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

One-step to Cluster and Visualize Gene Expression Matrix

Spatial alignment of single cell transcriptomic data.

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

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

R package to access DoRothEA's regulons

CodonTransformer: The ultimate tool for codon optimization, optimizing DNA sequences for heterologous protein expression across 164 species.

Deep learning for gene expression inference

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

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

Building classifiers using cancer transcriptomes across 33 different cancer-types

This repository contains the python package for Helical

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

integrated RNA-seq Analysis Pipeline

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

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