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

BMC Genomics

Software

Jason E Shoemaker¹  Tiago JS Lopes¹  Yoshihiro Kawaoka²  Yukiko Matsuoka³  Hiroaki Kitano⁴  Samik Ghosh⁵ 

[1]JST ERATO KAWAOKA Infection-induced Host Responses Project, Tokyo, Japan

[2]JST ERATO KAWAOKA Infection-induced Host Responses Project, Tokyo, Japan

[3]Influenza Research Institute, Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA

[4]Institute of Medical Science, Division of Virology, Department of Microbiology and Immunology, University of Tokyo, Tokyo, Japan

[5]JST ERATO KAWAOKA Infection-induced Host Responses Project, Tokyo, Japan

[6]The Systems Biology Institute, Tokyo, Japan

[7]JST ERATO KAWAOKA Infection-induced Host Responses Project, Tokyo, Japan

[8]The Systems Biology Institute, Tokyo, Japan

[9]Sony Computer Science Laboratories, Inc, Tokyo, Japan

[10]Open Biology Unit, Okinawa Institute of Science and Technology, Okinawa, Japan

[11]The Systems Biology Institute, Tokyo, Japan

关键词: Cell type enrichment;    Microarray data;    Deconvolution;    Influenza;    Systems immunology;   

DOI  :  10.1186/1471-2164-13-460

 received in 2011-12-09, accepted in 2012-08-31,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundInterpreting 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.ResultsCTen (c ell t ype en richment) 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.ConclusionsIn 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/

【 授权许可】

CC BY   
© Shoemaker et al.; licensee BioMed Central Ltd. 2012