Overlooked poor-quality patient samples in sequencing data impair reproducibility of published clinically relevant datasets.

ABSTRACT:

Background

Reproducibility is a major concern in biomedical studies, and existing publication guidelines do not solve the problem. Batch effects and quality imbalances between groups of biological samples are major factors hampering reproducibility. Yet, the latter is rarely considered in the scientific literature.

Results

Our analysis uses 40 clinically relevant RNA-seq datasets to quantify the impact of quality imbalance between groups of samples on the reproducibility of gene expression studies. High-quality imbalance is frequent (14 datasets; 35%), and hundreds of quality markers are present in more than 50% of the datasets. Enrichment analysis suggests common stress-driven effects among the low-quality samples and highlights a complementary role of transcription factors

SUBMITTER: Sprang M 

PROVIDER: S-EPMC11328481 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature