Python package for integrating and analyzing multiple single-cell datasets (A Python version of LIGER)

Cartography of Genomic Interactions Enables Deep Analysis of Single-Cell Expression Data (Nature Communications, 2023)

INTEND (IntegratioN of Transcriptomic and EpigeNomic Data), a novel algorithm for integrating gene expression and DNA methylation datasets covering disjoint sets of samples

MOSS: Multi-Omic integration via Sparse Singular Decomposition

An interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis

Codebase and Data for the work on Network diffusion-based approach for survival prediction and identification of biomarkers using multi-omics data of Papillary Renal Cell Carcinoma

Python implementation of NetICS (Network-based Integration of Multi-omics data).

MOFA (Multi-Omics Factor Analysis) is a computational framework and statistical method designed for the integration and analysis of multi-omics data.

PhD thesis on data integration on inflammatory bowel disease

Modeling the metabolic changes during the epithelial-to-mesenchymal transition.

MOFA+ (Multi-Omics Factor Analysis) online tool