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NGScustomFunctions.R
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NGScustomFunctions.R
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### Custom functions by KJ ###
library(ggrepel)
library(cowplot)
library(patchwork)
library(ggplot2)
library(ggmosaic)
library(scales)
library(gplots)
options(warn=1)
###########################################################################
mycol <- c("burlywood4", "antiquewhite4", "antiquewhite3","burlywood2", "bisque1", "lightsalmon1",
"khaki1", "gold", "orange", "#E38900", "chocolate", "orange4",
"mediumorchid3", "#A58AFF", "plum2", "orchid1", "hotpink1", "lightpink3", "lightpink1", "coral1", "indianred3",
"#06A4FF","cornflowerblue", "slategray1", "cadetblue1",
"#99A800", "chartreuse3", "#00BC56", "#00C094","mediumturquoise","aquamarine", "darkseagreen1", "darkolivegreen1", "darkkhaki")
byr <- rev(RColorBrewer::brewer.pal(11,"RdYlBu")) %>% colorRampPalette
ryb <- RColorBrewer::brewer.pal(11,"RdYlBu") %>% colorRampPalette
wr <- colorRampPalette(rev(c("#A50026", "#D73027", "#F46D43", "#FDAE61", "#FEE090", "#FFFFBF", "white")))
wb <- colorRampPalette(c("white", "#E0F3F8", "#ABD9E9", "#74ADD1", "#4575B4", "#313695"))
############################################################################
'%!in%' <- function(x,y)!('%in%'(x,y))
############################################################################
convertHumanGeneList <- function(x) {
require("biomaRt")
human <- useMart("ensembl", dataset = "hsapiens_gene_ensembl")
mouse <- useMart("ensembl", dataset = "mmusculus_gene_ensembl")
genesV2 <- getLDS(attributes = c("hgnc_symbol"), filters = "hgnc_symbol", values = x, mart = human, attributesL = c("mgi_symbol"), martL = mouse, uniqueRows = T)
humanx <- unique(genesV2[, 2])
# Print the first 6 genes found to the screen
print(head(humanx))
return(humanx)
}
############################################################################
PFlog1pPF = function(x){
y = max(1, sum(x))
y = log(1 + x/y)*1e9/y
return(y)
}
############################################################################
RenameMetaCol <- function(seuObj,metaCol,namesOld,namesNew,levels=NULL) {
tmp <- seuObj@meta.data[,metaCol] %>% as.character
for (i in 1:length(namesOld)){
tmp[tmp == namesOld[[i]]] <- namesNew[[i]]
}
if (is.null(levels)){
seuObj@meta.data[,metaCol] <- factor(tmp)
} else {
seuObj@meta.data[,metaCol] <- factor(tmp,levels=levels)
}
Idents(seuObj) <- metaCol
return(seuObj)
}
############################################################################
scale01 <- function(x){(x-min(x))/(max(x)-min(x))}
############################################################################
notUniqueGenes <- function(object) {
rownames(object)[!(isUnique(rownames(object)))]
}
############################################################################
sumNotUniqueGenes <- function(macExp) {
genList <- unique(notUniqueGenes(macExp))
myList <- lapply(genList, function(gene) {
