Hello everyone,

I am working on a bulk RNA-seq differential expression analysis comparing tumor versus healthy tissue. The complication is that the samples come from two different sources, and the sequencing layout is fully confounded with condition:

• Tumor samples: paired-end sequencing (Source A)
• Healthy samples: single-end sequencing (Source B)

As a result, the effects of condition, source, and layout cannot be statistically separated. This makes standard batch correction approaches invalid, because there is no within-condition variation in these technical factors.

Based on previous discussions, it seems there are two potential strategies:

• Technical homogenization: Reprocess all samples uniformly as single-end (using only R1 for the paired-end tumor samples), then re-quantify.
• Introduce within-condition variation: Add samples where both conditions include both layouts (not possible in my project).

Since option 2 cannot be done, my question is:

Is option 1 considered an appropriate and valid strategy to to make a tumor vs healthy DE comparison defensible in this scenario?

Additionally, I would appreciate guidance on:

• Important QC checks after homogenization
• Recommendations for modeling dataset source once layout is no longer confounded
• Any references or previous workflows where this approach has been used successfully
• Potential limitations I should report when interpreting the results

Thank you very much in advance for your advice and feedback!

Best!

If by reprocess you mean generate libraries and re-sequence, then that's something you could do. Otherwise the biological and technical differences will be confounded, and there is nothing you can do to 'fix' that, and even if you could, how would you know you had done so?