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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/

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Detecting changes in lymphocyte cell counts and the appropriateness of follow-up analyses using CTen. (A) In infected tissue, as B cells (green), T cells (black), and macrophages (dark red) migrate into the sample area, they bring with them their own unique quantities of RNA, resulting in conserved expression patterns proportional to the cell type's increased (or decreased) presence. Here, we illustrate the potential results from a clustering study using WGCNA [15], resulting in (B) 4 clusters with unique temporal expression profiles; 3 of which are highly correlated to the changes in the number of lymphocytes shown in (A). (C) Analyzing the genes present in each cluster, CTen can distinguish which clusters represent gene expression and which represent cell migration, and determine the cell type responsible for the observed gene regulation. Using a "weighted-ranking" strategy (see text), CTen produces a heatmap showing the most enriched cell types for each cluster. Based on the CTen result, only the orange cluster is appropriate for further analysis using traditional bioinformatic techniques while the green, black, and dark red clusters reflect the relative changes in the number of lymphocytes during the infection. (D) We propose a new analytical workflow strategy which ensures that continued analysis of in vivo microarray data properly identifies events which may be coordinated with (or coordinating) cell migration.
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Figure 8: Detecting changes in lymphocyte cell counts and the appropriateness of follow-up analyses using CTen. (A) In infected tissue, as B cells (green), T cells (black), and macrophages (dark red) migrate into the sample area, they bring with them their own unique quantities of RNA, resulting in conserved expression patterns proportional to the cell type's increased (or decreased) presence. Here, we illustrate the potential results from a clustering study using WGCNA [15], resulting in (B) 4 clusters with unique temporal expression profiles; 3 of which are highly correlated to the changes in the number of lymphocytes shown in (A). (C) Analyzing the genes present in each cluster, CTen can distinguish which clusters represent gene expression and which represent cell migration, and determine the cell type responsible for the observed gene regulation. Using a "weighted-ranking" strategy (see text), CTen produces a heatmap showing the most enriched cell types for each cluster. Based on the CTen result, only the orange cluster is appropriate for further analysis using traditional bioinformatic techniques while the green, black, and dark red clusters reflect the relative changes in the number of lymphocytes during the infection. (D) We propose a new analytical workflow strategy which ensures that continued analysis of in vivo microarray data properly identifies events which may be coordinated with (or coordinating) cell migration.

Mentions: Figure 8 illustrates the proposed strategy for identifying cellular signatures in in vivo data and its implications for in vivo microarray based studies. In this illustration, microarray data was assembled over a span of 5 days from the lungs of mice infected with influenza virus on day 0 (lung tissues are illustrated in Figure 8A). After normalizing and differential expression testing, four gene clusters (Figure 8B) were identified using the user's preferred clustering tool.


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)

Detecting changes in lymphocyte cell counts and the appropriateness of follow-up analyses using CTen. (A) In infected tissue, as B cells (green), T cells (black), and macrophages (dark red) migrate into the sample area, they bring with them their own unique quantities of RNA, resulting in conserved expression patterns proportional to the cell type's increased (or decreased) presence. Here, we illustrate the potential results from a clustering study using WGCNA [15], resulting in (B) 4 clusters with unique temporal expression profiles; 3 of which are highly correlated to the changes in the number of lymphocytes shown in (A). (C) Analyzing the genes present in each cluster, CTen can distinguish which clusters represent gene expression and which represent cell migration, and determine the cell type responsible for the observed gene regulation. Using a "weighted-ranking" strategy (see text), CTen produces a heatmap showing the most enriched cell types for each cluster. Based on the CTen result, only the orange cluster is appropriate for further analysis using traditional bioinformatic techniques while the green, black, and dark red clusters reflect the relative changes in the number of lymphocytes during the infection. (D) We propose a new analytical workflow strategy which ensures that continued analysis of in vivo microarray data properly identifies events which may be coordinated with (or coordinating) cell migration.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 8: Detecting changes in lymphocyte cell counts and the appropriateness of follow-up analyses using CTen. (A) In infected tissue, as B cells (green), T cells (black), and macrophages (dark red) migrate into the sample area, they bring with them their own unique quantities of RNA, resulting in conserved expression patterns proportional to the cell type's increased (or decreased) presence. Here, we illustrate the potential results from a clustering study using WGCNA [15], resulting in (B) 4 clusters with unique temporal expression profiles; 3 of which are highly correlated to the changes in the number of lymphocytes shown in (A). (C) Analyzing the genes present in each cluster, CTen can distinguish which clusters represent gene expression and which represent cell migration, and determine the cell type responsible for the observed gene regulation. Using a "weighted-ranking" strategy (see text), CTen produces a heatmap showing the most enriched cell types for each cluster. Based on the CTen result, only the orange cluster is appropriate for further analysis using traditional bioinformatic techniques while the green, black, and dark red clusters reflect the relative changes in the number of lymphocytes during the infection. (D) We propose a new analytical workflow strategy which ensures that continued analysis of in vivo microarray data properly identifies events which may be coordinated with (or coordinating) cell migration.
Mentions: Figure 8 illustrates the proposed strategy for identifying cellular signatures in in vivo data and its implications for in vivo microarray based studies. In this illustration, microarray data was assembled over a span of 5 days from the lungs of mice infected with influenza virus on day 0 (lung tissues are illustrated in Figure 8A). After normalizing and differential expression testing, four gene clusters (Figure 8B) were identified using the user's preferred clustering tool.

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