list(name = gene, sums = colSums(macExp[rownames(macExp) == gene,]))
})
tmp <- do.call(rbind, lapply(myList, function(x) x$sums))
rownames(tmp) <- genList
macExp <- rbind(macExp[!(rownames(macExp) %in% genList), ], tmp)
return(macExp)
}
############################################################################
wcsv <- function(data.frame,filename="my.csv",append= "FALSE",col_names="TRUE", num_threads = readr_threads()){
data.frame <- cbind(rownames(data.frame),data.frame)
write_csv(data.frame,filename,append)
}
############################################################################
addNewlines <- function(x, ct = 25){
n = c(diff(cumsum(nchar(x)) %% ct), 0)
x = paste0(x, ifelse(n == 0, "", ifelse(n > 0, " ", "\n")), collapse = "")
return(x)
}
############################################################################
find.pK <- function(sweep.stats) # suppres creation of graphics
{
"%ni%" <- Negate("%in%")
if ("AUC" %ni% colnames(sweep.stats) == TRUE) {
bc.mvn <- as.data.frame(matrix(0L,
nrow = length(unique(sweep.stats$pK)),
ncol = 5
))
colnames(bc.mvn) <- c(
"ParamID", "pK", "MeanBC", "VarBC",
"BCmetric"
)
bc.mvn$pK <- unique(sweep.stats$pK)
bc.mvn$ParamID <- 1:nrow(bc.mvn)
x <- 0
for (i in unique(bc.mvn$pK)) {
x <- x + 1
ind <- which(sweep.stats$pK == i)
bc.mvn$MeanBC[x] <- mean(sweep.stats[ind, "BCreal"])
bc.mvn$VarBC[x] <- sd(sweep.stats[ind, "BCreal"])^2
bc.mvn$BCmetric[x] <- mean(sweep.stats[ind, "BCreal"]) / (sd(sweep.stats[
ind,
"BCreal"
])^2)
}
par(mar = rep(1, 4))
return(bc.mvn)
}
if ("AUC" %in% colnames(sweep.stats) == TRUE) {
bc.mvn <- as.data.frame(matrix(0L,
nrow = length(unique(sweep.stats$pK)),
ncol = 6
))
colnames(bc.mvn) <- c(
"ParamID", "pK", "MeanAUC", "MeanBC",
"VarBC", "BCmetric"
)
bc.mvn$pK <- unique(sweep.stats$pK)
bc.mvn$ParamID <- 1:nrow(bc.mvn)
x <- 0
for (i in unique(bc.mvn$pK)) {
x <- x + 1
ind <- which(sweep.stats$pK == i)
bc.mvn$MeanAUC[x] <- mean(sweep.stats[ind, "AUC"])
bc.mvn$MeanBC[x] <- mean(sweep.stats[ind, "BCreal"])
bc.mvn$VarBC[x] <- sd(sweep.stats[ind, "BCreal"])^2
bc.mvn$BCmetric[x] <- mean(sweep.stats[ind, "BCreal"]) / (sd(sweep.stats[
ind,
"BCreal"
])^2)
}
par(mar = rep(1, 4))
x <- plot(
x = bc.mvn$ParamID, y = bc.mvn$MeanAUC, pch = 18,
col = "black", cex = 0.75, xlab = NA, ylab = NA
)
x <- lines(
x = bc.mvn$ParamID, y = bc.mvn$MeanAUC, col = "black",
lty = 2
)
par(new = TRUE)
return(bc.mvn)
}
}
############################################################################
library(viridis)
umapp <- function(macExp, i) {
theme_feature <- theme_classic() +
theme(
axis.text = element_text(24), axis.ticks = element_blank(),
axis.title = element_text(size = 20),
legend.position = "bottom", legend.text = element_text(size = 18), legend.title = element_text(size = 20),
plot.title = element_text(face = "italic", size = 60),
legend.key.size = unit(1, "cm")
)
plot <- ggplot(macExp, aes(x = wnnUMAP_1, y = wnnUMAP_2)) +
geom_jitter(size = 0.5, alpha = 0.5, aes_string(color = as.name(i))) + # czemu okregi a nie koła?
