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pmid:29036513

ScanGEO: parallel mining of high-throughput gene expression data.

Katja Koeppen · Bruce A Stanton · Thomas H Hampton

Bioinformatics (Oxford, England) · 2017 doi:10.1093/bioinformatics/btx452

Paper

complete

Experiment

partial

Data

partial

Analysis

absent

Execution

absent

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ScanGEO: parallel mining of high-throughput gene expression data.
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Katja Koeppen
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Bruce A Stanton
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Thomas H Hampton
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Bioinformatics (Oxford, England)
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2017
abstract
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. AVAILABILITY AND IMPLEMENTATION: 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. CONTACT: katja.koeppen@dartmouth.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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