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男， 一个长大了才会遇到的帅哥， 稳健，潇洒，大方，靠谱。 一段生信缘，一棵技能树， 一枚大型测序工厂的螺丝钉， 一个随机森林中提灯觅食的津门旅客。 library(Seurat)
library(SeuratData)
levels(pbmc3k.final)
[1] "Naive CD4 T" "Memory CD4 T" "CD14+ Mono" "B" "CD8 T"
[6] "FCGR3A+ Mono" "NK" "DC" "Platelet"
VlnPlot(pbmc3k.final, "CD4",slot = "data")
 作为一个生物信息工程师，看到这样的图，请解释。 为什么CD14+ Mono和 Memory CD4 T 有怎么多的点，却没有小提琴呢？ 那， 我们要看看作图细节了。 > VlnPlot
function (object, features, cols = NULL, pt.size = 1, idents = NULL,
sort = FALSE, assay = NULL, group.by = NULL, split.by = NULL,
adjust = 1, y.max = NULL, same.y.lims = FALSE, log = FALSE,
ncol = NULL, combine = TRUE, slot = "data", ...)
{
return(ExIPlot(object = object, type = "violin", features = features,
idents = idents, ncol = ncol, sort = sort, assay = assay,
y.max = y.max, same.y.lims = same.y.lims, adjust = adjust,
pt.size = pt.size, cols = cols, group.by = group.by,
split.by = split.by, log = log, slot = slot, combine = combine,
...))
}
<bytecode: 0x1a20fce0>
<environment: namespace:Seurat>`
可惜并没有细节，再看ExIPlot。 > ExIPlot
Error: object 'ExIPlot' not found
不是显式函数啊。我们通过debug的方式进入函数内部。 > debug(VlnPlot)
> VlnPlot(pbmc3k.final, "CD4",slot = "data")
debugging in: VlnPlot(pbmc3k.final, "CD4", slot = "data")
debug: {
return(ExIPlot(object = object, type = "violin", features = features,
idents = idents, ncol = ncol, sort = sort, assay = assay,
y.max = y.max, same.y.lims = same.y.lims, adjust = adjust,
pt.size = pt.size, cols = cols, group.by = group.by,
split.by = split.by, log = log, slot = slot, combine = combine,
...))
}
Browse[2]>
debug: return(ExIPlot(object = object, type = "violin", features = features,
idents = idents, ncol = ncol, sort = sort, assay = assay,
y.max = y.max, same.y.lims = same.y.lims, adjust = adjust,
pt.size = pt.size, cols = cols, group.by = group.by, split.by = split.by,
log = log, slot = slot, combine = combine, ...))
Browse[2]> ExIPlot
function (object, features, type = "violin", idents = NULL, ncol = NULL,
sort = FALSE, assay = NULL, y.max = NULL, same.y.lims = FALSE,
adjust = 1, cols = NULL, pt.size = 0, group.by = NULL, split.by = NULL,
log = FALSE, combine = TRUE, slot = "data", ...)
{
assay <- assay %||% DefaultAssay(object = object)
DefaultAssay(object = object) <- assay
ncol <- ncol %||% ifelse(test = length(x = features) > 9,
yes = 4, no = min(length(x = features), 3))
data <- FetchData(object = object, vars = features, slot = slot)
features <- colnames(x = data)
if (is.null(x = idents)) {
cells <- colnames(x = object)
}
else {
cells <- names(x = Idents(object = object)[Idents(object = object) %in%
idents])
}
data <- data[cells, , drop = FALSE]
idents <- if (is.null(x = group.by)) {
Idents(object = object)[cells]
}
else {
object[[group.by, drop = TRUE]][cells]
}
if (!is.factor(x = idents)) {
idents <- factor(x = idents)
}
if (is.null(x = split.by)) {
split <- NULL
}
else {
split <- object[[split.by, drop = TRUE]][cells]
if (!is.factor(x = split)) {
split <- factor(x = split)
}
if (is.null(x = cols)) {
cols <- hue_pal()(length(x = levels(x = idents)))
cols <- Interleave(cols, InvertHex(hexadecimal = cols))
}
else if (length(x = cols) == 1 && cols == "interaction") {
split <- interaction(idents, split)
cols <- hue_pal()(length(x = levels(x = idents)))
}
else {
cols <- Col2Hex(cols)
}
if (length(x = cols) < length(x = levels(x = split))) {
cols <- Interleave(cols, InvertHex(hexadecimal = cols))
}
cols <- rep_len(x = cols, length.out = length(x = levels(x = split)))
names(x = cols) <- sort(x = levels(x = split))
}
if (same.y.lims && is.null(x = y.max)) {
y.max <- max(data)
}
plots <- lapply(X = features, FUN = function(x) {
return(SingleExIPlot(type = type, data = data[, x, drop = FALSE],
idents = idents, split = split, sort = sort, y.max = y.max,
adjust = adjust, cols = cols, pt.size = pt.size,
log = log, ...))
