Units for pathway expression #14
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Hello Jack! Thank you very much for such a great tool but one moment for me is unclear. |
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Replies: 3 comments 4 replies
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Hey. No worries at all. In the SCPA output, the Qval that we give is a transformed adjusted pval, and is the metric that we recommend using. You can consider that the higher the Qval, the more differentially regulated the pathway is between conditions. If you're doing a two-sample comparison, we also give a fold change value that's calculated from average pathway expression in population1 - population2 (it's a running sum of mean changes in gene expression for that pathway), so a negative value means the pathway will be higher in population2. Hope that's what you were looking for? I'll also add some more detailed explanation into the help documentation -- Jack |
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OK -- I've added some detail about the FC calculation if you run |
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Thank You very much! That was exactly what I was looking for)) |
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Hi Jack, |
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Hey, Are you meaning this tutorial? If you're following the same code in the tutorial then there should be the same results, hopefully. Did you just follow all the code on the page? Can you show me the results you got? Thanks |
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FYI -- I just re-ran the analysis using the code on the SCPA tutorial page and I get the same result. You may get slightly different results in the row/column clustering because ComplexHeatmap uses kmeans for this, but the actual qvals should be very similar |
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Hi Jack
Yes, I was using the tutorial with my data. You were right about the
complex heatmap doing something. My column clustering used to get messed up
(column names would show a cell cluster with meager differential pathway
enrichment as the best hit. It might just be me. However, when I took out
most custom parameters from Heatmap, it just ordered perfectly.
Thank you again, and sorry for the delay in answering.
Sudu
…On Sun, Feb 18, 2024 at 4:42 PM Jack ***@***.***> wrote:
FYI -- I just re-ran the analysis using the code on the SCPA tutorial page
and I get the same result. You may get slightly different results in the
row/column clustering because ComplexHeatmap uses kmeans for this, but the
actual qvals should be very similar
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Hey.
No worries at all. In the SCPA output, the Qval that we give is a transformed adjusted pval, and is the metric that we recommend using. You can consider that the higher the Qval, the more differentially regulated the pathway is between conditions. If you're doing a two-sample comparison, we also give a fold change value that's calculated from average pathway expression in population1 - population2 (it's a running sum of mean changes in gene expression for that pathway), so a negative value means the pathway will be higher in population2. Hope that's what you were looking for?
I'll also add some more detailed explanation into the help documentation --
?compare_pathways()-- to give a …