In this blog post, I am going to show you how to make a correlation heatmap with p-values and significant values labeled in the heatmap body. Let’s use the PBMC single cell data as an example.
You may want to read my previous blog post How to do gene correlation for single-cell RNAseq data.
Load libraries library(dplyr) library(Seurat) library(patchwork) library(ggplot2) library(ComplexHeatmap) library(SeuratData) library(hdWGCNA) library(WGCNA) set.seed(1234) prepare the data data("pbmc3k") pbmc3k #> An object of class Seurat #> 13714 features across 2700 samples within 1 assay #> Active assay: RNA (13714 features, 0 variable features) ## routine processing pbmc3k<- pbmc3k %>% NormalizeData(normalization.
You probably do not understand heatmap! Please read You probably don’t understand heatmaps by Mick Watson
In the blog post, Mick used heatmap function in the stats package, I will try to walk you through comparing heatmap, and heatmap.2 from gplots package.
Before I start, I want to quote this:
“The defaults of almost every heat map function in R does the hierarchical clustering first, then scales the rows then displays the image”