CTen: a web-based platform for identifying enriched cell types from heterogeneous microarray data

Interpreting in vivo sampled microarray data is often complicated by changes in the cell population demographics. To put gene expression into its proper biological context, it is necessary to distinguish differential gene transcription from artificial gene expression induced by changes in the cellul...

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Published inBMC genomics Vol. 13; no. 1; p. 460
Main Authors Shoemaker, Jason E, Lopes, Tiago J S, Ghosh, Samik, Matsuoka, Yukiko, Kawaoka, Yoshihiro, Kitano, Hiroaki
Format Journal Article
LanguageEnglish
Published England BioMed Central Ltd 06.09.2012
BioMed Central
BMC
Subjects
Cell type enrichment
Cells
Computational Biology
Deconvolution
DNA microarrays
Gene expression
Genes
Genetic aspects
Genetic transcription
Genetics
Genomics
Humans
Influenza
Internet
Microarray data
Oligonucleotide Array Sequence Analysis
Physiological aspects
Proteins
Science
Software
Systems immunology
Websites
Japan
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Summary:Interpreting in vivo sampled microarray data is often complicated by changes in the cell population demographics. To put gene expression into its proper biological context, it is necessary to distinguish differential gene transcription from artificial gene expression induced by changes in the cellular demographics. CTen (cell type enrichment) is a web-based analytical tool which uses our highly expressed, cell specific (HECS) gene database to identify enriched cell types in heterogeneous microarray data. The web interface is designed for differential expression and gene clustering studies, and the enrichment results are presented as heatmaps or downloadable text files. In this work, we use an independent, cell-specific gene expression data set to assess CTen's performance in accurately identifying the appropriate cell type and provide insight into the suggested level of enrichment to optimally minimize the number of false discoveries. We show that CTen, when applied to microarray data developed from infected lung tissue, can correctly identify the cell signatures of key lymphocytes in a highly heterogeneous environment and compare its performance to another popular bioinformatics tool. Furthermore, we discuss the strong implications cell type enrichment has in the design of effective microarray workflow strategies and show that, by combining CTen with gene expression clustering, we may be able to determine the relative changes in the number of key cell types.CTen is available at http://www.influenza-x.org/~jshoemaker/cten/
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ISSN:1471-2164
1471-2164
DOI:10.1186/1471-2164-13-460