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A double-robust test for high-dimensional gene coexpression networks conditioning on clinical information
Maomao Ding1, Ruosha Li2, Jin Qin3
1Meta Platforms, Inc., Menlo Park, California, USA.
Biometrics
|June 14, 2023
Summary
This study introduces a robust statistical test to assess gene expression dependence, considering clinical factors. The method is computationally efficient and reliable, even with potential model inaccuracies.
Area of Science:
- Bioinformatics
- Statistical Genetics
- Computational Biology
Background:
- Assessing gene expression dependence requires conditional independence tests.
- Existing methods may be sensitive to model assumptions.
Purpose of the Study:
- To develop a double-robust test for gene expression dependence.
- To evaluate gene associations in high-dimensional data while controlling for clinical information.
Main Methods:
- Proposed a class of double-robust tests for bivariate outcomes.
- Developed a multiple testing procedure controlling the false discovery rate.
- Utilized marginal density functions for valid inference.
Main Results:
- The proposed test is robust to model misspecification.
- Demonstrated accurate control of type-I error and false discovery rate.
- The method showed computational efficiency without resampling or tuning parameters.
Conclusions:
- The double-robust test offers a reliable approach for gene coexpression network analysis.
- Applied the method to gastric cancer data for pathway analysis.
- The findings enhance understanding of gene associations in clinical contexts.
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