[Feature request] one vs. all others

[acoteataltius]

I'd like to be able to input a contrast design (or otherwise choose design factors), to do a one vs all comparison within in a particular "condition" that has more than two levels. If my column "condition" has levels A, B, C, and D, do a comparison of A vs B, C, D.

Something like these options in R deseq2:
design <- ~0 + condition
contrast = c(1, -1/3, -1/3, -1/3)
contrast=list(c("conditionA"),
c("conditionB","conditionC","conditionD"))

Would it be possible to do something where if you leave the second option blank in contrast, like:
contrast = ['condition', 'A', '']
it compares A with all other samples?

[BorisMuzellec]

Hi @acoteataltius, that would be a convenient feature to have indeed.

It's not available in pydeseq2 yet, but I'm adding it to our feature wishlist. I'll give it a go when I have time, but I'm also happy to help anyone opening a PR. Not sure what would be the best way to implement it from a user perspective (maybe a one_vs_all boolean argument?).

In the meantime it seems that it would be possible to obtain the same results by manually setting the contrast_vector attribute after initializing the DeseqStats object, but I'm not 100% sure about this either.

[GalaMichal]

Hi @BorisMuzellec I'd like to ask it is even possible to compare all vs all? Basically, treating each level of the condition factor as a separate group and not setting any of them as a reference (e.g. healthy).

Something like in R Deseq2:
design <- ~0 + condition

[BorisMuzellec]

@GalaMichal there is unfortunately no direct way to do this as of yet. This relates to #213.

However I think it is possible to obtain the same design matrix using pydeseq2.utils.build_design_matrix with no intercept but an expanded design, and use it in your DeseqDataSet like this:

dds = ds.DeseqDataSet(counts=counts, metadata=metadata, design_factors="condition")

# This is where you replace the design matrix
dds.obsm["design_matrix"] =  build_design_matrix(
            metadata=dds.obs,
            design_factors=dds.design_factors,
            expanded=True,
            intercept=False,
        )

# And then you should be able to carry on as usual

dds.deseq2() # etc.

Let me know if this works!

[GalaMichal]

@BorisMuzellec thank you for quick response. Unfortunately, it doesn't work.
dds.deseq2() is calculated but stat_res = DeseqStats(dds) shows: KeyError: 'Condition_1_vs_Condition_1

The same situation occurs when I try, for example, stat_res = DeseqStats(dds, contrast =("Conditions", "Condition_1", "Condition_2"))

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