labs(title = i, color = "log2(RPKM+1)") +
scale_colour_gradientn(colours = viridis(50)) + # rev(inferno(50))
# breaks=c(min,max))+
# , labels=c("min", "max"))+
xlab("wnnUMAP_1") +
ylab("wnnUMAP_2") +
theme_feature
return(plot)
}
############################################################################
ensembleToHugo <- function(seuObj,assay){
require("biomaRt")
seuObj@active.assay <- assay
mart <- useMart(biomart = "ENSEMBL_MART_ENSEMBL",dataset="mmusculus_gene_ensembl")
lol <- getBM(filters = "ensembl_gene_id",
attributes = c("ensembl_gene_id", "external_gene_name"),
values = rownames(seuObj@assays[["RNA"]]@data), mart = mart)
ochseg <- subset(seuObj, features = seuObj$ensembl_gene_id )
lol$ensembl_gene_id <- lol$ensembl_gene_id[match(lol$ensembl_gene_id, rownames(lol))]
rownames(seuObj@assays[["RNA"]]@data) <- lol$external_gene_name
}
############################################################################
testmac <- function(macExp, j) {
for (i in j) {
if (identical(colnames(macExp)[which(grepl(j, colnames(macExp)))], character(0))) {
print(paste(i, " ", "is not in genes pool"))
} else {
return(colnames(macExp)[which(grepl(j, colnames(macExp)))])
}
}
}
############################################################################
testmacrev <- function(macExp, j) {
for (i in j) {
if (identical(rownames(macExp)[which(grepl(j, rownames(macExp)))], character(0))) {
print(paste(i, " ", "is not in genes pool"))
} else {
return(rownames(macExp)[which(grepl(j, rownames(macExp)))])
}
}
}
############################################################################
testseu <- function(seuobj, j) {
geneList <- list()
n <- 0
for (i in j) {
n <- n + 1
if (identical(rownames(seuobj)[which(grepl(i, rownames(seuobj)))], character(0))) {
print(paste(i, " ", "is not in genes pool"))
} else {
print(rownames(seuobj)[which(grepl(i, rownames(seuobj)))])
geneList[[n]] <- rownames(seuobj)[which(grepl(i, rownames(seuobj)))]
}
}
return(unlist(geneList))
}
############################################################################
h2 <- function(i) {
i[1:5, 1:5]
}
############################################################################
perc99 <- function(macExp) {
for (i in colnames(macExp[, -c(1:4)])) {
q99 <- quantile(macExp[which(macExp[, i] > 0), i], probs = 0.99)
macExp[(which(macExp[, i] > q99)), i] <- q99
}
return(macExp)
}
############################################################################
greyClusters <- function(seuObj, idents, redu = "wnn.umap") {
cellsInCluster <- WhichCells(seuObj, idents = idents)
DimPlot(seuObj,
reduction = redu,
label = F, group.by = "ident",
cells.highlight = list(cellsInCluster), cols.highlight = c("darkred"),
cols = "grey", pt.size = 0.3
) + ggtitle(idents) + theme(legend.position = "none")
}
############################################################################
percTab <- function(alldata) {
table(Idents(alldata), alldata$lab) %>%
prop.table(margin = 2) %>%
round(digits = 4)
}
############################################################################
mybarplot <- function(height, x = "Count", color = "p.adjust", showCategory = 8,
font.size = 12, title = "", label_format = 30, ...) { # barplot working on enrichKEGG and enrichPathway result objects (default stopped to working xd)
object <- height
colorBy <- match.arg(color, c("pvalue", "p.adjust", "qvalue"))
if (x == "geneRatio" || x == "GeneRatio") {
x <- "GeneRatio"
} else if (x == "count" || x == "Count") {
x <- "Count"
}
df <- fortify(object@result[1:showCategory, ], by = x)
df$Description <- factor(df$Description, levels = rev(df$Description))
if (colorBy %in% colnames(df)) {
p <- ggplot(df, aes_string(
x = x, y = "Description",
fill = colorBy
)) +
theme_dose(font.size) +
scale_fill_continuous(
low = "red",
high = "blue", name = color, guide = guide_colorbar(reverse = TRUE)
)
} else {
p <- ggplot(df, aes_string(
x = x, y = "Description",
fill = "Description"
)) +
theme_dose(font.size) +
theme(legend.position = "none")
}
p + geom_col() + ggtitle(title) + xlab(NULL) + ylab(NULL)
}
############################################################################
RenameGenesSeurat <- function(obj = ls.Seurat[[i]], newnames = HGNC.updated[[i]]$Suggested.Symbol) { # Replace gene names in different slots of a Seurat object. Run this before integration. Run this before integration. It only changes obj@assays$RNA@counts, @data and @scale.data.
print("Run this before integration. It only changes obj@assays$RNA@counts, @data and @scale.data.")
