1 - 34 of 34 results

MEM / Multi Experiment Matrix

Detects co-expressed genes in large platform-specific microarray collections. MEM is a web application for searching gene expression similarity. The software encompasses a variety of conditions, tissues and disease states and incorporates about a thousand datasets for both human and mouse, as well as hundreds of datasets for other model organisms. The results are presented in a graphical user interface that opens up several paths for further data analysis.


A package for meta-analysis based on ordered gene lists like those resulting from differential gene expression analysis. OrderedList quantifies the similarity between gene lists. The significance of the similarity score is estimated from random scores computed on perturbed data. It illustrates list similarity in intuitive plots and determines the score-driving genes for further analysis. In addition, OrderedList detects how far into the lists striking similarities occur. Finally, the algorithm determines the genes that drive the observed similarity score, i.e. genes with prominent ranks in all compared lists. These genes are most promissing for further analysis and interpretation.


Allows to work about omics data meta-analysis of differentially expressed gene detection, pathway, prediction, clustering, classification and network analyses. MetaKTSP contains several approaches that combine multiple omics data sets to improve the credibility of top scoring pair (TSP) biomarker selection. This tool implements a meta-analytic top scoring pair (MetaTSP) algorithm that combines multiple transcriptomic studies and generates a robust prediction model applicable to independent test studies.


A general probabilistic framework for combining high-throughput genomic data from several related microarray experiments using mixture models. A key feature of the model is the use of latent variables that represent quantities that can be combined across diverse platforms. We consider two methods for estimation of an index termed the probability of expression (POE). The first involves Markov Chain Monte Carlo (MCMC) techniques. The second method is a faster algorithm based on the expectation-maximization (EM) algorithm. metaArray allows data transformation for meta-analysis of microarray Data and combines differential expression on raw scale.


Contains tools for combining the results of multiple gene expression studies. metahdep accounts for both sampling and hierarchical dependence among studies, as well as fundamental covariate differences between studies. It first formats the data for meta-analysis, starting with the original .CEL files. A necessary step in formatting is the matching of probesets across array versions based on common gene content. metahdep requires the construction of a data.frame object mapping probeset IDs to common ‘new names’ for multiple array versions. Next, an effect size estimate (and associated variance estimate) representing the degree of differential expression for each probeset in each study is calculated. A common platform is not necessary to use the metahdep package, although users will need to define appropriate effect size estimates and should be aware of possible cross-platform inconsistencies.


A package and associated object definitions to merge and visualize multiple gene expression datasets. MergeMaid uses arbitrary character IDs and generates objects that can efficiently support a variety of joint analyses. Visualization tools support exploration and cross-study validation of the data, without requiring normalization across platforms. Tools include ``integrative correlation'' plots that is, scatterplots of all pairwise correlations in one study against the corresponding pairwise correlations of another, both for individual genes and all genes combined. Gene-specific plots can be used to identify genes whose changes are reliably measured across studies. Visualizations also include scatterplots of gene-specific statistics quantifying relationships between expression and phenotypes of interest, using linear, logistic and Cox regression.


Integrates different gene expression data for differentially expressed gene detection in an easy and efficient way. ShinyMDE handles processed and raw data generated from most widely used data platforms such as Affymetrix and Illumina. It provides user with an option of choosing the method of their choice from the list for meta-analysis. ShinyMDE is very simple to use and is developed with the aim of having an automated meta-analysis of gene expression data facilitating screening and downloading the results.

MiMiR / Microarray data Mining Resource

Enables systematic and effective capture of extensive experimental and clinical information with the highest minimum information about a microarray experiment (MIAME) score. MiMiR consists of an integrated platform for microarray data sharing, mining and analysis. It provides an environment for collection, capture, consistent annotation, visualization and dissemination of data. It contains over 3000 arrays worth of data for mining and analysis and supports over 200 research groups, including two international consortia.

A-MADMAN / Annotation-based MicroArray DataMeta ANalysis tool

Enables the retrieval, organization and meta-analysis of microarray expression data from public repositories. A-MADMAN allows retrieving gene expression datasets from Gene Expression Omnibus (GEO), annotating and locally organizing the downloaded samples, and generating an R object (ExpressionSet) which contains the integrated expression levels and all available metadata and sample characteristics. The software provides features specifically designed to plan and conduct meta-analyses of microarray expression data.

metafor / Meta-Analysis Package for R

A comprehensive collection of functions for conducting meta-analyses in R. The metafor package includes functions to calculate various effect sizes or outcome measures, fit fixed, random, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots. For meta-analyses of binomial and person-time data, metafor also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models).


A web app for meta-analysis and visualization of gene expression data. ExAtlas offers a set of computational and management tools for meta-analysis of data stored in the GEO database: (1) standard meta-analysis (fixed effects, random effects, z-score, and Fisher's methods); (2) analyses of global correlations between gene expression data sets; (3) gene set enrichment; (4) gene set overlap; (5) gene association by expression profile; (6) gene specificity; and (7) statistical analysis (ANOVA, pairwise comparison, and PCA). ExAtlas provides an option to identify coregulated genes. If a corresponding box is checked in the page for starting correlation analysis, then ExAtlas will identify lists of genes that are both upregulated or both downregulated in two data files.

INMEX / INtegrative Meta-analysis of EXpression data

Assists researchers in conducting two common types of analyses - meta-analysis of multiple gene expression datasets ( meta-analysis) or joint analysis of a gene expression dataset and a metabolomic dataset (integrative analysis), that have been collected under the same or comparable biological conditions. INMEX supports facile data upload, flexible data annotation, comprehensive meta-analysis approaches, as well as integrative analysis of metabolomic and transcriptomic data. With the increasing numbers of data sets that are being generated and becoming publicly available, INMEX will become a valuable tool to the research community.


Allows storage, analysis, and integration of microarray data and related genotype and phenotype data. PhenoGen enables researchers to mine data from microarray expression experiments. The website allows access to and manipulation of whole brain gene expression data from the BXD recombinant inbred (RI) mouse panel, the HXB/BXH RI rat panel, and several commonly used inbred strains of mice. It also offers the ability for extensive quality control, user-generated datasets, and a broad choice of statistical analysis.

Gene Expression Commons

Allows users to normalize microarray data. Gene Expression Commons is composed of two mains panels (i) Population, that stores several microarray data normalized according to the common reference and (ii) Model, that permits users to visualize biological context and relationships belonging to the first panel through 2D information. Users can submit their own data and combines different features for generating a customized model and share it with other users. The program can store information with different levels of privacy.