i want to ask something about PCA graph i get from DESEQ2 analysis .....is it necessary that the two groups im comparing show difference or can they be inteergreted together like the dots of for example healthy group should in in a place and dots of diseased group should be in another place ....or can they show the same pattern?
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written
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Alaa •
1 answer
No, it is not necessary for the two groups to show clear separation in a PCA plot from DESeq2. PCA visualizes the major sources of variance in your data, and if healthy and diseased samples overlap, it simply indicates that the differences between groups are not the primary drivers of variance.
This could mean the perturbation (disease) has minimal impact on gene expression, or other factors like batch effects, technical variability, or individual differences dominate the signal. Always check for batch effects first, especially if integrating multiple datasets—tools like ComBat or removeBatchEffect from limma can help correct them before re-running PCA.
If separation is absent, differential expression analysis may still reveal subtle changes, but expect fewer significant genes. For better PCA exploration, consider my PCAtools Bioconductor package, which offers enhanced diagnostics and biplots to interpret loadings and identify key genes driving any patterns.
In summary, overlapping groups are interpretable and common; they reflect the biology or data quality, not a flaw in the analysis.
Kevin
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Given that the samples were not interchanged or cross-contaminated, the overlap between case and control samples in the PCA suggests that the treatment or perturbation has little or no influence on gene expression.
But this means I still can get statistically important results or not ?even with bad PCA Because the core is to combine multiple studies different platforms some give good PCA some not
If you are combining multiple studies using different platforms, I would suggest checking for and correcting batch effects before DE analysis.