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Highly efficient hypothesis testing methods for regression-type tests with correlated observations and heterogeneous variance structure.

Yun ZhangGautam BandyopadhyayDavid J TophamAnn R FalseyXing Qui
Published in: BMC bioinformatics (2019)
As fast and numerically stable replacements for the weighted LMER test, the PB-transformed tests are especially suitable for "messy" high-throughput data that include both independent and matched/repeated samples. By using our method, the practitioners no longer have to choose between using partial data (applying paired tests to only the matched samples) or ignoring the correlation in the data (applying two sample tests to data with some correlated samples). Our method is implemented as an R package 'PBtest' and is available at https://github.com/yunzhang813/PBtest-R-Package .
Keyphrases
  • electronic health record
  • highly efficient
  • high throughput
  • big data
  • magnetic resonance
  • machine learning
  • single cell
  • risk assessment
  • deep learning