Hi everyone,
I'm investigating differentially expressed genes in my wild-type yeast vs a mutant strain. Each strain has been cultured in three different conditions (glucose, sucrose, glycerol). I've got two replicates of Illumina 100bp single end reads per condition and I'm analysing these with DESeq2 (v1.6.3).
Taking the glucose replicates for example. I've performed two analyses. The first using a counts table containing all replicates from all conditions. For the glucose comparison I specified a pairwise contrast like follows:
Comparing this output to the second analysis in which the counts table only contained my glucose replicates, I see ~100 fewer DE genes returned from the first analysis containing all conditions and replicates. The subsets of genes returned are also different. For example, the first analysis suggests a gene (which I know to be upregulated in the mutant on glucose via qRT-PCR) is not among the significant DE genes; yet this gene is the most significant DE gene in my second analysis.
While I don't fully understand the modelling that DESeq2 conducts during the analysis, It seems to me that information is drawn from all replicates supplied via the counts table regardless of whether they are specified as part of a pairwise contrast or not and that this is influencing the determination of DE genes in the first analysis.
What would the community recommend as the best course of action for this analysis? To create separate counts tables for the pairwise comparisons I wish to perform? Or to continue using the contrasts method as I have described?
Thanks in advance,
I'm investigating differentially expressed genes in my wild-type yeast vs a mutant strain. Each strain has been cultured in three different conditions (glucose, sucrose, glycerol). I've got two replicates of Illumina 100bp single end reads per condition and I'm analysing these with DESeq2 (v1.6.3).
Taking the glucose replicates for example. I've performed two analyses. The first using a counts table containing all replicates from all conditions. For the glucose comparison I specified a pairwise contrast like follows:
Code:
myresults <- results(ddsanalysis, contrast=c("condition","mutant_glucose","WT_glucose"))
While I don't fully understand the modelling that DESeq2 conducts during the analysis, It seems to me that information is drawn from all replicates supplied via the counts table regardless of whether they are specified as part of a pairwise contrast or not and that this is influencing the determination of DE genes in the first analysis.
What would the community recommend as the best course of action for this analysis? To create separate counts tables for the pairwise comparisons I wish to perform? Or to continue using the contrasts method as I have described?
Thanks in advance,
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