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Density distribution of gene expression profiles and evaluation of using maximal information coefficient to identify differentially expressed genes

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  • Han-Ming Liu
  • Dan Yang
  • Zhao-Fa Liu
  • Sheng-Zhou Hu
  • Shen-Hai Yan
  • Xian-Wen He

Abstract

The hypothesis of data probability density distributions has many effects on the design of a new statistical method. Based on the analysis of a group of real gene expression profiles, this study reveal that the primary density distributions of the real profiles are normal/log-normal and t distributions, accounting for 80% and 19% respectively. According to these distributions, we generated a series of simulation data to make a more comprehensive assessment for a novel statistical method, maximal information coefficient (MIC). The results show that MIC is not only in the top tier in the overall performance of identifying differentially expressed genes, but also exhibits a better adaptability and an excellent noise immunity in comparison with the existing methods.

Suggested Citation

  • Han-Ming Liu & Dan Yang & Zhao-Fa Liu & Sheng-Zhou Hu & Shen-Hai Yan & Xian-Wen He, 2019. "Density distribution of gene expression profiles and evaluation of using maximal information coefficient to identify differentially expressed genes," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-28, July.
    • Handle: RePEc:plo:pone00:0219551
    DOI: 10.1371/journal.pone.0219551
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    Cited by:

    1. Vidal Fey & Dhanaprakash Jambulingam & Henri Sara & Samuel Heron & Csilla Sipeky & Johanna Schleutker, 2021. "BioCPR–A Tool for Correlation Plots," Data, MDPI, vol. 6(9), pages 1-11, September.
    2. Md Tauhidul Islam & Jen-Yeu Wang & Hongyi Ren & Xiaomeng Li & Masoud Badiei Khuzani & Shengtian Sang & Lequan Yu & Liyue Shen & Wei Zhao & Lei Xing, 2022. "Leveraging data-driven self-consistency for high-fidelity gene expression recovery," Nature Communications, Nature, vol. 13(1), pages 1-17, December.

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