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cb_custom_functions.R
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cb_custom_functions.R
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## Functions shared across cerebellum analyses
library(liger)
library(Seurat)
######################################################################################
## Supplementary liger functions
#### an improvement to clusterLouvainJaccard -- allowing users to set more parameters and stores those
# parameters inside parameters slot of liger object (under list name 'Seurat')
# note that this produces same results as combination of makeSNN and calcClusters below
clusterLouvainJaccard2 = function (object, resolution = 0.1, k.param = 30, n.iter = 10,
reduction.type = 'NMF', force.recalc = T, save.SNN = T,
n.start = 10, print.output = F, dims.use = 1:ncol([email protected]), ...)
{
temp.seurat = CreateSeuratObject(t(Reduce(rbind, [email protected])))
[email protected] = t(Reduce(rbind, [email protected]))
rownames([email protected]) = colnames([email protected])
temp.seurat@dr$NMF = new(Class = "dim.reduction", cell.embeddings = [email protected],
key = "NMF")
temp.seurat <- FindClusters(object = temp.seurat, reduction.type = reduction.type,
dims.use = dims.use, force.recalc = force.recalc, save.SNN = save.SNN,
resolution = resolution, k.param = k.param, n.iter = n.iter,
n.start = n.start, print.output = print.output, ...)
object@clusters = temp.seurat@ident
if (!('Seurat' %in% names(object@parameters))) {
object@parameters[['Seurat']] = list()
}
object@parameters[['Seurat']][['BuildSNN']] = [email protected]$BuildSNN
res_params = paste0('FindClusters.res.', resolution)
object@parameters[['Seurat']][[res_params]] = [email protected][[res_params]]
return(object)
}
### Function to add percent_mito column to cell.data slot in liger object
getPercentMito <- function(object) {
all.genes = Reduce(union, lapply([email protected], rownames))
mito.genes <- grep(pattern = "^mt-", x = all.genes, value = TRUE, ignore.case = T)
percent_mito = unlist(lapply([email protected], function(x) {
colSums(x[mito.genes, ])/colSums(x)
}), use.names = F)
[email protected][['percent_mito']] <- percent_mito
return(object)
}
### Function to add CB region column to cell.data slot in liger object
# (using cell names like IXa_M003_TAGCACAAGCAAATCA)
getCBRegion <- function(object) {
sup_region = as.character(sapply(rownames([email protected]), function(x){
strsplit(x, '_')[[1]][1]
}))
regions = sapply(sup_region, function(x) {
if (grepl('AN1', x = x)) {
return('AN1')
} else if (grepl('AN2', x = x)) {
return('AN2')
} else if (grepl('Calb', x) | grepl('Purk', x)) {
return('Purk')
} else if (grepl('COP', x = x)) {
return('COP')
} else if (grepl('VIII', x)) {
return('VIII')
} else if (grepl('VII', x)) {
return('VII')
} else if (grepl('VI', x) | grepl('Vl', x) | grepl('DEC', x)) {
return('VI')
} else if (grepl('IV', x) | grepl('CUL', x) | grepl('V', x)) {
return('CUL')
} else if (grepl('SIM', x)) {
return('SIM')
} else if (grepl('PF', x)) {
return('PF')
} else if (grepl('F', x)) {
return('F')
} else if (grepl('PRM', x)) {
return('PRM')
} else if (grepl('IX', x)) {
return('IX')
} else if (grepl('X', x)) {
return('X')
} else if (grepl('III', x)) {
return('III')
} else if (grepl('II', x)) {
return('II')
} else if (grepl('I', x)) {
return('I')
}
})
[email protected][['region']] = factor(regions)
return(object)
}
### Function to add animal column to cell.data slot in liger object
# (using cell names like IXa_M003_TAGCACAAGCAAATCA)
getCBAnimal <- function(object) {
animal = as.character(sapply(rownames([email protected]), function(x){
strsplit(x, '_')[[1]][2]
}))
[email protected][['animal']] = factor(animal)
return(object)
}
##############################################################################
# plotGene with png
# plotByDatasetAndCluster with png
plotGene_png <- function(object, gene, use.raw = F, use.scaled = F, scale.by = 'dataset',
log2scale = NULL, methylation.indices = NULL, plot.by = 'dataset',
set.dr.lims = F, pt.size = 0.1, min.clip = NULL, max.clip = NULL,
clip.absolute = F, points.only = F, option = 'plasma', cols.use = NULL,
zero.color = '#F5F5F5', axis.labels = NULL, do.legend = T, return.plots = F,
use.png = T, width = 7, height = 6, dpi = 100) {
if ((plot.by != scale.by) & (use.scaled)) {
warning("Provided values for plot.by and scale.by do not match; results may not be very
interpretable.")
