Dear All experts,

Hope you are all well. I am doing hierachical clustering to see whether sample in each group is clustered together. I used DESeq2 model and my code chunk below to do the clustering. I am wondering if I wanted to filter say top 100 differentially expressed genes, where should I add to my existing code chunk. Looking forward to hearing from you soon. This is just example dataset.

Kind Regards,

Synat

## Heatmap 

sampleDists <- dist(t(assay(vsd)))
library("RColorBrewer")
sampleDistMatrix <- as.matrix(sampleDists)
rownames(sampleDistMatrix) <- paste(vsd$Mouse, sep="-")
colnames(sampleDistMatrix) <- NULL
colors <- colorRampPalette( rev(brewer.pal(9, "Blues")))(255)

## annotation ============================ 

annotation.row <- data.frame(Treatment = factor(vsd@colData$Treatment,
                                                levels = c("Radiotherapy", "Sham-radiotherapy")), 
                             Group = factor(vsd@colData$Group,
                                            levels = c("0Gy:0.1 day","0Gy:1 day","0Gy:4 days",
                                                       "0Gy:8 days", "2Gy×5:0.1 day", "2Gy×5:1 day", 
                                                       "2Gy×5:4 days", "2Gy×5:8 days")))


## mapping =======================
rownames(annotation.row)<- rownames(sampleDistMatrix)

# plot the pheatmap ====================
library(pheatmap)
pheatmap(sampleDistMatrix,
         clustering_distance_rows=sampleDists,
         clustering_distance_cols=sampleDists,
         annotation_row = annotation.row, 
         border_color = TRUE, cluster_cols = F)