RNA <- obj@assays$RNA
if (nrow(RNA) == length(newnames)) {
if (length(RNA@counts)) RNA@counts@Dimnames[[1]] <- newnames
if (length(RNA@data)) RNA@data@Dimnames[[1]] <- newnames
if (length(RNA@scale.data)) RNA@scale.data@Dimnames[[1]] <- newnames
} else {
"Unequal gene sets: nrow(RNA) != nrow(newnames)"
}
obj@assays$RNA <- RNA
return(obj)
}
############################################################################
h2m <- function(i) { # human-mouse
lapply(i, function(j) {
tmp <- unlist(strsplit(j, ""))
tmp <- c(tmp[[1]], tolower(tmp[-1]))
return(paste(tmp, collapse = ""))
})
}
# as.character(expression(CD62L, CCR7, CXCR4))
#############################################################################
mosPlot <- function(labels,condition,legend=T,title='Percentage of cells in cluster originated from each sample (width of the column = number of cells in cluster)',
xlab="Cluster",ylab="Condition", eh=F){
#### Mosaic plot
cluster_names_all <- paste0(labels %>% table %>% sort %>% names)
x <- data.frame(cluster_id = labels, condition = condition)
if (eh == T){
x$condition <- factor(x$condition, levels = c("control","CSI","CSI+aPD1"))
return(ggplot(data = x) +
geom_mosaic(aes(x = product(cluster_id), fill=condition), na.rm=F, show.legend = legend) +
scale_fill_manual(values = rainbow(length(condition %>% table),s=0.8,v=0.8), breaks = c("control","CSI","CSI+aPD1")) +
labs(x = xlab, y = ylab, title=title) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, face="bold")) +
geom_hline(yintercept=0.5) +
geom_text(aes(0,0.5,label = "50 %", vjust = -1)))
}
if (eh == F){
#### ggplot geom_mosaic
x$condition <- factor(x$condition, levels = rev(condition %>% table %>% names))
mymosplot <- ggplot(data = x) +
geom_mosaic(aes(x = product(cluster_id), fill=condition), na.rm=F, show.legend = legend) +
scale_fill_manual(values = sample(mycol,(length(condition %>% table))),
breaks = levels(x$condition), guide = guide_legend(reverse = TRUE)) +
labs(x = xlab, y = ylab, title=title) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, face="bold"),
panel.background = element_rect(fill = "gray94", size = 2, linetype = "solid")) +
geom_hline(yintercept=0) +
geom_hline(yintercept=1) +
geom_text(aes(0,-0.02,label = "0 %", vjust = -1)) +
geom_text(aes(0,0.98,label = "100 %", vjust = -1))
return(
mymosplot)#theme(legend.position="left"))
}
}
#############################################################################
#############################################################################
mosPlotNorm <- function(labels,resp,cond="resp"){
if (cond %!in% c("resp","cond")){
stop("wrong condition")
}
cluster_names_all <- paste0(labels %>% table %>% sort %>% names)
x <- data.frame(cluster_id = labels, cond = resp)
##normalize the response
x <- table(x)
if (cond == "cond"){
control = sum(x[,"control"])
noresp = sum(x[,"CSI"])
resp = sum(x[,"CSI+aPD1"])
x[,"control"] <- round(x[,"control"] / (control/(resp + control + noresp)))
x[,"CSI"] <- round(x[,"CSI"] / (noresp/(resp + control + noresp)))
x[,"CSI+aPD1"] <- round(x[,"CSI+aPD1"] / (resp/(resp + control + noresp)))
df <- as.data.frame(x)
df = data.frame(x = rep(df$cluster_id, times = df$Freq), y = rep(df$cond, times = df$Freq))
colnames(df) <- c("cluster","condition")
return(mosPlot(df$cluster, df$condition, legend = T, eh=T, ylab="Condition",title='Proportion of cells in cluster originated from different condition after normalization')
)
}
if (cond == "resp") {
control = sum(x[,"control"])
noresp = sum(x[,"no resp"])
