Hi everyone,

I've been trying to use DESeq2 to look at expression differences across 4 SRR: 2967085, 2967086, 2973633 and 23351735.

I've used HTseq and got 4 files. An example of the files:

uc001aaa.3  0
uc001aac.4  0
uc001aae.4  0
uc001aah.4  0
uc001aai.1  0
uc001aak.3  0
uc001aal.1  0
uc001aaq.2  0
uc001aar.2  0
uc001aau.3  2

Then i tried:

sampleFiles <- grep("*S",list.files(directory),value=TRUE)
sampleCondition <- sub("(.*S).*","\\1",sampleFiles)
sampleTable <- data.frame(sampleName = sampleFiles,
                          fileName = sampleFiles,
                          condition = sampleCondition)
sampleTable$condition <- factor(sampleTable$condition)
sampleCondition
[1] "AS" "BS" "CS" "DS"

library("DESeq2")
ddsHTSeq <- DESeqDataSetFromHTSeqCount(sampleTable = sampleTable,
                                       directory = directory,
                                       design= ~ condition)
ddsHTSeq
dim: 82960 4 
metadata(1): version
assays(1): counts
rownames(82960): uc001aaa.3 uc001aac.4 ... uc031tky.1 uc031tkz.1
rowData names(0):
colnames(4): AS22967085.txt BS22967086.txt CS22973633.txt
  DS23351735.txt
colData names(1): condition

And then when i try:

dds <- DESeq(ddsHTSeq)
estimating size factors
estimating dispersions
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'm new in learning DESeq2. Can anyone help?

The error is telling you that you only have one sample for each sample type. In that situation you can't do any statistical analysis, because you need replicates to estimate the biological variability. It's possible that you have two groups with two samples in each, in which case you need to change your 'condition' to reflect that.

Hi Neha, and of course anyone who may encounter this kind of issue in the future. Sometimes you actually have count data that is duplicate or more and you are wondering "but my data is duplicate, why am i still getting this error", then in this case i advice you check you colData table, choose a column of design parameter that reflects your true experimental conditions and use it for your design in the deseq2 functional analysis. It will solve the error.