单细胞水平的差异基因热图的一些展示方式

发布于 2022-02-25  156 次阅读


定义绘图函数

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){
    # 转换前,先增加一列ID列,保存行名字
    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 + scale_fill_gradient(low = 'steel blue', high = 'pink')
#     p <- p + scale_fill_gradientn(colours = c('#3E5CC5','#65B48E','#E6EB00','#E64E00'))
    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) # 删除xy轴标题 
    p
}

 

Seurat自带的绘图

topgene <- FindAllMarkers(sce, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
topgene %>%
    group_by(cluster) %>%
    top_n(n = 10, wt = avg_log2FC) %>%
    f_df_order('avg_log2FC', 'cluster') -> top10

 

p <- DoHeatmap(sce, features = top10$gene, assay = 'integrated', label = F, disp.min = -0.5, disp.max = 1) +  scale_fill_gradientn(colours = c('#3E5CC5','#65B48E', '#E6EB00','red'))
ggsave(p, filename = 'fig1.C1_12inch.pdf', width = 12, height = 12)

p <- DoHeatmap(sce, features = top10$gene, assay = 'integrated', label = F, disp.min = -0.5, disp.max = 1) + scale_fill_gradient(low = 'white', high = 'red')
ggsave(p, filename = 'fig1.C3_12inch.pdf', width = 12, height = 12)

 

转换成均值再绘图

grp <- f_metaG2G(metaG = as.data.frame(Idents(sce)), matrixN = F)
p <- f_matrix_heatmap(scale(f_matrix_groupMean(mat, group = grp, autoG2G = F, normal_distribution = T, scale = T)), levels = top10$gene, xlevels = levels(sce))
ggsave(p, filename = 'fig1.Cm_12inch.pdf', width = 12, height = 12)

 


一枚爱好探索的医学生