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However, by estimating the contamination with est = rowSums(data)/sum(data), you are estimating the contamination without data of the empty droplets at all. So it does not yield then the effectivity as in the own publication of SoupX.
Basically saying you could run everything just like this: sc = SoupChannel(data, data, calcSoupProfile = FALSE) soupProf = data.frame(row.names = rownames(data), est = rowSums(data)/sum(data), counts = rowSums(data))
and it would give you similar results.
So I wonder why you chose to perform the estimation manually and not the automatic estimation, which does take the empty droplets into account.
Thanks a lot! I appreciate your resource!
Best,
Emilio
The text was updated successfully, but these errors were encountered:
Hi,
Maybe it's not a typical issue but just a question on why you have chosen to apply the SoupX pipeline in this way.
You have written:
sc = SoupChannel(data_tod, data, calcSoupProfile = FALSE)
soupProf = data.frame(row.names = rownames(data), est = rowSums(data)/sum(data), counts = rowSums(data))
However, by estimating the contamination with est = rowSums(data)/sum(data), you are estimating the contamination without data of the empty droplets at all. So it does not yield then the effectivity as in the own publication of SoupX.
Basically saying you could run everything just like this:
sc = SoupChannel(data, data, calcSoupProfile = FALSE)
soupProf = data.frame(row.names = rownames(data), est = rowSums(data)/sum(data), counts = rowSums(data))
and it would give you similar results.
So I wonder why you chose to perform the estimation manually and not the automatic estimation, which does take the empty droplets into account.
Thanks a lot! I appreciate your resource!
Best,
Emilio
The text was updated successfully, but these errors were encountered: