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I have RNA-seq for two groups of patients: Responders to chemotherapy (n=9) versus non-responders to chemotherapy (n=24)

I have done DESeq2 and obtain fold change of responders versus non-responders in which positive fold change means down-regulation in responders (if I am not wrong)

> head(df)
    gene       FC
1 286499 7.116071
2 260436 6.691189
3   2267 5.751322
4   5968 5.522079
5   4103 5.328008
6 442709 5.036087
> 

I have done GSEA in pre-ranked way

got this plot for KRAS pathway

enter image description here

I interpret this as; KRAS pathway in being down regulated in responders

Am I right?

Please correct me

When I separated genes for up and down regulated

For up regulated genes I did not get any significant GSEA but for down regulated genes in responders I got this

enter image description here

SO does this mean that KRAS pathway is being up regulated in responders because I have used down regulated genes in responders?

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  • $\begingroup$ log2(responders/non-responses) > 0 implies responders/non-responders > 1, which means responders > non-responders, which in turn means up-regulation in responders. How did you get to down-regulation in responders? Am I missing something? $\endgroup$ – Ram RS Aug 11 '20 at 15:37
  • $\begingroup$ Because few genes at the start of plot going to be up regulated but at the end more gene set started to be down regulated :( $\endgroup$ – Exhausted Aug 11 '20 at 15:44
  • $\begingroup$ I'm asking a basic math question. You're saying that a group of genes that have more expression in one subset compared to the other subset are down-regulated. Common sense dictates more expression equals up-regulated. Can you please explain that? $\endgroup$ – Ram RS Aug 11 '20 at 16:50
  • $\begingroup$ If a gene is expressed 4 units in responders and 2 in non-responders, log2FC = log2(4/2) = log2(2) = 1, which is >0. Thus, a gene up-regulated in responders (has more expression) will have log2FC > 0. How are you equating log2FC>0 with down-regulation in responders while measuring fold change with responder expression in the numerator? $\endgroup$ – Ram RS Aug 11 '20 at 16:52
  • $\begingroup$ @Ram RS that is why I got confused I know the direction of expression but I can not understand the GSEA part $\endgroup$ – Exhausted Aug 11 '20 at 18:31
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In Deseq2, it depends on contrast eg. if your contrast <- c("Condition", firstC, SecondC) is then -ve is downregulated in FirstC and same for +ve. It is always better to take look on count data for the confirmation (responders versus non-responders samples).

for more detail you can refer here

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    $\begingroup$ Thank you but actual question is about GSEA $\endgroup$ – Exhausted Aug 11 '20 at 7:29
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    $\begingroup$ The presence of most of the genes at the end of the plot (~4k ranks) is more than expected by chance. So yes, in general, we can say the pathway is downregulated because it has the highest score. ( but as I explained before you need to verify, in which condition those genes are down-regulated), then you can conclude in which state the pathway is down-regulated. You may also like to plot GSEA separately for both the list of statistically significant down-regulated and up-regulated genes. $\endgroup$ – Amarinder Singh Thind Aug 11 '20 at 15:44
  • $\begingroup$ @ Amarinder Singh Thind please look at the edition of my main post $\endgroup$ – Exhausted Aug 11 '20 at 16:10
  • $\begingroup$ your statement is a bit confusing as you wrote, "SO does this mean that KRAS pathway is being up-regulated in responders because I have used down-regulated genes in responders?". @Exhausted If I were in your place, I might take a look at all the genes in your pathway. Then try to investigate how many of them are part of the significantly DE up-regulated list and down-regulated list and try to justify my fining based on the ratio of down/up genes of the pathway. Higher the ratio, more are the chances that pathway in overall down-regulated. $\endgroup$ – Amarinder Singh Thind Aug 12 '20 at 1:27

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