}
if (use.raw) {
if (is.null(log2scale)) {
log2scale <- FALSE
}
# drop only outer level names
gene_vals <- getGeneValues([email protected], gene, log2scale = log2scale)
} else {
if (is.null(log2scale)) {
log2scale <- TRUE
}
# rescale in case requested gene not highly variable
if (use.scaled) {
# check for feature
if (!(scale.by %in% colnames([email protected])) & scale.by != 'none') {
stop("Please select existing feature in cell.data to scale.by, or add it before calling.")
}
gene_vals <- getGeneValues([email protected], gene)
cellnames <- names(gene_vals)
# set up dataframe with groups
gene_df <- data.frame(gene = gene_vals)
if (scale.by == 'none') {
gene_df[['scaleby']] = 'none'
} else {
gene_df[['scaleby']] = factor([email protected][[scale.by]])
}
gene_df1 <- gene_df %>%
group_by(scaleby) %>%
# scale by selected feature
mutate_at(vars(-group_cols()), function(x) { scale(x, center = F)})
gene_vals <- gene_df1$gene
names(gene_vals) <- cellnames
if (log2scale) {
gene_vals <- log2(10000 * gene_vals + 1)
}
} else {
# using normalized data
# indicate methylation indices here
gene_vals <- getGeneValues([email protected], gene, methylation.indices = methylation.indices,
log2scale = log2scale)
}
}
gene_vals[gene_vals == 0] <- NA
dr_df <- data.frame([email protected])
rownames(dr_df) <- rownames([email protected])
dr_df$gene <- as.numeric(gene_vals[rownames(dr_df)])
colnames(dr_df) <- c("dr1", "dr2", "gene")
# get dr limits for later
lim1 <- c(min(dr_df$dr1), max(dr_df$dr1))
lim2 <- c(min(dr_df$dr2), max(dr_df$dr2))
if (plot.by != 'none') {
if (!(plot.by %in% colnames([email protected]))) {
stop("Please select existing feature in cell.data to plot.by, or add it before calling.")
}
dr_df$plotby <- factor([email protected][[plot.by]])
} else {
dr_df$plotby <- factor("none")
}
# expand clip values if only single provided
num_levels <- length(levels(dr_df$plotby))
if (length(min.clip) == 1) {
min.clip <- rep(min.clip, num_levels)
names(min.clip) <- levels(dr_df$plotby)
}
if (length(max.clip) == 1) {
max.clip <- rep(max.clip, num_levels)
names(max.clip) <- levels(dr_df$plotby)
}
if (!is.null(min.clip) & is.null(names(min.clip))) {
if (num_levels > 1) {
message("Adding names to min.clip according to levels in plot.by group; order may not be
preserved as intended if multiple clip values passed in. Pass in named vector to
prevent this.")
}
names(min.clip) <- levels(dr_df$plotby)
}
if (!is.null(max.clip) & is.null(names(max.clip))) {
if (num_levels > 1) {
message("Adding names to max.clip according to levels in plot.by group; order may not be
preserved as intended if multiple clip values passed in. Pass in named vector to
prevent this.")
