gene-expression-profiles

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.

Recurrent Variational Auto gene encoder

Methods for training and interpreting deep radiogenomic neural networks

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.

Non-Negative Matrix Factorization for Gene Expression Clustering

R package for de novo pathway enrichment using KeyPathwayMiner

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

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.

Simple (effin') Enrichment Analysis in R

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.

Arabidopsis EcoGEx (R-📦 + 🕸️-App)

NanoString classifier based on NGS training set

SPACEGERM shiny app (archived, see GitLab for active fork)

CMap2 Top Coder Data Science Marathon Match

survival of patients using ors on TCGA data

A library and toolkit for common representation and analysis of gene expression profile data

SPOT - Swift Profiling Of Transcriptomes - a shiny app for gene ranking according to user-defined expression profiles

Predicting the Probability or Occurrence of Relapse for Colorectal Cancer Patients using Gene Expression Data