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Adaptive Filtering Increases Power to Detect Differentially Expressed Genes

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New Advances in Statistics and Data Science

Part of the book series: ICSA Book Series in Statistics ((ICSABSS))

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Abstract

Detecting differentially expressed genes is difficult due to the large number of genes simultaneously tested, resulting in low power for each test after adjusting for multiplicity. We propose a novel adaptive filtering procedure that improves power by filtering out genes that are unlikely to be differentially expressed. We show that the proposed procedure controls the false discovery rate asymptotically. Simulation study further demonstrate its advantage over the state-of-the-art competitors.

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Acknowledgements

Kun Liang is supported by Canada NSERC grant 435666-2013.

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Authors and Affiliations

  1. University of Waterloo, 200 University Ave W, Waterloo, ON, Canada

    Zixin Nie & Kun Liang

Authors
  1. Zixin Nie
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  2. Kun Liang
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Corresponding author

Correspondence to Kun Liang .

Editor information

Editors and Affiliations

  1. University of North Carolina, Chapel Hill, North Carolina, USA

    Ding-Geng Chen

  2. Columbia University, New York, New York, USA

    Zhezhen Jin

  3. University of California, Los Angeles, California, USA

    Gang Li

  4. University of Michigan-Ann Arbor, Ann Arbor, Michigan, USA

    Yi Li

  5. National Institutes of Health, Bethesda, Maryland, USA

    Aiyi Liu

  6. Georgia State University, Atlanta, Georgia, USA

    Yichuan Zhao

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Nie, Z., Liang, K. (2017). Adaptive Filtering Increases Power to Detect Differentially Expressed Genes. In: Chen, DG., Jin, Z., Li, G., Li, Y., Liu, A., Zhao, Y. (eds) New Advances in Statistics and Data Science. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-69416-0_8

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