resp = sum(x[,"resp"])
x[,"control"] <- round(x[,"control"] / (control/(resp + control + noresp)))
x[,"no resp"] <- round(x[,"no resp"] / (noresp/(resp + control + noresp)))
x[,"resp"] <- round(x[,"resp"] / (resp/(resp + control + noresp)))
df <- as.data.frame(x)
df = data.frame(x = rep(df$cluster_id, times = df$Freq), y = rep(df$cond, times = df$Freq))
colnames(df) <- c("cluster","response")
return(mosPlot(df$cluster, df$response, legend = T, ylab="Response status",title='Proportion of cells in cluster originated from different response status after normalization')
)
}
}
#############################################################################
############################################################################
fp <- function(object, features, dims = c(1, 2), cells = NULL, cols = if (blend) {
c("lightgrey", "#ff0000", "#00ff00")
} else {
c("#FDE725FF", "#440154FF")
}, mycol=rev(ryb(50)), pt.size=NULL, pt.s = 0.3, order = FALSE, min.cutoff = NA, max.cutoff = NA,
reduction = NULL, split.by = NULL, keep.scale = "feature",
shape.by = NULL, slot = "data", blend = FALSE, blend.threshold = 0.5,
label = FALSE, label.size = 7, label.color = "midnightblue", label.bg.color="white", bg.r = 0.1, repel = TRUE, max.iter=100,autosize=FALSE,
alpha=0.7, ncol = NULL, coord.fixed = FALSE, by.col = TRUE, sort.cell = NULL, jitter=0,
interactive = FALSE, combine = TRUE, raster = NULL, split.ncol=NULL, split.nrow=NULL) {
library(patchwork)
if (!is.null(x = sort.cell)) {
warning("The sort.cell parameter is being deprecated. Please use the order ",
"parameter instead for equivalent functionality.",
call. = FALSE, immediate. = TRUE
)
if (isTRUE(x = sort.cell)) {
order <- sort.cell
}
}
if (interactive) {
return(IFeaturePlot(
object = object, feature = features[1],
dims = dims, reduction = reduction, slot = slot
))
}
if (!(is.null(x = keep.scale)) && !(keep.scale %in% c(
"feature",
"all"
))) {
stop("`keep.scale` must be set to either `feature`, `all`, or NULL")
}
no.right <- theme(
axis.line.y.right = element_blank(), axis.ticks.y.right = element_blank(),
axis.text.y.right = element_blank(), axis.title.y.right = element_text(
face = "bold.italic",
size = 14, margin = margin(r = 7)
)
)
reduction <- reduction %||% DefaultDimReduc(object = object)
if (length(x = dims) != 2 || !is.numeric(x = dims)) {
stop("'dims' must be a two-length integer vector")
}
if (blend && length(x = features) != 2) {
stop("Blending feature plots only works with two features")
}
if (blend) {
default.colors <- eval(expr = formals(fun = FeaturePlot)$cols)
cols <- switch(EXPR = as.character(x = length(x = cols)),
`0` = {
warning("No colors provided, using default colors",
call. = FALSE, immediate. = TRUE
)
default.colors
},
`1` = {
warning("Only one color provided, assuming specified is double-negative and augmenting with default colors",
call. = FALSE, immediate. = TRUE
)
c(cols, default.colors[2:3])
},
`2` = {
warning("Only two colors provided, assuming specified are for features and agumenting with '",
default.colors[1], "' for double-negatives",
call. = FALSE, immediate. = TRUE
)
c(default.colors[1], cols)
},
`3` = cols,
{
warning("More than three colors provided, using only first three",
call. = FALSE, immediate. = TRUE
)
cols[1:3]
}
)
}
if (blend && length(x = cols) != 3) {
stop("Blending feature plots only works with three colors; first one for negative cells")
}
dims <- paste0(Key(object = object[[reduction]]), dims)
cells <- cells %||% colnames(x = object)
data <- FetchData(object = object, vars = c(
