Whose sample is it anyway? Widespread misannotation of samples in transcriptomics studies

@inproceedings{Toker2016WhoseSI,
  title={Whose sample is it anyway? Widespread misannotation of samples in transcriptomics studies},
  author={Lilah Toker and Min Feng and Paul Pavlidis and Hans van Bokhoven and Leonard P Freedman},
  booktitle={F1000Research},
  year={2016}
}
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… CONTINUE READING
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