Can I run DESeq2 with SMART-Seq data
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Assa Yeroslaviz ★ 1.5k
@assa-yeroslaviz-1597
Last seen 10 weeks ago
Germany

my data set comes from single-cell SMART-Seq data, where I have in total 48 samples, 24 samples for the control and 24 for my KO. Each sample represent one cell.

I have used STAR to map the samples against my indexed genome and featureCounts to quantify the reads onto the genes.

If I do a pre-filtering before running the DESeq function only very little genes are left, as I'm not sure what a meaningful filter threshold would be.

keep <- rowSums(counts(dds) <= 100 ) == 12
table(keep)

keep
FALSE  TRUE 
56869   141 

I was wondering if this is a feasible way of analysing the data, as I have many 0 reads in the count matrix (which would have been expected from the single-cell data set.

(or would the kallisto -> tximport -> DESeq2 or Kallisto -> RSEM way would be better in this case?)

thanks for the advice

Assa

DESeq2 SMART-seq • 797 views
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The choice of preprocessing is on you. There are a variety of benchmark papers and preprints you can get guidance from.

Towards prefiltering, I personally think it's more meaningful gor single-cell data to filter for the percent of cells per condition/cluster/group expressing a gene rather than count cutoffs. For example, at least 10% of cells of st least one group should express (count>0) a gene so it's not a spurious detection. DESeq2 vignette has a single-cell section with recommendations for analysis.

In any case, if you have biological replicates you might consider pseudobulk analysis.

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I haven't seen this part before. Thanks for pointing it out.

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@mikelove
Last seen 1 hour ago
United States

There should be no problem to run DESeq2.

You can leave the filtering to results() or, better, use IHW.

Your filter looks wrong, it has a less than, just leave out filtering for now.

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thanks, the filter is the other way around, true 🙈

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