Email updates

Keep up to date with the latest news and content from BMC Genomics and BioMed Central.

Open Access Software

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

Jason E Shoemaker1*, Tiago JS Lopes1, Samik Ghosh2, Yukiko Matsuoka12, Yoshihiro Kawaoka134 and Hiroaki Kitano1256

Author affiliations

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

2 The Systems Biology Institute, 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 Sony Computer Science Laboratories, Inc, Tokyo, Japan

6 Open Biology Unit, Okinawa Institute of Science and Technology, Okinawa, Japan

For all author emails, please log on.

Citation and License

BMC Genomics 2012, 13:460  doi:10.1186/1471-2164-13-460

Published: 6 September 2012

Abstract

Background

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.

Results

CTen (

    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.

Conclusions

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/ webcite

Keywords:
Cell type enrichment; Microarray data; Deconvolution; Influenza; Systems immunology