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| library(Seurat)
library(ggsci)
library(tidyverse)
f_df_order <- function(df, oN, groupN){
df <- as.data.frame(df)
for(oi in levels(df[[groupN]])){
idx <- df[[groupN]] == oi
tmp <- df[idx, ]
tmp <- tmp[order(tmp[[oN]], decreasing = T),]
df[idx, ] <- tmp
}
df
}
f_metaG2G <- function(metaG, matrixN=F, downSample=NULL){
res <- list()
alltype <- unique(metaG[[1]])
for(type in alltype){
res[[type]] <- rownames(metaG)[metaG[[1]] == type]
if (matrixN){
res[[type]] <- gsub('-','.',res[[type]])
}
}
if(!is.null(downSample)){
pTb <- f_br_cluster(NULL, downSample, metaG)
for (nM in colnames(pTb)){
tmp <- pTb[nM]
pTb[nM][tmp>0] <- min(tmp[tmp>0])
}
pTb <- apply(X = pTb, FUN = max, MARGIN=1)
for(nM in names(pTb)){
res[[nM]] <- sample(x = res[[nM]], size = pTb[nM])
}
}
res
}
f_scale_t <- function(matrixA){
t(scale(t(as.matrix(matrixA))))
}
f_matrix_groupMean <- function(matrixA, group, matrixN = T, normal_distribution=F, downSample=NULL, autoG2G=T, scale=F){
if(!matrixN){
matrixA <- as.data.frame(as.matrix(matrixA))
}
res <- data.frame(row.names = rownames(matrixA))
if(autoG2G){
group <- f_metaG2G(group, matrixN = matrixN, downSample = downSample)
}
matrixA <- matrixA[, Reduce(x = group, f = c)]
if(!normal_distribution){
matrixA <- f_rank_transformation(matrixA)
}else{
if(scale){
matrixA <- f_scale_t(matrixA)
}
}
rowF <- rowMeans
for(name in names(group)){
if (length(group[[name]]) == 1){
res[[name]] <- matrixA[,group[[name]]]
}else{
res[[name]] <- rowF(matrixA[,group[[name]]])
}
}
res
}
f_matrix_groupMean_g <- function(groupN, matrixA, group, matrixN = T, normal_distribution=F, autoG2G=T, scale=F){
if(!matrixN){
matrixA <- as.data.frame(as.matrix(matrixA))
}
res <- data.frame(row.names = rownames(matrixA))
if(autoG2G){
group <- f_metaG2G(group, matrixN = matrixN, downSample = downSample)
}
group <- Reduce(x = group, f = c)
matrixA <- matrixA[, group]
if(!normal_distribution){
matrixA <- f_rank_transformation(matrixA)
}else{
if(scale){
matrixA <- f_scale_t(matrixA)
}
}
groupN <- f_metaG2G(groupN, matrixN = matrixN)
rowF <- rowMeans
for(name in names(groupN)){
tmp <- (group %in% groupN[[name]])
if (sum(tmp) == 1){
res[[name]] <- matrixA[,tmp]
}else{
res[[name]] <- rowF(matrixA[,tmp])
}
}
res
}
require(reshape2)
require(ggplot2)
f_matrix_heatmap <- function(dfA, levels = NULL, xlevels=NULL){
dfA <- as.data.frame(dfA)
dfA$df_ID <- rownames(dfA)
dfm <- melt(dfA, na.rm = T, id.vars = c('df_ID'))
if(is.null(xlevels)){
dfm$variable <- factor(x = as.character(dfm$variable), ordered = T)
}else{
dfm$variable <- factor(x = as.character(dfm$variable), levels = xlevels)
}
if (length(levels) > 0){
dfm$df_ID <- factor(x = as.character(dfm$df_ID), levels = rev(levels))
}
p <- ggplot(dfm, aes(x=variable, y=df_ID))
p <- p + geom_tile(aes(fill=value))
p <- p + scale_fill_gradient(low = 'white', high = 'red')
p <- p + xlab("samples") + theme_bw() + theme(panel.grid.major = element_blank()) + theme(legend.key=element_blank())
p <- p + theme(axis.text.x=element_text(angle=45, hjust=1, vjust=1))
p <- p + labs(x=NULL, y=NULL)
p
}
|