Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.

🐟 🍣 🍱 Highly-accurate & wicked fast transcript-level quantification from RNA-seq reads using selective alignment

NicheNet: predict active ligand-target links between interacting cells

Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch

Training and evaluating a variational autoencoder for pan-cancer gene expression data

Building classifiers using cancer transcriptomes across 33 different cancer-types

Deep learning for gene expression inference

Spatial alignment of single cell transcriptomic data.

Gene Expression Omnibus Analysis with Shiny 🔬

R/shiny interface for interactive visualization of data in SummarizedExperiment objects

😎 A curated list of software and resources for exploring and visualizing (browsing) expression data 😎

Python3 binding to mRMR Feature Selection algorithm (currently not maintained)

integrated RNA-seq Analysis Pipeline

Data and code related to the paper "ADAGE-Based Integration of Publicly Available Pseudomonas aeruginosa..." Jie Tan, et al · mSystems · 2016

TCGA data acquisition and processing for Project Cognoma

R package to access DoRothEA's regulons

Cell-Centric View of Tissue Transcriptome Measuring Cellular Compositions of Immune Microenvironment From Mouse RNA-Seq Data.

BioBombe: Sequentially compressed gene expression features enhances biological signatures

Power analysis is essential to optimize the design of RNA-seq experiments and to assess and compare the power to detect differentially expressed genes. PowsimR is a flexible tool to simulate and evaluate differential expression from bulk and especially single-cell RNA-seq data making it suitable for a priori and posterior power analyses.

Repository for the R package EPIC, to Estimate the Proportion of Immune and Cancer cells from bulk gene expression data.