Grace-AKO: a novel and stable knockoff filter for variable selection incorporating gene network structures.
Peixin Tian1, Yiqian Hu1, Zhonghua Liu2
1Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, China.
BMC Bioinformatics
|November 15, 2022
Summary
This study introduces Grace-AKO, a new method for selecting important genes in high-dimensional data. It effectively controls false discovery rates (FDR) and identifies key genes, improving upon existing models.
Area of Science:
- Genomics
- Biostatistics
- Bioinformatics
Background:
- Variable selection is crucial for identifying genes linked to clinical outcomes in genomic studies.
- High-dimensional data with limited samples necessitates methods to control false discovery rate (FDR).
Purpose of the Study:
- To propose a novel method, Grace-AKO, for graph-constrained estimation to identify significant genes.
- To control finite-sample FDR and enhance model stability in high-dimensional genomic data.
Main Methods:
- Grace-AKO combines aggregation of multiple knockoffs (AKO) with a network-constrained penalty.
- The method is designed to control FDR in finite-sample settings.
Main Results:
- Simulation studies demonstrate Grace-AKO's superior finite-sample FDR control compared to the original Grace model.
- Application to prostate cancer data identified 47 candidate genes associated with prostate-specific antigen (PSA) levels.
- Over 75% of the identified genes were validated, indicating strong biological relevance.
Conclusions:
- Grace-AKO offers an effective approach for gene selection in high-dimensional data.
- The method successfully controls FDR and improves model stability, with validated findings in a real-world cancer dataset.
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