dims, "ident",
features
), cells = cells, slot = slot)
if (ncol(x = data) < 4) {
stop("None of the requested features were found: ", paste(features,
collapse = ", "
), " in slot ", slot, call. = FALSE)
} else if (!all(dims %in% colnames(x = data))) {
stop("The dimensions requested were not found", call. = FALSE)
}
features <- colnames(x = data)[4:ncol(x = data)]
min.cutoff <- mapply(FUN = function(cutoff, feature) {
return(ifelse(test = is.na(x = cutoff), yes = min(data[
,
feature
]), no = cutoff))
}, cutoff = min.cutoff, feature = features)
max.cutoff <- mapply(FUN = function(cutoff, feature) {
return(ifelse(test = is.na(x = cutoff), yes = max(data[
,
feature
]), no = cutoff))
}, cutoff = max.cutoff, feature = features)
check.lengths <- unique(x = vapply(X = list(
features, 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")
}
brewer.gran <- ifelse(test = length(x = cols) == 1, yes = brewer.pal.info[cols, ]$maxcolors, no = length(x = cols))
data[, 4:ncol(x = data)] <- sapply(
X = 4:ncol(x = data),
FUN = function(index) {
data.feature <- as.vector(x = data[, index])
min.use <- Seurat:::SetQuantile(cutoff = min.cutoff[index -
3], data.feature)
max.use <- Seurat:::SetQuantile(cutoff = max.cutoff[index -
3], data.feature)
data.feature[data.feature < min.use] <- min.use
data.feature[data.feature > max.use] <- max.use
if (brewer.gran == 2) {
return(data.feature)
}
data.cut <- if (all(data.feature == 0)) {
0
} else {
as.numeric(x = as.factor(x = cut(
x = as.numeric(x = data.feature),
breaks = brewer.gran
)))
}
return(data.cut)
}
)
colnames(x = data)[4:ncol(x = data)] <- features
rownames(x = data) <- cells
data$split <- if (is.null(x = split.by)) {
RandomName()
} else {
switch(EXPR = split.by,
ident = Idents(object = object)[cells,
drop = TRUE
],
object[[split.by, drop = TRUE]][cells,
drop = TRUE
]
)
}
if (!is.factor(x = data$split)) {
data$split <- factor(x = data$split)
}
if (!is.null(x = shape.by)) {
data[, shape.by] <- object[[shape.by, drop = TRUE]]
}
plots <- vector(mode = "list", length = ifelse(test = blend,
yes = 4, no = length(x = features) * length(x = levels(x = data$split))
))
xlims <- c(floor(x = min(data[, dims[1]])), ceiling(x = max(data[
,
dims[1]
])))
ylims <- c(floor(min(data[, dims[2]])), ceiling(x = max(data[
,
dims[2]
])))
if (blend) {
ncol <- 4
color.matrix <- Seurat:::BlendMatrix(
two.colors = cols[2:3], col.threshold = blend.threshold,
negative.color = cols[1]
)
cols <- cols[2:3]
colors <- list(
color.matrix[, 1], color.matrix[1, ],
as.vector(x = color.matrix)
)
}
for (i in 1:length(x = levels(x = data$split))) {
ident <- levels(x = data$split)[i]
data.plot <- data[as.character(x = data$split) == ident, ,
drop = FALSE
]
if (blend) {
features <- features[1:2]
no.expression <- features[colMeans(x = data.plot[
,
features
]) == 0]
if (length(x = no.expression) != 0) {
stop("The following features have no value: ",
paste(no.expression, collapse = ", "),
call. = FALSE
)
}
data.plot <- cbind(
data.plot[, c(dims, "ident")],
Seurat:::BlendExpression(data = data.plot[, features[1:2]])
)
features <- colnames(x = data.plot)[4:ncol(x = data.plot)]
}
for (j in 1:length(x = features)) {
feature <- features[j]
if (blend) {
cols.use <- as.numeric(x = as.character(x = data.plot[
,
feature
])) + 1
cols.use <- colors[[j]][sort(x = unique(x = cols.use))]
} else {
cols.use <- NULL
}
data.single <- data.plot[, c(
dims, "ident", feature,
shape.by
)]
plot <- mySingleDimPlot(
data = data.single, dims = dims, alpha=alpha,
