BiGER: Bayesian Rank Aggregation in Genomics with Extended Ranking Schemes
Wang, K.; Yang, Y.; Xia, Y.; Xiao, G.; Lim, J.; Wang, X.
Show abstract
With the rise of large-scale genomic studies, large gene lists targeting important diseases are increasingly common. While evaluating each study individually gives valuable insights on specific samples and study designs, the wealth of available evidence in the literature calls for robust and efficient meta-analytic methods. Crucially, the diverse assumptions and experimental protocols underlying different studies require a flexible but rigorous method for aggregation. To address these issues, we propose BiGER, a fast and accurate Bayesian rank aggregation method for the inference of latent global rankings. Unlike existing methods in the field, BiGER accommodates mixed gene lists with top-ranked and top-unranked genes as well as bottom-tied and missing genes, by design. Using a Bayesian hierarchical framework combined with variational inference, BiGER efficiently aggregates large-scale gene lists with high accuracy, while providing valuable insights into source-specific reliability for researchers. Through both simulated and real datasets, we show that BiGER is a useful tool for reliable meta-analysis in genomic studies.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Identification of putative causal loci in whole-genome sequencing data via knockoff statistics 95%
- Simultaneous estimation of bi-directional causal effects and heritable confounding from GWAS summary statistics 95%
- Testing and controlling for horizontal pleiotropy with the probabilistic Mendelian randomization in transcriptome-wide association studies 94%
Similar papers in this journal
- Deep Mendelian Randomization: Investigating the causal knowledge of genomic deep learning models 93%
- Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data 93%
- Efficient and Flexible Integration of Variant Characteristics in Rare Variant Association Studies Using Integrated Nested Laplace Approximation 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.