High-Dimensional Covariate-Augmented Generalized Factor Model
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Existing methods for multi-omics representation learning often lack interpretability or overlook critical omics-specific and additional information. To address these limitations and meet the practical demands, we introduce CMGFM, an interpretable multi-omics representation learning approach via covariate-augumented generalized factor model. CMGFM is designed to account for cross-modal heterogeneity, capture nonlinear dependencies among the data, incorporate additional information, and provide excellent interpretability while maintaining high computational efficiency.
Check out our Package Website for a more complete description of the methods and analyses.
InstallationâCMGFMâ depends on the âRcppâ and âRcppArmadilloâ package, which requires appropriate setup of computer. For the users that have set up system properly for compiling C++ files, the following installation command will work.
## Method 1ï¼
if (!require("remotes", quietly = TRUE))
install.packages("remotes")
remotes::install_github("feiyoung/CMGFM")
## Method 2: install from CRAN
install.packages("CMGFM")
For usage examples and guided walkthroughs, check the vignettes directory of the repo.
For the codes in simulation study, check the simu_code directory of the repo.
CMGFM version 1.1 released! (2024-06-23)
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