Popular GitHub Repositories for Omics

scAR (single-cell Ambient Remover) is a deep learning model for removal of the ambient signals in droplet-based single cell omics

Multi-source Translation

MOVE (Multi-Omics Variational autoEncoder) for integrating multi-omics data and identifying cross modal associations

a cutting-edge cell segmentation model specifically designed for single-molecule resolved spatial omics datasets. It addresses the challenge of accurately segmenting individual cells in complex imaging datasets, leveraging a unique approach based on graph neural networks (GNNs).

SALMON: Survival Analysis Learning with Multi-Omics Neural Networks

Biology-driven deep generative model for cell-type annotation in cytometry. Scyan is an interpretable model that also corrects batch-effect and can be used for debarcoding or population discovery.

conST: an interpretable multi-modal contrastive learning framework for spatial transcriptomics

Disease subnetwork detection with explainable Graph Neural Networks

SCassist : An AI Based Workflow Assistant for Single-Cell Analysis

scDataset: Scalable Data Loading for Deep Learning on Large-Scale Single-Cell Omics

The first GANs-based omics-to-omics translation framework

MorphLink: Bridging Cellular Morphological Behaviors and Molecular Dynamics in Multi-modal Spatial Omics

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

MOVIS: A Multi-Omics Software Solution for Multi-modal Time-Series Clustering, Embedding, and Visualizing Tasks, by Aleksandar An?el, Dominik Heider, and Georges Hattab

Domain Adaptive and Fine-grained Anomaly Detection for Single-cell Sequencing Data and Beyond

An R package for deconvoluting cell types in bulk-tissue/cell-free MeDIP-seq data leveraging a Bayesian hierarchical model. Depends on RStan.

Pytorch implementation of "Multi-domain translation between single-cell imaging and sequencing data using autoencoders" (https://www.nature.com/articles/s41467-020-20249-2) with custom models.

This repository contains code for MFmap (model fidelity map), a semi-supervised generative model integrating gene expression, copy number and mutation data, matching cell lines to cancer subtypes. MFmap compresses high dimensional omics data of cell lines and bulk tumours into subtype informative low dimensional latent representations and predicts the subtype of cell lines with high accuracy and good generative performance. http://h2926513.stratoserver.net:3838/MFmap_shiny/

SpaTranslator is a graph-based generative model for cross-modality prediction in spatial omics data. It enables the in silico generation of missing omics modalities—such as spatial ATAC-seq, RNA-seq, and histone modification profiles—across paired or unpaired tissue sections, by leveraging spatial neighborhood information and adversarial learning.