I am analyzing bulk RNA data from neurons derived from iPSC cells. The experimental design matrix is as follows:
lineage indicates what iPSC line the neuronal sample was derived from, and
method the method used to derive said neurons. All iPSC lines were derived from the same starting material, so they can be considered to be biological replicates of each other.
The main question I am trying to answer is what genes are significantly up/down regulated for a given
method compared to the other two methods. I am also interested in determining how 'bad' of a batch effect the source iPSC
lineage is (ie. how much of the variation is explained by
lineage vs by
method, we hope to find it is mostly explained by
The way I have been handling this with
DESeq2 is using the design
~ lineage + method. One of my colleagues claimed, however, that this was an inappropriate use of
DESeq2 since I do not have the right replicate structure. He claimed that I needed biological replicates that were identical from a design perspective in order for
DESeq2 to be an appropriate choice (eg multiple samples generated from
1, ect). This would imply design matrix like this (here I am adding a sample column for clairty):
His rationale was that
DESeq2 is not able to leverage the fact that
C1 are "expected" to be the "same"/similar and that the modeling is making an assumption of additive variance between the
method where as batch-correction methods (such as
ComBat) would not suffer from these problems, making them a more appropriate choice.
My questions are:
Is it correct that, given a design like the first matrix,
DESeq2is not an appropriate choice when attempting to control for
lineagewhile comparing between
methods, and that other batch-correction-specific methods should be used?
DESeq2is an appropriate choice for this senario, is the design
~ lineage + methodthe most appropriate? Also, what is the best way to compare the strength of the overall effect of
lineagevs the overall effect of
method? I'm guessing this might involve extracting and comparing the model coefficients or perhaps a likelihood ratio test.