}
names(max.clip) <- levels(dr_df$plotby)
}
p_list <- list()
for (sub_df in split(dr_df, f = dr_df$plotby)) {
# maybe do quantile cutoff here
group_name <- as.character(sub_df$plotby[1])
if (!clip.absolute) {
max_v <- quantile(sub_df$gene, probs = max.clip[group_name], na.rm = T)
min_v <- quantile(sub_df$gene, probs = min.clip[group_name], na.rm = T)
} else {
max_v <- max.clip[group_name]
min_v <- min.clip[group_name]
}
sub_df$gene[sub_df$gene < min_v & !is.na(sub_df$gene)] <- min_v
sub_df$gene[sub_df$gene > max_v & !is.na(sub_df$gene)] <- max_v
ggp <- ggplot(sub_df, aes(x = dr1, y = dr2, color = gene)) + geom_point(size = pt.size) +
labs(col = gene)
if (!is.null(cols.use)) {
ggp <- ggp + scale_color_gradientn(colors = cols.use,
na.value = zero.color)
} else {
ggp <- ggp + scale_color_viridis_c(option = option,
direction = -1,
na.value = zero.color)
}
if (set.dr.lims) {
ggp <- ggp + xlim(lim1) + ylim(lim2)
}
if (plot.by != 'none') {
base <- as.character(sub_df$plotby[1])
} else {
base <- ""
}
ggp <- ggp + ggtitle(base)
if (!is.null(axis.labels)) {
ggp <- ggp + xlab(axis.labels[1]) + ylab(axis.labels[2])
}
if (!do.legend) {
ggp <- ggp + theme(legend.position = "none")
}
if (points.only) {
ggp <- ggp + theme(
axis.line = element_blank(), axis.text.x = element_blank(),
axis.text.y = element_blank(), axis.ticks = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(), legend.position = "none",
panel.background = element_blank(), panel.border = element_blank(),
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
plot.background = element_blank(), plot.title = element_blank()
)
}
if (use.png) {
legend_s = get_legend(ggp)
ggp <- AugmentPlot(ggp, width = width, height = height, dpi = dpi) + legend_s
}
p_list[[as.character(sub_df$plotby[1])]] <- ggp
}
if (plot.by == 'dataset') {
p_list <- p_list[names([email protected])]
}
if (return.plots){
if (length(p_list) == 1) {
return(p_list[[1]])
} else {
return(p_list)
}
} else {
for (plot in p_list) {
print(plot)
}
}
}
plotByDatasetAndCluster_png <- function(object, clusters = NULL, title = NULL, pt.size = 0.3,
text.size = 3, do.shuffle = T, rand.seed = 1,
axis.labels = NULL, do.legend = T, legend.size = 5,
return.plots = F, use.png = T, width = 7, height = 6, dpi = 100) {
tsne_df <- data.frame([email protected])
colnames(tsne_df) <- c("tsne1", "tsne2")
tsne_df$Dataset <- unlist(lapply(1:length(object@H), function(x) {
rep(names(object@H)[x], nrow(object@H[[x]]))
}))
c_names <- names(object@clusters)
if (is.null(clusters)) {
# if clusters have not been set yet
if (length(object@clusters) == 0) {
clusters <- rep(1, nrow([email protected]))
names(clusters) <- c_names <- rownames([email protected])
} else {
clusters <- object@clusters
c_names <- names(object@clusters)
}
}
tsne_df$Cluster <- clusters[c_names]
if (do.shuffle) {
set.seed(rand.seed)
idx <- sample(1:nrow(tsne_df))
tsne_df <- tsne_df[idx, ]
}
p1 <- ggplot(tsne_df, aes(x = tsne1, y = tsne2, color = Dataset)) +
geom_point(size = pt.size) +
guides(color = guide_legend(override.aes = list(size = legend.size)))
centers <- tsne_df %>% group_by(Cluster) %>% summarize(
tsne1 = median(x = tsne1),
tsne2 = median(x = tsne2)
)
p2 <- ggplot(tsne_df, aes(x = tsne1, y = tsne2, color = Cluster)) + geom_point(size = pt.size) +
guides(color = guide_legend(override.aes = list(size = legend.size)))
if (!use.png) {
p2 <- p2 + geom_text(data = centers, mapping = aes(label = Cluster),
colour = "black", size = text.size)
}
if (!is.null(title)) {
p1 <- p1 + ggtitle(title[1])
p2 <- p2 + ggtitle(title[2])
}
if (!is.null(axis.labels)) {
p1 <- p1 + xlab(axis.labels[1]) + ylab(axis.labels[2])
p2 <- p2 + xlab(axis.labels[1]) + ylab(axis.labels[2])
}
if (!do.legend) {
p1 <- p1 + theme(legend.position = "none")
p2 <- p2 + theme(legend.position = "none")
}
if (use.png) {
p1_legend = get_legend(p1)
p2_legend = get_legend(p2)
p1 <- AugmentPlot(p1, width = width, height = height, dpi = dpi) + p1_legend
p2 <- AugmentPlot(p2, width = width, height = height, dpi = dpi) +
