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

Shoemaker JE, Lopes TJ, Ghosh S, Matsuoka Y, Kawaoka Y, Kitano H - BMC Genomics (2012)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: JST ERATO KAWAOKA Infection-induced Host Responses Project, Tokyo, Japan. jshoe@ims.u-tokyo.ac.jp

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 (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.

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/

Show MeSH

Related in: MedlinePlus

CTen versus GO analysis. A list of upregulated genes in lung tissue collected from mice infected with the influenza virus is analyzed in (A) CTen and (B) DAVID. The first cluster to have a cell specific term is ranked 29th in the DAVID analysis. A complete list of the terms belonging to each cluster is available in Additional file 7.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3473317&req=5

Figure 7: CTen versus GO analysis. A list of upregulated genes in lung tissue collected from mice infected with the influenza virus is analyzed in (A) CTen and (B) DAVID. The first cluster to have a cell specific term is ranked 29th in the DAVID analysis. A complete list of the terms belonging to each cluster is available in Additional file 7.

Mentions: Using a list of genes found to be upregulated in lung tissue collected from mice infected with influenza virus (microarray data unpublished; the gene list is available on the CTen website under the "Simple Example" tab), we compared the results of a CTen analysis to a GO analysis using DAVID [7]. Using the CTen website, we find a very high enrichment of bone marrow derived and peritoneal macrophages (Figure 7A), both of which have been exposed to lipopolysaccharide (LPS) and collected at different time points. Macrophage migration to the site of infection is one of the first steps in coordinating the innate immune response [19]. Both LPS exposure [20] and influenza infection [21] induces the activation of the Toll-like receptor pathways, and macrophages are often susceptible to influenza infection themselves [22]. Thus, an increase in macrophage numbers is consistent with previously published studies [23] and the observation of the resulting cell type as "macrophage exposed to LPS", indicates that the macrophages have possibly become infected with the influenza virus as well.


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

Shoemaker JE, Lopes TJ, Ghosh S, Matsuoka Y, Kawaoka Y, Kitano H - BMC Genomics (2012)

CTen versus GO analysis. A list of upregulated genes in lung tissue collected from mice infected with the influenza virus is analyzed in (A) CTen and (B) DAVID. The first cluster to have a cell specific term is ranked 29th in the DAVID analysis. A complete list of the terms belonging to each cluster is available in Additional file 7.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3473317&req=5

Figure 7: CTen versus GO analysis. A list of upregulated genes in lung tissue collected from mice infected with the influenza virus is analyzed in (A) CTen and (B) DAVID. The first cluster to have a cell specific term is ranked 29th in the DAVID analysis. A complete list of the terms belonging to each cluster is available in Additional file 7.
Mentions: Using a list of genes found to be upregulated in lung tissue collected from mice infected with influenza virus (microarray data unpublished; the gene list is available on the CTen website under the "Simple Example" tab), we compared the results of a CTen analysis to a GO analysis using DAVID [7]. Using the CTen website, we find a very high enrichment of bone marrow derived and peritoneal macrophages (Figure 7A), both of which have been exposed to lipopolysaccharide (LPS) and collected at different time points. Macrophage migration to the site of infection is one of the first steps in coordinating the innate immune response [19]. Both LPS exposure [20] and influenza infection [21] induces the activation of the Toll-like receptor pathways, and macrophages are often susceptible to influenza infection themselves [22]. Thus, an increase in macrophage numbers is consistent with previously published studies [23] and the observation of the resulting cell type as "macrophage exposed to LPS", indicates that the macrophages have possibly become infected with the influenza virus as well.

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: JST ERATO KAWAOKA Infection-induced Host Responses Project, Tokyo, Japan. jshoe@ims.u-tokyo.ac.jp

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 (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.

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/

Show MeSH
Related in: MedlinePlus