indices", "Clonal Sizes")
if (plot_type %in% c("bar", "ring") && !is.null(group_by)) {
data <- data %>%
dplyr::group_by(!!!syms(setdiff(colnames(data), c("Sample", ".values")))) %>%
summarise(.values = sum(!!sym(".values")), .groups = "drop")
}
data$.names <- factor(data$.names, levels = name_levels)
if (plot_type == "bar") {
BarPlot(data, x = group_by %||% "Sample", y = ".values", group_by = ".names",
group_name = group_name, position = "stack", facet_by = facet_by, split_by = split_by,
xlab = xlab %||% group_by %||% "Sample", ylab = ylab, ...)
} else if (plot_type == "ring") {
RingPlot(data, x = group_by %||% "Sample", y = ".values", group_by = ".names",
group_name = group_name, facet_by = facet_by, split_by = split_by,
xlab = xlab %||% group_by %||% "Sample", ylab = ylab, ...)
} else if (plot_type == "box") {
BoxPlot(data, x = ".names", y = ".values", group_by = group_by,
facet_by = facet_by, split_by = split_by,
xlab = xlab %||% group_name, ylab = ylab, ...)
} else { # violin
ViolinPlot(data, x = ".names", y = ".values", group_by = group_by,
facet_by = facet_by, split_by = split_by,
xlab = xlab %||% group_name, ylab = ylab, ...)
}
}
}
#' ClonalOverlapPlot
#'
#' @description Plot the overlap of the clones in different samples/groups.
#' @param data The product of [scRepertoire::combineTCR], [scRepertoire::combineTCR], or
#' [scRepertoire::combineExpression].
#' @param clone_call How to call the clone - VDJC gene (gene), CDR3 nucleotide (nt),
#' CDR3 amino acid (aa), VDJC gene + CDR3 nucleotide (strict) or a custom variable
#' in the data
#' @param chain indicate if both or a specific chain should be used - e.g. "both",
#' "TRA", "TRG", "IGH", "IGL"
#' @param group_by The column name in the meta data to group the cells. Default: "Sample"
#' @param group_by_sep The separator used to concatenate the group_by when multiple columns are used.
#' @param full Whether to plot the full heatmap, or just a triangle. Default is TRUE.
#' @param split_by The column name in the meta data to split the plots. Default: NULL
#' @param order The order of the groups. Default is an empty list.
#' It should be a list of values. The names are the column names, and the values are the order.
#' @param method The method to calculate the overlap. Default is "raw".
#' * "overlap" - overlap coefficient
#' * "morisita" - Morisita’s overlap index
#' * "jaccard" - Jaccard index
#' * "cosine" - cosine similarity
#' * "raw" - exact number of overlapping clones
#' See also [scRepertoire::clonalOverlap].
#' @param palette The color palette to use. Default is "Blues".
#' @param label_accuracy The accuracy of the labels. Default is NULL.
#' If NULL, it will be 1 for "raw" and 0.01 for other methods.
#' @param label_cutoff The cutoff for the labels to show. Default is 1e-3.
#' @param cluster_rows Whether to cluster the rows. Default is FALSE.
#' @param cluster_columns Whether to cluster the columns. Default is FALSE.
#' @param show_row_names Whether to show the row names. Default is TRUE.
#' @param show_column_names Whether to show the column names. Default is TRUE.
#' @param ... Other arguments passed to the specific plot function [plotthis::Heatmap()].
#' @return A ComplexHeatmap object or a list if `combine` is FALSE
#' @importFrom stats as.dist
#' @importFrom rlang syms
#' @importFrom dplyr %>% filter
#' @importFrom tidyr separate pivot_longer unite