Question: DESeq2 with multiple treatments but no replicates
1
gravatar for jpc41
13 days ago by
jpc4110
jpc4110 wrote:

Hi everyone,

I've been trying to use DESeq2 to look at expression differences across different transcripts, with 8 (unreplicated) samples across 8 treatments.

My count data looks as follows:

> head(countdata)
                   cts46 cts47 cts48 cts49 cts50 cts51 cts52 cts53
ENST00000000233.9      4     0     1     1     4     3     2     4
ENST00000000412.7      0     0     0     0     0     1     0     0
ENST00000000442.10     0     0     0     0     0     0     0     0
ENST00000001008.5      1     1     2     2    67    72    31    43
ENST00000001146.6      0     0     0     0     0     0     0     0

And my column data as follows:

> coldata
        Treatment
cts46        Untr
cts47         Ifn
cts48         Vir
cts49     Ifn_Vir
cts50    Ltm_Untr
cts51     Ltm_Ifn
cts52     Ltm_Vir
cts53 Ltm_Ifn_Vir

I tried:

ddsMat <- DESeqDataSetFromMatrix(countData=countdata, colData=coldata, design=~Treatment)
ddsMat <- DESeq(ddsMat)

But I got the following error:

Error in checkForExperimentalReplicates(object, modelMatrix) : 

  The design matrix has the same number of samples and coefficients to fit,
  so estimation of dispersion is not possible. Treating samples
  as replicates was deprecated in v1.20 and no longer supported since v1.22.

I can't figure out what the issue is, and would really appreciate any help! I'd just like to investigate differential expression across my treatment conditions. Thanks so much.

deseq2 rna-seq • 78 views
ADD COMMENTlink modified 13 days ago by Michael Love24k • written 13 days ago by jpc4110

I also tried setting minReplicatesForReplace=Inf as suggested in one part of the manual, but the error message remains the same.

ADD REPLYlink written 13 days ago by jpc4110
Answer: DESeq2 with multiple treatments but no replicates
2
gravatar for Michael Love
13 days ago by
Michael Love24k
United States
Michael Love24k wrote:

Simply put, DESeq2 doesn't support inference without replicates. The previous version (DESeq) had an option to give results in such a case by estimating dispersion across samples as if they were replicates, and then this was carried over into DESeq2, but later removed as a feature. I'm not convinced there is value in the results from such an analysis, and I think it can give misleading results.

ADD COMMENTlink written 13 days ago by Michael Love24k

Ok, I suspected this was the case. Thanks for your help.

ADD REPLYlink written 12 days ago by jpc4110
Please log in to add an answer.

Help
Access

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 16.09
Traffic: 297 users visited in the last hour