geom_text(data = centers, mapping = aes(tsne1, tsne2, label = Cluster),
colour = "black", size = text.size) + p2_legend
}
if (return.plots) {
return(list(p1, p2))
} else {
print(p1)
print(p2)
}
}
###############################################################################
## Next few functions all have to do with plotFactor_new (with png option)
get_legend <- function(p) {
tmp <- ggplot_gtable(ggplot_build(p))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend <- tmp$grobs[[leg]]
legend
}
# note that setting point size to -1 seems to make it disappear
# (trick that I can't find in documentation, but got from Seurat)
# also tried Pethukov's ggrastr, but it caused too much distortion
# also started taking way too long when playing with raster.width > 4
# still working on fixing legend for first page of plots
plotFactors_new <- function(object, num.genes = 8, cells.highlight = NULL, plot.tsne = F,
return.plots = F, pt.size1 = 0.5, pt.size2 = 1,
tsne.colors = c('lemonchiffon', 'red'), use.png = F,
width = 7, height = 6, dpi = 100) {
k <- ncol([email protected])
pb <- txtProgressBar(min = 0, max = k, style = 3)
W <- t(object@W)
rownames(W) <- colnames([email protected][[1]])
Hs_norm <- [email protected]
H_raw = do.call(rbind, object@H)
plot_list = list()
tsne_list = list()
for (i in 1:k) {
# creates array of 2 by 1 plots
par(mfrow = c(2, 1))
top_genes.W <- rownames(W)[order(W[, i], decreasing = T)[1:num.genes]]
top_genes.W.string <- paste0(top_genes.W, collapse = ", ")
factor_textstring <- paste0("Factor", i)
plot_title1 <- paste(factor_textstring, "\n", top_genes.W.string, "\n")
h_df = data.frame(x = 1:nrow(Hs_norm), h_norm = Hs_norm[, i],
h_raw = H_raw[, i], dataset = [email protected]$dataset,
highlight = FALSE)
top <- ggplot(h_df, aes(x = x, y=h_raw, col = dataset)) + geom_point(size = pt.size1) +
labs(x = 'Cell', y = 'Raw H Score') + ggtitle(plot_title1) + theme(legend.position = 'none')
bottom <- ggplot(h_df, aes(x = x, y=h_norm, col = dataset)) + geom_point(size = pt.size1) +
labs(x = 'Cell', y = 'H_norm Score') +
theme(legend.position = 'top',
legend.title = element_blank()) +
guides(colour = guide_legend(override.aes = list(size = 2)))
if (!is.null(cells.highlight)) {
h_df[cells.highlight, 'highlight'] = TRUE
top <- top + geom_point(data = subset(h_df, highlight == TRUE),
aes(x, h_raw),
col = "black",
size = pt.size1)
bottom <- bottom + geom_point(data = subset(h_df, highlight == TRUE),
aes(x, h_norm),
col = "black",
size = pt.size1)
}
if (use.png){
top = AugmentPlot(top, width = width, height = height, dpi = dpi)
top = top + labs(x = 'Cell', y = 'Raw H Score')
# save legend before losing it -- might have to recreate manually
bottom_legend = get_legend(bottom)
bottom = AugmentPlot(bottom, width = width, height = height, dpi = dpi)
bottom = bottom + labs(x = 'Cell', y = 'H_norm Score')
}
full = plot_grid(top, bottom, ncol = 1, rel_heights = c(1, 1))
plot_list[[i]] = full
if (plot.tsne) {
tsne_df <- data.frame(Hs_norm[, i], [email protected])
factorlab <- paste0("Factor", i)
colnames(tsne_df) <- c(factorlab, "tSNE1", "tSNE2")
p1 <- ggplot(tsne_df, aes_string(x = "tSNE1", y = "tSNE2", color = factorlab)) +
geom_point(size = pt.size2) +
scale_color_gradient(low = tsne.colors[1], high = tsne.colors[2]) + ggtitle(label = paste('Factor', i)) +
theme(legend.position = 'none')
if (use.png){
p1 = AugmentPlot(p1, width = width, height = height, dpi = dpi)
}
tsne_list[[i]] = p1
}
setTxtProgressBar(pb, i)
}
if (return.plots) {
return(list(plot_list, tsne_list))
} else {
for (i in 1:k) {
print(plot_list[[i]])
print(tsne_list[[i]])
}
}
}
library(png)
# AugmentPlot taken from Seurat v3
#' Augments ggplot2-based plot with a PNG image.
#'
#' Creates "vector-friendly" plots. Does this by saving a copy of the plot as a PNG file,
#' then adding the PNG image with \code{\link[ggplot2]{annotation_raster}} to a blank plot
#' of the same dimensions as \code{plot}. Please note: original legends and axes will be lost
#' during augmentation.
