Reference mapping for single-cell genomics

Toolkit for highly memory efficient analysis of single-cell RNA-Seq, scATAC-Seq and CITE-Seq data. Analyze atlas scale datasets with millions of cells on laptop.

The Proteomics Experimental Design file format: Standard for experimental design annotation

Read specialized NGS formats as data frames in R, Python, and more.

𝒫robabilistic modeling of RNA velocity ⬱

scMEGA: Single-cell Multiomic Enhancer-based Gene regulAtory network inference

SIMBA: SIngle-cell eMBedding Along with features

A repository to convert SDRF proteomics files into pipelines config files

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

MerCat2: python code for versatile k-mer counting and diversity estimation for database independent property analysis for metaome data

A novel method for single-cell diagonal integration: scConfluence

DeepInsight3D package to deal with multi-omics or multi-layered data

It contains the functional code to analyze P2 scRNA-seq and scATAC-seq data.

This is a R package that intends to perform all the features possible by tensor decomposition based unsupervised feature extraction

Code for Highly Trustworthy Multimodal Learning (HTML) Method on Omics

R package for de novo pathway enrichment using KeyPathwayMiner

A pythonic library for analysing immunopeptidomic experiments

Regional Association of Methylation variability with the Exposome and geNome (RAMEN) is an R package whose goal is to identify Variable Methylated Regions (VMRs) in microarray DNA methylation data. Additionally, using Genotype (G) and Environmental (E) data, it can identify which G, E, G+E or GxE model better explains this variability.

Visual exploration of multi-dimensional proteomics data