ExAtlas: An interactive online tool for meta-analysis of gene expression data

We have developed ExAtlas, an on-line software tool for meta-analysis and visualization of gene expression data. In contrast to existing software tools, ExAtlas compares multi-component data sets and generates results for all combinations (e.g. all gene expression profiles versus all Gene Ontology a...

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Veröffentlicht in:Journal of bioinformatics and computational biology Jg. 13; H. 6; S. 1550019
Hauptverfasser: Sharov, Alexei A, Schlessinger, David, Ko, Minoru S H
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore 01.12.2015
Schlagworte:
Analysis of Variance
Computational Biology - methods
Databases, Genetic
Gene Expression Profiling
Gene Ontology
Imaging, Three-Dimensional
Internet
Meta-Analysis as Topic
Software
Workflow
gene set enrichment
correlation matrix
ANOVA
expected proportion of false positives
Meta-analysis
PCA
ISSN:1757-6334
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Zusammenfassung:We have developed ExAtlas, an on-line software tool for meta-analysis and visualization of gene expression data. In contrast to existing software tools, ExAtlas compares multi-component data sets and generates results for all combinations (e.g. all gene expression profiles versus all Gene Ontology annotations). ExAtlas handles both users' own data and data extracted semi-automatically from the public repository (GEO/NCBI database). ExAtlas provides a variety of tools for meta-analyses: (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 produces graphical outputs, including heatmaps, scatter-plots, bar-charts, and three-dimensional images. Some of the most widely used public data sets (e.g. GNF/BioGPS, Gene Ontology, KEGG, GAD phenotypes, BrainScan, ENCODE ChIP-seq, and protein-protein interaction) are pre-loaded and can be used for functional annotations.
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ISSN:1757-6334
DOI:10.1142/S0219720015500195