CellMapper

Microarray

Infers cell type-specific expression based on co-expression similarity with known cell type marker genes. Can make accurate predictions using publicly available expression data, even when a cell type has not been isolated before.

Source attribution

  • BioconductorCellMapper

Related resources

Low- and high-level wrappers for Gemma's RESTful API. They enable access to curated expression and differential expression data from over 10,000 published studies. Gemma is a web site, database and a set of tools for the meta-analysis, re-use and sharing of genomics data, currently primarily targeted at the analysis of gene expression profiles.

101 week ago
R
Apache-2.0

GEOexplorer is a webserver and R/Bioconductor package and web application that enables users to perform gene expression analysis. The development of GEOexplorer was made possible because of the excellent code provided by GEO2R (https: //www.ncbi.nlm.nih.gov/geo/geo2r/).

52 years ago
R
GPL-3

topGO package provides tools for testing GO terms while accounting for the topology of the GO graph. Different test statistics and different methods for eliminating local similarities and dependencies between GO terms can be implemented and applied.

210 months ago
R

The ASURI (Analysis of SUrvival and patients RIsk prediction based on gene signatures) package discovers marker genes that are related to risk prediction capabilities and to a clinical variable of interest. It uses two main steps, including subsampling glmnet and unicox. The package implements robust functions to discover survival markers related to a clinical phenotype and to predict a risk score, allowing to study the patient's risk based on the gene signatures. Several plots are provided to visualise the relevance of the genes, the risk score, and patient stratification, as well as a robust version of the Kaplan-Meier curves.

03 weeks ago
R
LGPL-3 + file LICENSE

This package is intended to identify differentially expressed genes driven by Copy Number Alterations from samples with both gene expression and CNA data.

Non-parametric method for identifying differentially expressed (up- or down- regulated) genes based on the estimated percentage of false predictions (pfp). The method can combine data sets from different origins (meta-analysis) to increase the power of the identification.