})
label.fxn <- switch(EXPR = type, violin = ylab, ridge = xlab,
stop("Unknown ExIPlot type ", type, call. = FALSE))
for (i in 1:length(x = plots)) {
key <- paste0(unlist(x = strsplit(x = features[i], split = "_"))[1],
"_")
obj <- names(x = which(x = Key(object = object) == key))
if (length(x = obj) == 1) {
if (inherits(x = object[[obj]], what = "DimReduc")) {
plots[[i]] <- plots[[i]] + label.fxn(label = "Embeddings Value")
}
else if (inherits(x = object[[obj]], what = "Assay")) {
next
}
else {
warning("Unknown object type ", class(x = object),
immediate. = TRUE, call. = FALSE)
plots[[i]] <- plots[[i]] + label.fxn(label = NULL)
}
}
else if (!features[i] %in% rownames(x = object)) {
plots[[i]] <- plots[[i]] + label.fxn(label = NULL)
}
}
if (combine) {
combine.args <- list(plots = plots, ncol = ncol)
combine.args <- c(combine.args, list(...))
if (!"legend" %in% names(x = combine.args)) {
combine.args$legend <- "none"
}
plots <- do.call(what = "CombinePlots", args = combine.args)
}
return(plots)
}
<bytecode: 0x19dc8580>
<environment: namespace:Seurat>
这一层函数也没讲小提琴如何画的，再看SingleExIPlot。 Browse[2]> SingleExIPlot
function (data, idents, split = NULL, type = "violin", sort = FALSE,
y.max = NULL, adjust = 1, pt.size = 0, cols = NULL, seed.use = 42,
log = FALSE)
{
if (!is.null(x = seed.use)) {
set.seed(seed = seed.use)
}
if (!is.data.frame(x = data) || ncol(x = data) != 1) {
stop("'SingleExIPlot requires a data frame with 1 column")
}
feature <- colnames(x = data)
data$ident <- idents
if ((is.character(x = sort) && nchar(x = sort) > 0) || sort) {
data$ident <- factor(x = data$ident, levels = names(x = rev(x = sort(x = tapply(X = data[,
feature], INDEX = data$ident, FUN = mean), decreasing = grepl(pattern = paste0("^",
tolower(x = sort)), x = "decreasing")))))
}
if (log) {
noise <- rnorm(n = length(x = data[, feature]))/200
data[, feature] <- data[, feature] + 1
}
else {
noise <- rnorm(n = length(x = data[, feature]))/1e+05
}
if (all(data[, feature] == data[, feature][1])) {
warning(paste0("All cells have the same value of ", feature,
"."))
}
else {
data[, feature] <- data[, feature] + noise
}
axis.label <- "Expression Level"
y.max <- y.max %||% max(data[, feature])
if (is.null(x = split) || type != "violin") {
vln.geom <- geom_violin
fill <- "ident"
}
else {
data$split <- split
vln.geom <- geom_split_violin
fill <- "split"
}
switch(EXPR = type, violin = {
x <- "ident"
y <- paste0("`", feature, "`")
xlab <- "Identity"
ylab <- axis.label
geom <- list(vln.geom(scale = "width", adjust = adjust,
trim = TRUE), theme(axis.text.x = element_text(angle = 45,
hjust = 1)))
jitter <- geom_jitter(height = 0, size = pt.size)
log.scale <- scale_y_log10()
axis.scale <- ylim
}, ridge = {
x <- paste0("`", feature, "`")
y <- "ident"
xlab <- axis.label
ylab <- "Identity"
geom <- list(geom_density_ridges(scale = 4), theme_ridges(),
scale_y_discrete(expand = c(0.01, 0)), scale_x_continuous(expand = c(0,
0)))
jitter <- geom_jitter(width = 0, size = pt.size)
