Count_data <- read.csv("Count_data.csv", check.names=FALSE) 


Counts <- Count_data
rownames(Counts) <- Count_data$names
counts_IDs <- Count_data
Counts_only <- Count_data %>% select(-names)  # create the table with only counts here

group <- c(rep("SH",4), rep("U2",4), rep("U7",4),rep("RU",4))

y <- DGEList(counts=Counts_only, genes = counts_IDs$names, group=group)

design_edgeR <- model.matrix(~0+group, data=y$samples,genes= Counts$names)
colnames(design_edgeR) <- levels(y$samples$group)


#HEATMAPS:

logcounts <- cpm(y,log=TRUE)

var_genes <- apply(logcounts, 1, var)
head(var_genes)


# Get the gene names for the top 500 most variable genes
select_var <- names(sort(var_genes, decreasing=TRUE))[1:30]
head(select_var)

highly_variable_lcpm <- logcounts[select_var, ]
dim(highly_variable_lcpm)


## Get some nicer colours
library(gplots)
mypalette <- brewer.pal(11,"RdYlBu")
morecols <- colorRampPalette(mypalette)
# Set up colour vector for celltype variable
col.cell <- c("purple","orange")[group]

# Plot the heatmap
heatmap.2(highly_variable_lcpm,col=rev(morecols(50)),trace="none", main="Top variable genes across conditions-Macrophages",ColSideColors=col.cell,scale="row",margins=c(9,9))

The resulting heatmap shows some random numbers as gene names instead of my gene names: