• Konrad Zych
  • Basten L Snoek
  • Mark Elvin
  • Miriam Rodriguez
  • K Joeri Van der Velde
  • Danny Arends
  • Harm-Jan Westra
  • Morris A Swertz
  • Gino Poulin
  • Jan E Kammenga
  • Rainer Breitling
  • Ritsert C Jansen
  • Yang Li

In high-throughput molecular profiling studies, genotype labels can be wrongly assigned at various experimental steps; the resulting mislabeled samples seriously reduce the power to detect the genetic basis of phenotypic variation. We have developed an approach to detect potential mislabeling, recover the "ideal" genotype and identify "best-matched" labels for mislabeled samples. On average, we identified 4% of samples as mislabeled in eight published datasets, highlighting the necessity of applying a "data cleaning" step before standard data analysis.

Original languageEnglish
Pages (from-to)e0171324
JournalPLoS One
Issue number2
Publication statusPublished - 2017
Externally publishedYes

    Research areas

  • Journal Article

ID: 4307208