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Determining the optimal number of independent components for reproducible transcriptomic data analysis.

Ulykbek KairovLaura CantiniAlessandro GrecoAskhat MolkenovUrszula CzerwinskaEmmanuel BarillotAndrei Zinovyev
Published in: BMC genomics (2017)
We suggest a protocol of ICA application to transcriptomics data with a possibility of prioritizing components with respect to their reproducibility that strengthens the biological interpretation. Computing too few components (much less than MSTD) is not optimal for interpretability of the results. The components ranked within MSTD range have more chances to be reproduced in independent studies.
Keyphrases
  • data analysis
  • single cell
  • randomized controlled trial
  • rna seq
  • machine learning
  • big data