col.by = feature, order = order, pt.size = pt.size,
cols = cols.use, shape.by = shape.by, label = FALSE,
raster = raster, my.pt.size = pt.s, autosize = autosize,
jitter = jitter
) + scale_x_continuous(limits = xlims) +
scale_y_continuous(limits = ylims) + theme_cowplot() +
Seurat:::CenterTitle()
if (label) {
plot <- fplabelClustersWithOutilnedText(plot = plot, id = "ident", repel = repel, size = label.size, color = label.color, bg.color=label.bg.color, bg.r=bg.r,max.iter=max.iter)
}
if (length(x = levels(x = data$split)) > 1) {
plot <- plot + theme(panel.border = element_rect(
fill = NA,
colour = "black"
))
plot <- plot + if (i == 1) {
labs(title = feature)
} else {
labs(title = NULL)
}
if (j == length(x = features) && !blend) {
suppressMessages(expr = plot <- plot + scale_y_continuous(
sec.axis = dup_axis(name = ident),
limits = ylims
) + no.right)
}
if (j != 1) {
plot <- plot + theme(
axis.line.y = element_blank(),
axis.ticks.y = element_blank(), axis.text.y = element_blank(),
axis.title.y.left = element_blank()
)
}
if (i != length(x = levels(x = data$split))) {
plot <- plot + theme(
axis.line.x = element_blank(),
axis.ticks.x = element_blank(), axis.text.x = element_blank(),
axis.title.x = element_blank()
)
}
} else {
plot <- plot + labs(title = feature)
}
if (!blend) {
plot <- plot + guides(color = NULL)
cols.grad <- cols
if (length(x = cols) == 1) {
plot <- plot + scale_color_brewer(palette = cols)
} else if (length(x = cols) > 1) {
unique.feature.exp <- unique(data.plot[, feature])
if (length(unique.feature.exp) == 1) {
warning(
"All cells have the same value (",
unique.feature.exp, ") of ", feature, "."
)
if (unique.feature.exp == 0) {
cols.grad <- cols[1]
} else {
cols.grad <- cols
}
}
plot <- suppressMessages(expr = plot + scale_color_gradientn(
colors = cols.grad,
guide = "colorbar"
))
}
}
if (!(is.null(x = keep.scale)) && keep.scale == "feature" &&
!blend) {
max.feature.value <- max(data.single[, feature])
min.feature.value <- min(data.single[, feature])
plot <- suppressMessages(plot & scale_color_gradientn(
colors = mycol,
limits = c(min.feature.value, max.feature.value)
))
}
if (coord.fixed) {
plot <- plot + coord_fixed()
}
plot <- plot
plots[[(length(x = features) * (i - 1)) + j]] <- plot
}
}
if (blend) {
blend.legend <- Seurat:::BlendMap(color.matrix = color.matrix)
for (ii in 1:length(x = levels(x = data$split))) {
suppressMessages(expr = plots <- append(
x = plots,
values = list(blend.legend + scale_y_continuous(
sec.axis = dup_axis(name = ifelse(test = length(x = levels(x = data$split)) >
1, yes = levels(x = data$split)[ii], no = "")),
expand = c(0, 0)
) + labs(
x = features[1], y = features[2],
title = if (ii == 1) {
paste("Color threshold:", blend.threshold)
} else {
NULL
}
) + no.right), after = 4 * ii - 1
))
}
}
plots <- Filter(f = Negate(f = is.null), x = plots)
if (is.null(x = ncol)) {
ncol <- 2
if (length(x = features) == 1) {
ncol <- 1
}
if (length(x = features) > 6) {
ncol <- 3
}
if (length(x = features) > 9) {
ncol <- 4
}
}
ncol <- ifelse(test = is.null(x = split.by) || blend, yes = ncol,
no = length(x = features))
legend <- if (blend) {
"none"
} else {
split.by %iff% "none"
}
if (combine) {
if (by.col && !is.null(x = split.by) && !blend) {
plots <- lapply(X = plots, FUN = function(x) {
return(suppressMessages(expr = x + theme_cowplot() +
ggtitle("") + scale_y_continuous(
sec.axis = dup_axis(name = ""),
limits = ylims
) + no.right))
})
nsplits <- length(x = levels(x = data$split))
idx <- 1
for (i in (length(x = features) * (nsplits - 1) +
