ScanGEO: parallel mining of high-throughput gene expression data
Current options to mine publicly available gene expression data deposited in NCBI's gene expression omnibus (GEO), such as the GEO web portal and related applications, are optimized to reanalyze a single study, or search for a single gene, and therefore require manual intervention to reanalyze...
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Published in | Bioinformatics (Oxford, England) Vol. 33; no. 21; pp. 3500 - 3501 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
England
Oxford University Press
01.11.2017
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Subjects | |
Online Access | Get full text |
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Summary: | Current options to mine publicly available gene expression data deposited in NCBI's gene expression omnibus (GEO), such as the GEO web portal and related applications, are optimized to reanalyze a single study, or search for a single gene, and therefore require manual intervention to reanalyze multiple studies for user-specified gene sets. ScanGEO is a simple, user-friendly Shiny web application designed to identify differentially expressed genes across all GEO studies matching user-specified criteria, for a flexible set of genes, visualize results and provide summary statistics and other reports using a single command.
The ScanGEO source code is written in R and implemented as a Shiny app that can be freely accessed at http://scangeo.dartmouth.edu/ScanGEO/. For users who would like to run a local instantiation of the app, the R source code is available under a GNU GPLv3 license at https://github.com/StantonLabDartmouth/AppScanGEO.
katja.koeppen@dartmouth.edu.
Supplementary data are available at Bioinformatics online. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1367-4803 1367-4811 |
DOI: | 10.1093/bioinformatics/btx452 |