#'
#' @param plot A ggplot object
#' @param width,height Width and height of PNG version of plot
#' @param dpi Plot resolution
#'
#' @return A ggplot object
#'
#' @importFrom png readPNG
#' @importFrom ggplot2 ggplot_build ggsave ggplot aes_string geom_blank annotation_raster ggtitle
#'
#' @export
#'
#' @examples
#' \dontrun{
#' plot <- DimPlot(object = pbmc_small)
#' AugmentPlot(plot = plot)
#' }
#'
AugmentPlot <- function(plot, width = 10, height = 10, dpi = 100) {
pbuild.params <- ggplot_build(plot = plot)$layout$panel_params[[1]]
range.values <- c(
pbuild.params$x.range,
pbuild.params$y.range
)
xyparams <- GetXYAesthetics(
plot = plot,
geom = class(x = plot$layers[[1]]$geom)[1]
)
title <- plot$labels$title
tmpfile <- tempfile(fileext = '.png')
ggsave(
filename = tmpfile,
plot = plot + NoLegend() + NoAxes() + theme(plot.title = element_blank()),
width = width,
height = height,
dpi = dpi
)
img <- readPNG(source = tmpfile)
file.remove(tmpfile)
blank <- ggplot(
data = plot$data,
mapping = aes_string(x = xyparams$x, y = xyparams$y)
) + geom_blank()
blank <- blank + plot$theme + ggtitle(label = title)
blank <- blank + annotation_raster(
raster = img,
xmin = range.values[1],
xmax = range.values[2],
ymin = range.values[3],
ymax = range.values[4]
)
return(blank)
}
# other functions which are necessary -- GetXYAesthetics, NoLegend, NoAxes
# Get X and Y aesthetics from a plot for a certain geom
#
# @param plot A ggplot2 object
# @param geom Geom class to filter to
# @param plot.first Use plot-wide X/Y aesthetics before geom-specific aesthetics
#
# @return A named list with values 'x' for the name of the x aesthetic and 'y' for the y aesthetic
#
GetXYAesthetics <- function(plot, geom = 'GeomPoint', plot.first = TRUE) {
geoms <- sapply(
X = plot$layers,
FUN = function(layer) {
return(class(x = layer$geom)[1])
}
)
geoms <- which(x = geoms == geom)
if (length(x = geoms) == 0) {
stop("Cannot find a geom of class ", geom)
}
geoms <- min(geoms)
if (plot.first) {
x <- as.character(x = plot$mapping$x %||% plot$layers[[geoms]]$mapping$x)[2]
y <- as.character(x = plot$mapping$y %||% plot$layers[[geoms]]$mapping$y)[2]
} else {
x <- as.character(x = plot$layers[[geoms]]$mapping$x %||% plot$mapping$x)[2]
y <- as.character(x = plot$layers[[geoms]]$mapping$y %||% plot$mapping$y)[2]
}
return(list('x' = x, 'y' = y))
}
NoLegend <- function(...) {
no.legend.theme <- theme(
# Remove the legend
legend.position = 'none',
# Validate the theme
validate = TRUE,
...
)
return(no.legend.theme)
}
#' @inheritParams SeuratTheme
#' @param keep.text Keep axis text
#' @param keep.ticks Keep axis ticks
#'
#' @importFrom ggplot2 theme element_blank
#' @export
#'
#' @rdname SeuratTheme
#' @aliases NoAxes
#'
#' @examples
#' # Generate a plot with no axes
#' library(ggplot2)
#' df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2))
#' p <- ggplot(data = df, mapping = aes(x = x, y = y)) + geom_point(mapping = aes(color = 'red'))
#' p + NoAxes()
#'
NoAxes <- function(..., keep.text = FALSE, keep.ticks = FALSE) {
blank <- element_blank()
no.axes.theme <- theme(
# Remove the axis elements
axis.line.x = blank,
axis.line.y = blank,
# Validate the theme
validate = TRUE,
...
)
if (!keep.text) {
no.axes.theme <- no.axes.theme + theme(
axis.text.x = blank,
axis.text.y = blank,
axis.title.x = blank,
axis.title.y = blank,
validate = TRUE,
...
)
}
if (!keep.ticks){
no.axes.theme <- no.axes.theme + theme(
axis.ticks.x = blank,
axis.ticks.y = blank,
validate = TRUE,
...