1):(length(x = features) * nsplits)) {
plots[[i]] <- suppressMessages(expr = plots[[i]] +
scale_y_continuous(
sec.axis = dup_axis(name = features[[idx]]),
limits = ylims
) + no.right)
idx <- idx + 1
}
idx <- 1
for (i in which(x = 1:length(x = plots) %% length(x = features) ==
1)) {
plots[[i]] <- plots[[i]] + ggtitle(levels(x = data$split)[[idx]]) +
theme(plot.title = element_text(hjust = 0.5))
idx <- idx + 1
}
idx <- 1
if (length(x = features) == 1) {
for (i in 1:length(x = plots)) {
plots[[i]] <- plots[[i]] + ggtitle(levels(x = data$split)[[idx]]) +
theme(plot.title = element_text(hjust = 0.5))
idx <- idx + 1
}
ncol <- 1
nrow <- nsplits
} else {
if (is.null(split.ncol)){
nrow <- split.by %iff% length(x = levels(x = data$split))
}
else {
nrow <- split.ncol
ncol <- split.nrow
}
}
plots <- plots[c(do.call(what = rbind, args = split(
x = 1:length(x = plots),
f = ceiling(x = seq_along(along.with = 1:length(x = plots)) / length(x = features))
)))]
plots <- wrap_plots(plots, ncol = nrow, nrow = ncol)
if (!is.null(x = legend) && legend == "none") {
plots <- plots & NoLegend()
}
} else {
plots <- wrap_plots(plots, ncol = ncol, nrow = split.by %iff%
length(x = levels(x = data$split)))
}
if (!is.null(x = legend) && legend == "none") {
plots <- plots & NoLegend()
}
if (!(is.null(x = keep.scale)) && keep.scale == "all" &&
!blend) {
max.feature.value <- max(data.plot[, features])
min.feature.value <- min(data.plot[, features])
plots <- suppressMessages(plots & scale_color_gradientn(
colors = mycol,
limits = c(min.feature.value, max.feature.value)
))
}
}
return(plots)
}
############################################################################
fp2 <- function(object, features, dims = c(1, 2), cells = NULL, cols = if (blend) {
c("lightgrey", "#ff0000", "#00ff00")
} else {
c("#FDE725FF", "#440154FF")
}, pt.size=NULL, pt.s = 1, order = FALSE, min.cutoff = NA, max.cutoff = NA,
reduction = NULL, split.by = NULL, keep.scale = "feature",
shape.by = NULL, slot = "data", blend = FALSE, blend.threshold = 0.5,
label = FALSE, label.size = 7, label.color = "midnightblue", label.bg.color="white", bg.r = 0.1, repel = TRUE, max.iter=1,autosize=FALSE,
alpha=0.7, ncol = NULL, coord.fixed = FALSE, by.col = TRUE, sort.cell = NULL,
interactive = FALSE, combine = TRUE, raster = NULL, split.ncol=NULL, split.nrow=NULL) {
library(patchwork)
if (!is.null(x = sort.cell)) {
warning("The sort.cell parameter is being deprecated. Please use the order ",
"parameter instead for equivalent functionality.",
call. = FALSE, immediate. = TRUE
)
if (isTRUE(x = sort.cell)) {
order <- sort.cell
}
}
if (interactive) {
return(IFeaturePlot(
object = object, feature = features[1],
dims = dims, reduction = reduction, slot = slot
))
}
if (!(is.null(x = keep.scale)) && !(keep.scale %in% c(
"feature",
"all"
))) {
stop("`keep.scale` must be set to either `feature`, `all`, or NULL")
}
no.right <- theme(
axis.line.y.right = element_blank(), axis.ticks.y.right = element_blank(),
axis.text.y.right = element_blank(), axis.title.y.right = element_text(
face = "bold.italic",
size = 14, margin = margin(r = 7)
)
)
reduction <- reduction %||% DefaultDimReduc(object = object)
if (length(x = dims) != 2 || !is.numeric(x = dims)) {
stop("'dims' must be a two-length integer vector")
}
if (blend && length(x = features) != 2) {
stop("Blending feature plots only works with two features")
}
if (blend) {
default.colors <- eval(expr = formals(fun = FeaturePlot)$cols)
cols <- switch(EXPR = as.character(x = length(x = cols)),