Hi,
I am currently using DESeq2 for my RNAseq data with a multi factorial design. I have 4 samples L1-L4 (7 biol. replicates) with each sample combining 2 factors.
My DESeq glm look like this:
dds <- DESeqDataSetFromMatrix(countData = countTable, colData = Design, design = ~ A + B)
Now, the problem is that among my DE genes for let's say condition A, there are genes with very high expression in L1 and low expression in L2,L3,L4. Now, if expression in L1 is high enough, this gene will be called as highly expressed in the A_high treatment group, even though sample L2 only shows weak expression.
Here's an example from my data set. Expression of Gene A is L1>>L3>L2>L4. Yet, in the multi factor design, it gets called as significantly over expressed in cond A_high, i.e. L1 and L2. FC=log2FoldChange
Now I am looking for a way to correct for this. I could filter the list of DE genes of A_high by selecting only those genes where log2FoldChange is bigger in L1 and L2 compared to L3 and L4, but this seems not very well thought through.
Maybe one of you guys might have an idea how to deal with this.
I am currently using DESeq2 for my RNAseq data with a multi factorial design. I have 4 samples L1-L4 (7 biol. replicates) with each sample combining 2 factors.
HTML Code:
B_high B_low A_high L1 L2 A_low L3 L4
dds <- DESeqDataSetFromMatrix(countData = countTable, colData = Design, design = ~ A + B)
Now, the problem is that among my DE genes for let's say condition A, there are genes with very high expression in L1 and low expression in L2,L3,L4. Now, if expression in L1 is high enough, this gene will be called as highly expressed in the A_high treatment group, even though sample L2 only shows weak expression.
Here's an example from my data set. Expression of Gene A is L1>>L3>L2>L4. Yet, in the multi factor design, it gets called as significantly over expressed in cond A_high, i.e. L1 and L2. FC=log2FoldChange
HTML Code:
ID A_FC A_padj B_FC B_padj L1_FC L1_padj L2_FC L2_padj L3_FC L3_padj L4_FC L4_padj GeneA 0.497 0.048 0.149 0.758 0.84 4.09E-05 -0.26 0.59 -0.73 0.01 -0.05 0.92
Now I am looking for a way to correct for this. I could filter the list of DE genes of A_high by selecting only those genes where log2FoldChange is bigger in L1 and L2 compared to L3 and L4, but this seems not very well thought through.
Maybe one of you guys might have an idea how to deal with this.
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