log.scale <- scale_x_log10()
axis.scale <- function(...) {
invisible(x = NULL)
}
}, stop("Unknown plot type: ", type))
plot <- ggplot(data = data, mapping = aes_string(x = x, y = y,
fill = fill)[c(2, 3, 1)]) + labs(x = xlab, y = ylab,
title = feature, fill = NULL) + theme_cowplot() + theme(plot.title = element_text(hjust = 0.5))
plot <- do.call(what = "+", args = list(plot, geom))
plot <- plot + if (log) {
log.scale
}
else {
axis.scale(min(data[, feature]), y.max)
}
if (pt.size > 0) {
plot <- plot + jitter
}
if (!is.null(x = cols)) {
if (!is.null(x = split)) {
idents <- unique(x = as.vector(x = data$ident))
splits <- unique(x = as.vector(x = data$split))
labels <- if (length(x = splits) == 2) {
splits
}
else {
unlist(x = lapply(X = idents, FUN = function(pattern,
x) {
x.mod <- gsub(pattern = paste0(pattern, "."),
replacement = paste0(pattern, ": "), x = x,
fixed = TRUE)
x.keep <- grep(pattern = ": ", x = x.mod, fixed = TRUE)
x.return <- x.mod[x.keep]
names(x = x.return) <- x[x.keep]
return(x.return)
}, x = unique(x = as.vector(x = data$split))))
}
if (is.null(x = names(x = labels))) {
names(x = labels) <- labels
}
}
else {
labels <- levels(x = droplevels(data$ident))
}
plot <- plot + scale_fill_manual(values = cols, labels = labels)
}
return(plot)
}
<bytecode: 0x1a735330>
<environment: namespace:Seurat>
我们看到核心了，ggplot的代码。 geom <- list(vln.geom(scale = "width", adjust = adjust,
trim = TRUE), theme(axis.text.x = element_text(angle = 45,
hjust = 1)))
jitter <- geom_jitter(height = 0, size = pt.size)
log.scale <- scale_y_log10()
axis.scale <- ylim
这句子写的真美啊。 那我们就要来试一下了。记得退出debug模式哦。 undebug(VlnPlot)
p<-VlnPlot(pbmc3k.final, "CD4",slot = "data")
#ggplot对象中记录了一张图的所有信息，为了方便演示，我们只取数据出来。
head(p$data) # 从图中抠数据，学会了吗？
CD4 ident
AAACATACAACCAC 1.370958e-05 Memory CD4 T
AAACATTGAGCTAC -5.646982e-06 B
AAACATTGATCAGC 3.631284e-06 Memory CD4 T
AAACCGTGCTTCCG 6.328626e-06 CD14+ Mono
AAACCGTGTATGCG 4.042683e-06 NK
AAACGCACTGGTAC -1.061245e-06 Memory CD4 T
原汁原味的啊： ggplot(p$data,aes(ident,CD4)) + geom_violin() + theme_bw()
啥也没有：  加个抖动吧： ggplot(p$data,aes(ident,CD4)) + geom_violin() + geom_jitter()+ theme_bw()
 这下有点了。所以我们看到的点有左右的区分其实是抖出来的，本身数据的点应该是在一条直线上。然而，小提琴呢？ ggplot(p$data,aes(ident,CD4)) + geom_violin(scale = "width", adjust =1, trim = TRUE) + geom_jitter()+ theme_bw()
 这里我们用seurat内部绘制小提琴图的方式还原了我们问题：为什么CD14+ Mono和 Memory CD4 T 有怎么多的点，却没有小提琴呢？经过上面演示我们知道，其实默认的情况下，我们的数据是都没有小提琴的。所以，当务之急是抓紧时间看看geom_violin的帮助文档。 ?geom_violin
?geom_violin
?geom_violin
好了，我们知道一个关键的参数scale = "width"导致了这种局面，其他没有出现小提琴的应该是零值比例太多。 作为好奇，我们看看改一下adjust会有什么改变。 ggplot(p$data,aes(ident,CD4)) + geom_violin(scale = "width", adjust =.5, trim = TRUE) + geom_jitter()+ theme_bw()
 腰变细了，好玩。 既然已经基本锁定问题，我们如何画出都有小提琴的小提琴图呢？也许可以用的方法之一就是，数据过滤。 VlnPlot(subset(pbmc3k.final,CD4 > 0 ), "CD4")+ theme_bw()
 什么？改一下腰围？ VlnPlot(subset(pbmc3k.final,CD4 > 0 ), "CD4",adjust = .2)+ theme_bw()
 Seurat小提琴图为什么有的只有点儿？那是因为还有更多的点没忽视。
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