Abstract
Concern about the reproducibility and reliability of biomedical research has been rising. An understudied issue is the prevalence of sample mislabeling, one impact of which would be invalid comparisons. We studied this issue in a corpus of human transcriptomics studies by comparing the provided annotations of sex to the expression levels of sex-specific genes. We identified apparent mislabeled samples in 46% of the datasets studied, yielding a 99% confidence lower-bound estimate for all studies of 33%. In a separate analysis of a set of datasets concerning a single cohort of subjects, 2/4 had mislabeled samples, indicating laboratory mix-ups rather than data recording errors. While the number of mixed-up samples per study was generally small, because our method can only identify a subset of potential mix-ups, our estimate is conservative for the breadth of the problem. Our findings emphasize the need for more stringent sample tracking, and that re-users of published data must be alert to the possibility of annotation and labelling errors.
Highlights
Recent years have seen an increase in concern about the quality of scientific research, along with efforts to improve reliability and reproducibility[1,2]
We identified a corpus of 70 human gene expression studies that had sample sex annotation (4160 samples in total) run on two platforms
We developed a simple robust method for classifying samples by sex based on three sex specific genes – XIST, RPS4Y1 and KDM5D
Summary
Recent years have seen an increase in concern about the quality of scientific research, along with efforts to improve reliability and reproducibility[1,2]. These issues are highly relevant to genomics studies, which deal with complex and often weak signals measured genome-wide. In transcriptomics studies (our focus here), mRNA is extracted from samples and processed using microarrays or RNA-seq, followed by statistical analysis to identify patterns of interest (e.g. differential expression). If mix-ups are present but undetected, the conclusions of the analysis might be affected and pollute the literature, as well as create a lurking problem for those who re-use the data
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