)
}
return(no.axes.theme)
}
# also some hadley wickham functions
`%||%` <- function(lhs, rhs) {
if (!is.null(x = lhs)) {
return(lhs)
} else {
return(rhs)
}
}
###############################################################################
## Supplementary Seurat functions
# This function takes all possible pairs of clusters in a Seurat object and finds
# DE genes between them -- concatenates all dataframes into single result and returns
# it also downsamples by default to speed it up
# ... should be additional parameters for FindMarkers
FindMarkersPairwise = function(object, max.cells.per.ident = 700, ...) {
clusters <- levels(object@ident)
pairwise <- combn(clusters, 2)
results <- list()
for(i in 1:ncol(pairwise)) {
message('Finding DE genes between ', pairwise[1, i], ' and ', pairwise[2, i])
markers_pair <- FindMarkers(object, ident.1 = pairwise[1, i], ident.2 = pairwise[2, i],
max.cells.per.ident = max.cells.per.ident, ...)
comparisons <- pairwise[, i]
markers_pair$comparison <- paste(comparisons[1], comparisons[2], sep = '_')
markers_pair$gene <- rownames(markers_pair)
results[[i]] <- markers_pair
}
results.df <- do.call(rbind, results)
return(results.df)
}
FindMarkersPairwisePerm = function(object, clusters = NULL, max.cells.per.ident = 700, only.pos = T, ...) {
clusters_all <- levels(object@ident)
if (is.null(clusters)) {
clusters = clusters_all
}
results <- list()
index = 0
for (i in clusters) {
for (j in clusters_all) {
if (i != j) {
index = index + 1
message('Finding DE genes between ', i, ' and ', j)
markers_pair <- FindMarkers(object, ident.1 = i, ident.2 = j, only.pos = only.pos,
max.cells.per.ident = max.cells.per.ident, ...)
markers_pair$pct_diff = markers_pair$pct.1 - markers_pair$pct.2
markers_pair$ident.1 <- i
markers_pair$ident.2 <- j
markers_pair$gene <- rownames(markers_pair)
results[[index]] <- markers_pair
}
}
}
results.df <- do.call(rbind, results)
return(results.df)
}
##############################################################################
# feature plot with raw data instead of scaled data
# also has necessary internal function dependencies below (all functions until next section
# are just dependencies)
FeaturePlot_raw = function (object, features.plot, min.cutoff = NA, max.cutoff = NA,
dim.1 = 1, dim.2 = 2, cells.use = NULL, pt.size = 1, cols.use = c("yellow",
"red"),
pch.use = 16, overlay = FALSE, do.hover = FALSE,
data.hover = "ident", do.identify = FALSE, reduction.use = "tsne",
use.imputed = FALSE, nCol = NULL, no.axes = FALSE, no.legend = TRUE,
coord.fixed = FALSE, dark.theme = FALSE, do.return = FALSE,
vector.friendly = FALSE, png.file = NULL, png.arguments = c(10,
10, 100))
{
cells.use <- SetIfNull(x = cells.use, default = colnames(x = object@data))
if (is.null(x = nCol)) {
nCol <- 2
if (length(x = features.plot) == 1) {
nCol <- 1
}
if (length(x = features.plot) > 6) {
nCol <- 3
}
if (length(x = features.plot) > 9) {
nCol <- 4
}
}
num.row <- floor(x = length(x = features.plot)/nCol - 1e-05) +
1
if (overlay | do.hover) {
num.row <- 1
nCol <- 1
}
par(mfrow = c(num.row, nCol))
dim.code <- GetDimReduction(object = object, reduction.type = reduction.use,
slot = "key")
dim.codes <- paste0(dim.code, c(dim.1, dim.2))
data.plot <- as.data.frame(GetCellEmbeddings(object = object,
reduction.type = reduction.use, dims.use = c(dim.1, dim.2),
cells.use = cells.use))
x1 <- paste0(dim.code, dim.1)
x2 <- paste0(dim.code, dim.2)
data.plot$x <- data.plot[, x1]
data.plot$y <- data.plot[, x2]
data.plot$pt.size <- pt.size
names(x = data.plot) <- c("x", "y")
data.use <- t(x = FetchData(object = object, vars.all = features.plot,
cells.use = cells.use, use.imputed = use.imputed, use.raw = T))
min.cutoff <- mapply(FUN = function(cutoff, feature) {
ifelse(test = is.na(x = cutoff), yes = min(data.use[feature,
]), no = cutoff)
}, cutoff = min.cutoff, feature = features.plot)
max.cutoff <- mapply(FUN = function(cutoff, feature) {
ifelse(test = is.na(x = cutoff), yes = max(data.use[feature,
]), no = cutoff)
}, cutoff = max.cutoff, feature = features.plot)
check_lengths = unique(x = vapply(X = list(features.plot,
min.cutoff, max.cutoff), FUN = length, FUN.VALUE = numeric(length = 1)))
if (length(x = check_lengths) != 1) {
