Hi Laurie,
Sorry about the issue with that GCT file I'd sent.
We were able to get this data to run in DESeq2 with some significant caveats.
DESeq2 isn't really designed to handle single replicate comparisons, let alone many of them. So we ended up running your data as a six different One vs. Remaining comparisons, that is, we did TZGpp vs All the other cells, TfhZGs vs all the other cells, Tfhs
vs all the other cells, etc.
Those results are here:
TfhZGs: https://cloud.genepattern.org/gp/pages/index.jsf?jobid=398537&openNewWindow=false
TZGs: https://cloud.genepattern.org/gp/pages/index.jsf?jobid=398538&openNewWindow=false
Tfhs: https://cloud.genepattern.org/gp/pages/index.jsf?jobid=398539&openNewWindow=false
TZGpp: https://cloud.genepattern.org/gp/pages/index.jsf?jobid=398540&openNewWindow=false
Tfhpp: https://cloud.genepattern.org/gp/pages/index.jsf?jobid=398541&openNewWindow=false
TfhZGpp: https://cloud.genepattern.org/gp/pages/index.jsf?jobid=398542&openNewWindow=false
The jobs did run into some errors (calculating the mean expression for the class with only one sample), but this was after the differential expression results were already calculated, so if you look at the ".DESeq2_results_report.txt" file for each job, that should contain the Log2FC results that you're looking for. These files are tab delimited plain text files. I've also attached the six CLS files that I used to run these jobs if you'd like to rerun them yourself.
Note that this is not the only way you could run this experiment. If you were just interested in the difference between the cell types and just wanted to control for the tissue specific effects, you could annotate the tissue in a confounding variable cls file (also attached as Tissue_Confounding.cls). Then you could construct CLS files with, for example, the TfhZG cells from both spleen and Peyer's Patches. That CLS would look something like this:
6 2 1
# OTHER TfhZG
0 0 1 0 0 1
And you could, in theory, do the opposite and calculate the tissue specific differential expression controlling for cell types using the Tissue CLS as the main CLS and the cell types in a confounding variable CLS file like this:
6 3 1
# TZG Tfh TfhZG
0 1 2 0 1 2
That said, I think the job results above are probably what you were looking for. Feel free to reach out to us with any other questions, of if anything here doesn't make sense
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Hi Laurie,
Unfortunately the DESeq2 module on GenePattern does not support automatically doing "all pairs", the only option would be to create gct files containing just the samples you want for each individual comparison. However, doing this with only one sample per type (resulting in 1 vs 1 comparisons), it's strongly discouraged by the DESeq2 authors. To quote from the DESeq2 Documentation:
"Experiments without replicates do not allow for estimation of the dispersion of counts around the expected value for each group, which is critical for differential expression analysis. ... We provide this approach for data exploration only, but for accurately
identifying differential expression, biological replicates are required."
Because this isn't a standard pipeline, I'm not entirely sure if the module will even be able to process the data when given a file containing just the two samples. Your best bet if you still want to use DESeq2 to try these comparisons would be running it natively in the R environment where you'd have full access to its functionality (even though these 1v1 comparisons are not advised).
The Log2FC results you get out of DESeq2 might still be better than manually calculating a Log2(FC) in, for example, Excel based on the raw counts, but the other statistics definitely wouldn't be valid. That said, if you just want a really rough idea what the log2 Fold Changes might be simply calculating it in Excel would be easier and faster.
There isn't really much in the way of a good option here, sorry we couldn't be of more help
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