Trhova/Multi-omics
Intuitive guide to multi-omics integration with toy examples: supervised latent components (DIABLO), unsupervised shared/partial/unique structure (DIVAS), VAEs/conditional VAEs, and key baselines (MOFA, JIVE, SNF) with practical tips + code.
This guide helps life scientists integrate multiple types of 'omics' data, such as microbiome, metabolomics, and transcriptomics, to uncover biological patterns. You input your patient-level omics datasets, and it shows you how to extract meaningful shared or unique insights across these data layers, resulting in a better understanding of disease states or biological mechanisms. It's designed for researchers, biologists, and data scientists working with multi-omics data who want to move beyond simple correlations.
Use this if you have multiple 'omics' datasets for the same set of biological samples and want to understand how they interact to explain patient groups or underlying biological processes.
Not ideal if you are looking for a tool to establish definitive causal relationships, as multi-omics integration helps find patterns but does not prove causation on its own.
Stars
76
Forks
11
Language
Python
License
—
Category
Last pushed
Dec 28, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Trhova/Multi-omics"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
scverse/scvi-tools
Deep probabilistic analysis of single-cell and spatial omics data
scverse/scanpy
Single-cell analysis in Python. Scales to >100M cells.
Teichlab/celltypist
A tool for semi-automatic cell type classification
theislab/scarches
Reference mapping for single-cell genomics
Lotfollahi-lab/nichecompass
End-to-end analysis of spatial multi-omics data