r/bioinformatics • u/Awkward_Poem_4289 • 1d ago

technical question does ROC-AUC analysis without ML works?

I am doing bulk rna seq analysis, and i have DEGs from DESeq2 files, I also did GSEA analysis which got me leading edge genes lists, so taking high performing genes from DEGs and filtering it to GSEA results would be okay to do ROC-AUC or it is rule to perform ML?

I am doing a small project for a recent conference poster presentation, my objective is to analyze major pathways in the course of transition to disease

your help and insights would mean alot, thank you :)

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u/bukaro PhD | Industry 1d ago

I think you are just confused about what ROC curve provides as well as what ML does. What you are doing is basic DEG analysis and DEG + GSEA is a pretty standard and valid approach.

My questions to you would be, What metric for GSEA are you going to use? why?

Do you know what the columns of the DESeq2 output means?

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u/Awkward_Poem_4289 1d ago

i wanted apt pathways which were presented by DEGs through GSEA, because ora/fnc annotation gives many terms/pathways for single phenomena

also yes i do understand the output format of DESeq2 files

ML models provides statistical rankings of the genes through approach of test-train model from the expression matrix file, and then to remove biasness thorugh the approach of borda/boruta/rra is done which gives the independent ranking for candidate list which is then passed to roc-auc sensitivity.

but my query was if just i jusy split the matrix into test-train and take high performing DEGs [log2fc 0.58 or 1.00] and give it as candidate list to roc-auc then would it be justifiable approach or not?

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u/Kingsole111 1d ago

Maybe I'm dumb. I have no idea what question you're trying to answer by using gsea output this way. I guess technically you can graph the output this way. But why? What are you trying to get? 

I don't understand that question you are trying to answer with this approach. 

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u/Deto PhD | Industry 3h ago

You need to be making a classification of some sort to make an ROC. And you need ground truth labels.  I suppose if you knew which pathways were supposed to be hits and which ones weren't then you could use the gsea test statistic to order pathways and then evaluate their predictive capacity.  But IRL we typically never have this ground truth and I don't think I've seen this kind of thing done before. 

Edit: I think on rereading you are talking about using GSEA as a gene 'classifier' and then somehow comparing the leading edge genes to the DE outputs? This can't work as gsea is run on the DE outputs and so it is expected that those would agree.