Exploring transcriptomic and genomic latent variable correction approaches in differential expression analysis
This research study evaluates the effectiveness of combining surrogate variables and principal components to correct for latent variables in transcriptomic and genomic datasets. The authors demonstrate that this combined framework significantly improves replicability in ALS research.
Why it matters
Improving data correction methods is critical for the accuracy and reproducibility of biological research, particularly in complex disease studies like ALS.
Scientific Reports ( 2026 ) Cite this article
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