stop("There must be the same number of minimum and maximum cuttoffs as there are features")
}
if (overlay) {
pList <- list(BlendPlot(data.use = data.use, features.plot = features.plot,
data.plot = data.plot, pt.size = pt.size, pch.use = pch.use,
cols.use = cols.use, dim.codes = dim.codes, min.cutoff = min.cutoff,
max.cutoff = max.cutoff, coord.fixed = coord.fixed,
no.axes = no.axes, no.legend = no.legend, dark.theme = dark.theme))
}
else {
pList <- mapply(FUN = SingleFeaturePlot, feature = features.plot,
min.cutoff = min.cutoff, max.cutoff = max.cutoff,
coord.fixed = coord.fixed, MoreArgs = list(data.use = data.use,
data.plot = data.plot, pt.size = pt.size, pch.use = pch.use,
cols.use = cols.use, dim.codes = dim.codes, no.axes = no.axes,
no.legend = no.legend, dark.theme = dark.theme,
vector.friendly = vector.friendly, png.file = png.file,
png.arguments = png.arguments), SIMPLIFY = FALSE)
}
if (do.hover) {
if (length(x = pList) != 1) {
stop("'do.hover' only works on a single feature or an overlayed FeaturePlot")
}
if (is.null(x = data.hover)) {
features.info <- NULL
}
else {
features.info <- FetchData(object = object, vars.all = data.hover)
}
return(HoverLocator(plot = pList[[1]], data.plot = data.plot,
features.info = features.info, dark.theme = dark.theme,
title = features.plot))
}
else if (do.identify) {
if (length(x = pList) != 1) {
stop("'do.identify' only works on a single feature or an overlayed FeaturePlot")
}
return(FeatureLocator(plot = pList[[1]], data.plot = data.plot,
dark.theme = dark.theme))
}
else {
print(x = cowplot::plot_grid(plotlist = pList, ncol = nCol))
}
ResetPar()
if (do.return) {
return(pList)
}
}
SetIfNull <- function(x, default) {
if (is.null(x = x)) {
return(default)
} else {
return(x)
}
}
# globalVariables(names = c('x', 'y', 'gene'), package = 'Seurat', add = TRUE)
SingleFeaturePlot <- function(
data.use,
feature,
data.plot,
pt.size,
pch.use,
cols.use,
dim.codes,
min.cutoff,
max.cutoff,
coord.fixed,
no.axes,
no.title = FALSE,
no.legend,
dark.theme,
vector.friendly = FALSE,
png.file = NULL,
png.arguments=c(10,10,100)
) {
#first, consider vector friendly case
if (vector.friendly) {
previous_call <- blank_call <- png_call <- match.call()
blank_call$pt.size <- -1
blank_call$vector.friendly <- FALSE
png_call$no.axes <- TRUE
png_call$no.legend <- TRUE
png_call$vector.friendly <- FALSE
png_call$no.title <- TRUE
blank_plot <- eval(blank_call, sys.frame(sys.parent()))
png_plot <- eval(png_call, sys.frame(sys.parent()))
png.file <- SetIfNull(x = png.file, default = paste0(tempfile(), ".png"))
ggsave(filename = png.file, plot = png_plot,
width = png.arguments[1],
height = png.arguments[2],
dpi = png.arguments[3])
to_return <- AugmentPlot(blank_plot, png.file)
file.remove(png.file)
return(to_return)
}
data.gene <- na.omit(object = data.frame(data.use[feature, ]))
# Check for quantiles
min.cutoff <- SetQuantile(cutoff = min.cutoff, data = data.gene)
max.cutoff <- SetQuantile(cutoff = max.cutoff, data = data.gene)
# Mask any values below the minimum and above the maximum values
data.gene <- sapply(
X = data.gene,
FUN = function(x) {
return(ifelse(test = x < min.cutoff, yes = min.cutoff, no = x))
}
)
data.gene <- sapply(
X = data.gene,
FUN = function(x) {
return(ifelse(test = x > max.cutoff, yes = max.cutoff, no = x))
}
)
data.plot$gene <- data.gene
# Stuff for break points
if (length(x = cols.use) == 1) {
brewer.gran <- brewer.pal.info[cols.use, ]$maxcolors
} else {
brewer.gran <- length(x = cols.use)
}
# Cut points
if (all(data.gene == 0)) {
data.cut <- 0
} else {
data.cut <- as.numeric(x = as.factor(x = cut(
x = as.numeric(x = data.gene),
breaks = brewer.gran
)))
}
data.plot$col <- as.factor(x = data.cut)
# Start plotting
p <- ggplot(data = data.plot, mapping = aes(x = x, y = y))
if (brewer.gran != 2) {
if (length(x = cols.use) == 1) {
p <- p + geom_point(
mapping = aes(color = col),
size = pt.size,
shape = pch.use
) + scale_color_brewer(palette = cols.use)
} else {
p <- p + geom_point(
mapping = aes(color = col),
size = pt.size,
shape = pch.use
) + scale_color_manual(values = cols.use)
}
} else {
if (all(data.plot$gene == data.plot$gene[1])) {
warning(paste0("All cells have the same value of ", feature, "."))
