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Replicability in cancer omics data analysis: measures and empirical explorations.

Jiping Wang1, Hongmin Liang2, Qingzhao Zhang2,3

  • 1Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.

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|July 25, 2022
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Replicability in cancer omics data is often low and varies significantly between datasets. This study introduces quantitative measures to assess replicability, highlighting its dependence on signal strength and sample size.

Keywords:
cancer omics data analysisquantitative propertiesreplicability measure

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Area of Science:

  • Biomedical research
  • Genomics
  • Cancer omics

Background:

  • Replicability of findings is crucial in biomedical research, especially for cancer omics data.
  • Existing research lacks quantitative measures and properties of replicability assessments.

Purpose of the Study:

  • To address the knowledge gap in quantitative replicability measures for cancer omics data.
  • To examine the properties and inference methods for three key replicability measures.

Main Methods:

  • Analysis of three The Cancer Genome Atlas (TCGA) datasets.
  • Examination of distributional properties of selected replicability measures.
  • Simulation studies to validate findings and explore influencing factors.

Main Results:

  • Cancer omics data generally exhibit low replicability.
  • Significant variations in replicability were observed across different TCGA datasets.
  • Replicability is shown to be dependent on signal level and sample size.

Conclusions:

  • The study provides a quantitative framework for understanding replicability in cancer omics.
  • Findings underscore the need for careful consideration of replicability in omics research.
  • This work advances the understanding of replicability for identification-focused studies.