Deseq2 supervised heatmap
3
0
Entering edit mode
aristotele_m ▴ 40
@aristotele_m-6821
Last seen 6.9 years ago
Italy

I want to create a supervised heatmap for the differential expression data obtained from rnaseq analysis using deseq2.

 

I'm not able to see the up and down genes cluster  expression  even if I plot only differential expresion data results.

dds <- DESeq(dds)
res<-results(dds)
resOrdered <- res[order(res$padj),]
head(resOrdered)

select_genes<-rownames(subset(resOrdered, padj < 0.1))

pheatmap(assay(rld)[ select_genes,],cluster_rows =F)

heatmap.2(assay(rld)[ select_genes,] ,density.info = "none",symm=F,Colv =T,trace="none")

If I want to sho better the upregolted gene and downregulate what can I do?

thanks so much!

 

 

 
deseq2 supervised • 8.9k views
ADD COMMENT
0
Entering edit mode

Are you using a simple design or one with multiple factors?

Do the samples cluster if you choose only the top 10 genes? top 20 genes?

ADD REPLY
0
Entering edit mode

thanks so much,

I have one sample design ( treated vs control) .

ADD REPLY
0
Entering edit mode

Did you try my second suggestion: to see if clustering works with the top genes?

ADD REPLY
0
Entering edit mode
aristotele_m ▴ 40
@aristotele_m-6821
Last seen 6.9 years ago
Italy

Thanks so much for you help!

If I use only 10 or 20 genes work really well.

I have problem on the visualization plot. I mean  if I use pheatmap I can't see wit the colors all red on the dirst group and all green in the second group. on heatmap.2 gave me the right results. Even if I use this code I can' obtain a very clear red and green. Where I did wrong?

 

mat<-assay(rld)[select_gene,]

mat<-mat-rowMeans(mat)

heatmap(mat,cluster_rows =F)

 

 

ADD COMMENT
0
Entering edit mode
@sean-davis-490
Last seen 12 weeks ago
United States

Try adding `scale="row"` to heatmap.2 or pheatmap.

ADD COMMENT
0
Entering edit mode
aristotele_m ▴ 40
@aristotele_m-6821
Last seen 6.9 years ago
Italy

This is the results o differential expression.:

out of 24250 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)     : 123, 0.51%
LFC < 0 (down)   : 163, 0.67%
outliers [1]     : 0, 0%
low counts [2]   : 1213, 5%
(mean count < 3.1)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

 

 

 

 

 

ADD COMMENT

Login before adding your answer.

Traffic: 742 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6