Hello Everyone,

I am working on a project with the goal of improving the prediction accuracy of melanoma relapse by combining the RNA signature with the clinical features.

Firstly, I calculated the asssociation/relationship between the clinical features with the outcome (early recurrence < 3yrs vs no relapse with at least 5 yrs follow up) and found that Tcat, mitosis and last_vitalstatus are closely related with the outcome result.

Secondly,I need to do the DGE by using the DESeq2 to extract a signature. During this process, I set the closely related clinical features as controls in the design to exclude their effect on the DGE result.

The R code can run successfully, but most of the generated volcano plot are weird when I consider some control factors. Only when I use the last_vitalstatus as control, the volcano plot looks normal (Fig3). The more control factors, the more stretched of the volcano plot, as presented by the last figure(VolcanoPlot_Mitoses_LastVitalStatus_Tcat_Ascontrol.jpeg). If I don't consider any control, the volcano plot is perfect, as presented by the Fig1 (VolcanoPlot_NoControl.jpeg).

The code is attached. I am very grateful, if someone can tell me if I did something wrong. As the Outcome is the feature that I am interested, so I put it at the end of the design.

dds <- DESeqDataSetFromMatrix(countData=RawCountMatrix, colData=EventControl, design= ~ mitoses+Last_vitalstatus+Tcat+Outcome_Grouping)

keep <- rowSums(counts(dds)) >= 10 dds <- dds[keep,]

dds <- DESeq(dds) res <- results(dds)

resOrdered <- res[order(res$padj),] write.csv(as.data.frame(resOrdered), file="Outcome_Good-Bad.csv")

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I set the closely related clinical features as controls in the design to exclude their effect

This can be problematic if they are highly correlated.

Compute

cor( colData(dds)[,c("factor1","factor2",...)] )

To see if they are too closely related.

Otherwise it would be helpful to show PCA plots colored by your different variables.