Question: multiple comparisons in a DESeq analysis with single replica in
0
gravatar for Assa Yeroslaviz
4.6 years ago by
Assa Yeroslaviz1.2k
Munich
Assa Yeroslaviz1.2k wrote:

I have a data set of six sinalge samples with no replica. I know, that DESeq can work with these, as I have done it already. I would like to know, what to do in this case, when working with multiple comparisons.

I have ran the analysis in a pair-wise mode. DESeq than gives this output:

estimating size factors # workflow of the DESeq2 function pair-wise modus
estimating dispersions
same number of samples and coefficients to fit, estimating dispersion by treating samples as replicates
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing

But when doing the DESeq() command with all the samples in one go, I sort of trick DESeq to think there are multiple replica in the data set and I get this output:

estimating size factors #multiple comparisons in one count table
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing

is it better in this case to run DESeq in a pair-wise modus?

Don't I sort of cheat the results, when letting DESeq thinks there are multiple replica in the data set?

thanks

Assa

deseq multiple comparisons • 1.9k views
ADD COMMENTlink modified 4.5 years ago by Biostar ♦♦ 20 • written 4.6 years ago by Assa Yeroslaviz1.2k

Can you show the design in both cases so we know what you mean by tricking DESeq() in the second instance? I'm assuming you have some sort of factorial design in the latter case.

ADD REPLYlink written 4.6 years ago by Devon Ryan88k

This is the script I am using:

> featureCountTable <- read.delim2("ReadCountTable.txt", sep="\t", quote="", row.names=1)
> coldata <- read.delim2("coldata.txt", sep="\t", quote="", row.names=1)
> coldata

    sample    treatment
CTRL1    ctrl1    ctrl
CTRL2    ctrl2    ctrl
KO1    KO1    KO1
KO2    KO2    KO1
KO3    KO3    KO2
KO4    KO4    KO3

> cds <- DESeqDataSetFromMatrix (
    countData =     featureCountTable,
    colData   =     coldata,  
    design    = ~    treatment
    )

> fit = DESeq(cds)

> res_1 = results(fit, contrast=c("treatment","ctrl","KO1"))
> res_2 = results(fit, contrast=c("treatment","ctrl","KO2"))
> res_3=...

This is how I do it here, But I don't get the message, that DESeq2 "discover", that i don't have any replica. which made me wonder whether or not this is correct. In the first run, I always extract the columns I needed for the comparison, which was a bit tedious.

ADD REPLYlink written 4.6 years ago by Assa Yeroslaviz1.2k
1

So when you do the "pair-wise" method, am I correct in assuming that you're just loading two samples at once? It's getting the replicate information from the treatment column, so it's correct in the second instance that you do in fact have replicates (at least, you're telling it that you do). You don't need replicates for everything, just one group.

Whether your design in the second instance is correct or not depends on the underlying biology, though since you're treating things as unreplicated in one instance, I would guess that this doesn't actually match the experiment.

ADD REPLYlink written 4.6 years ago by Devon Ryan88k

yes, I do have one replica for each condition (or two for some). The different experiments are independent of each other. This is why I first tried to run them separately.

So if I understand it correctly, I can't fit all the samples in  DESeq in one go and than just extract the differentially expressed genes, if the conditions are not somehow related to each other. If the experiments were done as single, separate cases, so should be the analysis.

thanks Devon

ADD REPLYlink written 4.6 years ago by Assa Yeroslaviz1.2k
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