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

Spatial alignment of single cell transcriptomic data.

One-step to Cluster and Visualize Gene Expression Matrix

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

CodonTransformer (1M+ Downloads); The tool for codon optimization, optimizing DNA for protein expression

A framework for state-of-the-art pre-trained bio foundation models on genomics and transcriptomics modalities.

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

R package to access DoRothEA's regulons

Deep learning for gene expression inference

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

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

Building classifiers using cancer transcriptomes across 33 different cancer-types

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.

A repository with exploration into using transformers to predict DNA ↔ transcription factor binding

BASiCS: Bayesian Analysis of Single-Cell Sequencing Data. This is an unstable experimental version. Please see http://bioconductor.org/packages/BASiCS/ for the official release version

integrated RNA-seq Analysis Pipeline

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