Hi there, I am using DESeq2 to analyse RNAseq from siRNA treated samples and 2 controls (Scramble and Untreated). Each treatment has 4 cell lines:
treatment cellline group <fct> <fct> <fct> 1 Untreated 1 Control 2 Scramble 1 Control 3 Knockdown 1 Knockdown 4 Untreated 2 Control 5 Scramble 2 Control 6 Knockdown 2 Knockdown 7 Untreated 3 Control 8 Scramble 3 Control 9 Knockdown 3 Knockdown 10 Untreated 4 Control 11 Scramble 4 Control 12 Knockdown 4 Knockdown
I would like to contrast Knockdown versus all Control samples but remove effects due to (i) cell line and (ii) scramble. I would like to use the most optimal design and contrast to achieve this.
The straight forward options are to either lump together Untreated and Scramble into a single Control group:
design = ~ cellline + group results(dds, contrast=c("group", "Knockdown", "Control"))
or ignore Untreated samples all together:
design = ~ cellline + treatment results(dds, contrast=c("group", "Knockdown", "Scramble"))
Neither are optimal and I wondered whether there is a better option that incorporates Scramble treatment into the design but avoid the error:
model matrix is not full rank, so the model cannot be fit as specified. For example, could i specify a likelihood ratio rest with the full model =
~ cellline + treatment + group and reduced =
~ cellline + treatment?
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