Question: Strange MA-plot using DESeq2
0
gravatar for Cecelia
19 months ago by
Cecelia20
Cecelia20 wrote:

I am running DESeq2 to perform a differential expression analysis two treatment conditions (Treat vs. Control) of a wasp infection in a beetle species. However I got some strange looking MA-plot when comparing some conditions.

There is an obvious set of outlier red dots in the down-regulated region that looks very strange. Can anybody explain what causes such results?

The count data was imported from Salmon output. The R command I used are below:

Importing data

filesevipus12h<- c("quant1.sf",    
 "quant2.sf",
 "quant3.sf",
 "quant4.sf",
 "quant5.sf",
 "quant6.sf")

names(filesevipus12h) <- c("Treat_12h_rep1",  "Treat_12h_rep2",  "Treat_12h_rep3",  "Control_12h_rep1",  
"Control_12h_rep2",  "Control_12h_rep3")
txifilesevipus12h <- tximport(filesevipus12h, type="salmon", tx2gene=tx2genepus)

DE analysis

ddsTCpus12 <- DESeqDataSetFromTximport(txifilesevipus12h, colData=colDatapus12,
                                   design=~ Treatment)
ddsTCpus12 <- DESeq(ddsTCpus12)

MA plot

pus12res <- results(ddsTCpus12, alpha=0.05)
plotMA(pus12res, main=paste0('G. pusilla'), ylim=c(-8,8))

Any help will be greatly appreciated. Thanks

enter image description here

I am having problem inserting images in biostar, the direct link to the DEseq2 image is below:

https://ibb.co/mgnzQ6

rna-seq deseq2 R • 1.8k views
ADD COMMENTlink modified 6 months ago by Renesh1.6k • written 19 months ago by Cecelia20
2

This looks mostly like the genes really do just change mostly in one direction. The normalization is a bit off center, but it's not terrible.

ADD REPLYlink written 19 months ago by Devon Ryan90k

Please post image to understand the issue better.

ADD REPLYlink written 19 months ago by cpad011211k

I've corrected the link for the image.

ADD REPLYlink written 19 months ago by Devon Ryan90k
3
gravatar for sHeyne
19 months ago by
sHeyne30
Freiburg, Germany
sHeyne30 wrote:

The red cloud at the bottom looks like a contamination from some other cell/tissue type (in case of eg. mouse or human). In your case, all samples/replicates are from "whole body" beetles? Don't know how this then could happen...

In order to see if this effect relates to one or more replicates, you can check all pairwise correlation plots of your replicates. You should see a clear, reoccurring stripe next to the diagonal for certain samples. If you leave then out this sample(s), your MA-plot should look more normal.

ADD COMMENTlink written 19 months ago by sHeyne30

Thanks for your suggestions! But could you explain more about the pairwise correlation plots of the replicates? What R function should I apply then?

ADD REPLYlink written 19 months ago by Cecelia20

Just out of interest, did you find out if the statement of sHeyne suggesting contamination was true? I do not really understand how one would make the connection from a cloud of apparently downregulated genes to contamination by other cells.

ADD REPLYlink written 6 months ago by ATpoint17k
0
gravatar for Renesh
6 months ago by
Renesh1.6k
United States
Renesh1.6k wrote:

Easy to understand and visualize MA plot for gene expression data: https://reneshbedre.github.io/blog/ma.html

ADD COMMENTlink written 6 months ago by Renesh1.6k
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