BARRR: Bidimensional Augmented Reduced Rank Regression for Multi-View Multi-Cohort Data
Bidimensional data integration across multiple cohorts (i.e., sample groups) and views (i.e., sources or modalities) is crucial in biomedical research to derive holistic insights from heterogeneous datasets.
ProofPaper ↗
Key points
- Existing approaches for bidimensional integration are scarce and predominantly unsupervised, overlooking the valuable insights offered by covariates.
- To address this gap, we introduce the Bidimensional Augmented Reduced Rank Regression (baRRR) method.
- Our approach models covariate-related and unrelated effects concurrently, while also decomposing effect levels (global, partially shared, and individual) across both dimensions.
- We apply our approach to data from The Cancer Genome Atlas (TCGA), using somatic mutations as covariates to inform patterns of variability across different omics platforms and cancer types.
Sources (1)
- [1]BARRR: Bidimensional Augmented Reduced Rank Regression for Multi-View Multi-Cohort DataarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 05:06 AM
Bidimensional data integration across multiple cohorts (i.e., sample groups) and views (i.e., sources or modalities) is crucial in biomedical research to derive holistic insights from heterogeneous datasets.
Existing approaches for bidimensional integration are scarce and predominantly unsupervised, overlooking the valuable insights offered by covariates.
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