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

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

Deep learning methods for feature selection in gene expression autism data.

TissUUmaps is a browser-based tool for fast visualization and exploration of millions of data points overlaying a tissue sample. TissUUmaps can be used as a web service or locally in your computer, and allows users to share regions of interest and local statistics.

Recurrent Variational Auto gene encoder

Methods for training and interpreting deep radiogenomic neural networks

Non-Negative Matrix Factorization for Gene Expression Clustering

Network Regression Embeddings reveal cell-type Transcription Factor coordination for target gene (TG) regulation

orthomap is a python package to extract orthologous maps (in other words the evolutionary age of a given orthologous group) from OrthoFinder/eggNOG results. Orthomap results (gene ages per orthogroup) can be further used to calculate weigthed expression data (transcriptome evolutionary index) from scRNA sequencing objects.

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

R package for de novo pathway enrichment using KeyPathwayMiner

Integrative approach to feature selection combining weighted LASSO and prior biological knowldge

Scripts and data from "Towards a global investigation of transcriptomic signatures through co-expression networks and pathway knowledge for the identification of disease mechanisms"

Shared TREM-1 expression signatures of asthma affection and control

scTEI - add any phylogenetically based transcriptome evolutionary index (TEI) to single-cell data objects

Simple (effin') Enrichment Analysis in R

ROSeq - A rank based approach to modeling gene expression with filtered and normalized read count matrix. Takes in the complete filtered and normalized read count matrix, the location of the two sub-populations and the number of cores to be used.

Weighted Gene Co-expression Network Analysis;

The goal of iCTC is to detect whether peripheral blood cells have CTCs (circulating tumor cell) or not.

A multi-response Gaussian model capable of accurately estimating the composition of blood samples from their gene expression profiles. Fit on Affymetrix Gene ST gene expression profiles using the glmnet R package.