gene-expression

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

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

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

Deep learning for gene expression inference

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

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

Building classifiers using cancer transcriptomes across 33 different cancer-types

Spatial alignment of single cell transcriptomic data.

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

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

R package to access DoRothEA's regulons

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

Enjoy your transcriptomic data and analysis responsibly - like sipping a cocktail

Fast visualization tool for large-scale and high dimensional single-cell data

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

BioBombe: Sequentially compressed gene expression features enhances biological signatures

Haystack: Epigenetic Variability and Transcription Factor Motifs Analysis Pipeline

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

Gene Expression Omnibus Analysis with Shiny 🔬

This project aims to predict people who will survive breast cancer using machine learning models with the help of clinical data and gene expression profiles of the patients.

GREIN : GEO RNA-seq Experiments Interactive Navigator

A random-forest-based approach for imputing clustered incomplete data

characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomics data with nonuniform cellular densities

Bayesian inference of clone-specific gene expression estimates by integrating single-cell RNA-seq and single-cell DNA-seq data

A COBRApy extension for genome-scale models of metabolism and expression (ME-models)