p <- p + geom_point(color = cols.use[1], size = pt.size, shape = pch.use)
} else {
p <- p + geom_point(
mapping = aes(color = gene),
size = pt.size,
shape = pch.use
) + scale_color_gradientn(
colors = cols.use,
guide = guide_colorbar(title = feature)
)
}
}
if (dark.theme) {
p <- p + DarkTheme()
}
if (no.axes) {
p <- p + theme(
axis.line = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
if (!no.title) p <- p + labs(title = feature, x ="", y="")
if (no.title) p <- p + labs(x ="", y="")
} else {
if (no.title) p <- p + labs(x = dim.codes[1], y = dim.codes[2])
if (!(no.title)) p <- p + labs(title = feature, x = dim.codes[1], y = dim.codes[2])
}
if (no.legend) {
p <- p + theme(legend.position = 'none')
}
if(coord.fixed) {
p <- p + coord_fixed()
}
return(p)
}
library(stats)
# Find the quantile of a data
#
# Converts a quantile in character form to a number regarding some data
# String form for a quantile is represented as a number prefixed with 'q'
# For example, 10th quantile is 'q10' while 2nd quantile is 'q2'
#
# Will only take a quantile of non-zero data values
#
SetQuantile <- function(cutoff, data) {
if (grepl(pattern = '^q[0-9]{1,2}$', x = as.character(x = cutoff), perl = TRUE)) {
this.quantile <- as.numeric(x = sub(
pattern = 'q',
replacement = '',
x = as.character(x = cutoff)
)) / 100
data <- unlist(x = data)
data <- data[data > 0]
cutoff <- quantile(x = data, probs = this.quantile)
}
return(as.numeric(x = cutoff))
}
# Reset Par
#
# Reset the graphing space to
# mfrow = c(1, 1)
#
ResetPar <- function(...) {
par(mfrow = c(1, 1), ...)
}
######################################################################################
# Additional utility functions
ligerToSeurat2<-function(liger, ...){
seurat<-ligerToSeurat(liger, ...)
seurat<-RenameCells(seurat, new.names = rownames([email protected]))
seurat<-regionslot(seurat)
[email protected][['liger_ident']]<-factor(liger@clusters)
return(seurat)
}
##############################
# spatial gene selection
###
# give option to do the averaging in raw gene space or scaled gene space
# both are by default false
# cell.var is the column in cell.data to split by
# see required utility function, ligerToSeurat_raw below
library(pastecs)
spatial_variable_select_clean <-function(liger_object, cell.var = "regions", use.raw = F,
use.scale = F, alpha = 0.01){
# convert to Seurat for averaging
tmp.seurat = ligerToSeurat_raw(liger_object, renormalize = T, raw.only = T)
[email protected][[cell.var]] = [email protected][[cell.var]]
tmp.seurat = SetIdent(tmp.seurat, ident.use = [email protected][[cell.var]])
# get average expression for each region (returns dataframe)
avg_lobe_expression_mat = AverageExpression(tmp.seurat, return.seurat = F,
use.raw = use.raw, use.scale = use.scale)
#Between lobe variability
mean<-rowMeans(avg_lobe_expression_mat)
var<-RowVar(avg_lobe_expression_mat)
#getting VMR
avg_lobe_expression_mat[['VMR']] = var/mean
avg_lobe_expression_mat[['logVMR']] = log(avg_lobe_expression_mat$VMR)
# getting spatially significant gene list
vmrs =as.matrix(avg_lobe_expression_mat[,"logVMR"])
# convert to density (empirical dist)
d = density(na.omit(vmrs))
ts_y = ts(d$y)
# inflection points of distribution
tp = turnpoints(ts_y)
idx = which.max(d$y[tp$tppos])