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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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CTen validation and robustness of the enrichment score rankings. From an additional cell-specific gene expression database, we took the top 5% most highly expressed genes for five kinds of tissue (lung, bone marrow, kidney, spleen and liver) and three lymphocytes in different cellular states (CD4+ and CD8+ T cells, B cells and macrophages that were unstimulated or collected 7 hours after simulation with LPS). Each list was analyzed in CTen and we show the top 5 enrichments scores for (A) the tissue (B) lymphocyte test lists. To evaluate the robustness of these results, we repeated this procedure for the top 2-10% most highly expressed genes in each cell type. The enrichment scores from CTen were ranked from highest to lowest, and (C) the heatmap illustrates the top 3 most enriched cell types identified by CTen (columns) for each lymphocyte data tested (row labels). The bar plot on the right hand side summarizes the number of genes per test list. The heatmap for the tissue data is available as Additional file 5.
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Figure 5: CTen validation and robustness of the enrichment score rankings. From an additional cell-specific gene expression database, we took the top 5% most highly expressed genes for five kinds of tissue (lung, bone marrow, kidney, spleen and liver) and three lymphocytes in different cellular states (CD4+ and CD8+ T cells, B cells and macrophages that were unstimulated or collected 7 hours after simulation with LPS). Each list was analyzed in CTen and we show the top 5 enrichments scores for (A) the tissue (B) lymphocyte test lists. To evaluate the robustness of these results, we repeated this procedure for the top 2-10% most highly expressed genes in each cell type. The enrichment scores from CTen were ranked from highest to lowest, and (C) the heatmap illustrates the top 3 most enriched cell types identified by CTen (columns) for each lymphocyte data tested (row labels). The bar plot on the right hand side summarizes the number of genes per test list. The heatmap for the tissue data is available as Additional file 5.

Mentions: We found that CTen consistently ranked the correct cell type the highest for each tissue tested (Figure 5A) and, with the exception of bone marrow, there was a large difference in the scores between the first and second most enriched cell types. Not surprisingly, bone marrow was identified as being highly enriched for bone. For the lymphocyte gene lists (Figure 5B), CTen not only identified the correct lymphocyte but most often identified the correct cellular state of the lymphocyte as being the top ranked cell type. Only for the unstimulated macrophages did CTen rank the inappropriate cellular states the highest. Thus, from independent, cell-specific gene expression data, we confirmed that CTen provides clear guidance in relating gene expression data to the appropriate cell type.


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 validation and robustness of the enrichment score rankings. From an additional cell-specific gene expression database, we took the top 5% most highly expressed genes for five kinds of tissue (lung, bone marrow, kidney, spleen and liver) and three lymphocytes in different cellular states (CD4+ and CD8+ T cells, B cells and macrophages that were unstimulated or collected 7 hours after simulation with LPS). Each list was analyzed in CTen and we show the top 5 enrichments scores for (A) the tissue (B) lymphocyte test lists. To evaluate the robustness of these results, we repeated this procedure for the top 2-10% most highly expressed genes in each cell type. The enrichment scores from CTen were ranked from highest to lowest, and (C) the heatmap illustrates the top 3 most enriched cell types identified by CTen (columns) for each lymphocyte data tested (row labels). The bar plot on the right hand side summarizes the number of genes per test list. The heatmap for the tissue data is available as Additional file 5.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: CTen validation and robustness of the enrichment score rankings. From an additional cell-specific gene expression database, we took the top 5% most highly expressed genes for five kinds of tissue (lung, bone marrow, kidney, spleen and liver) and three lymphocytes in different cellular states (CD4+ and CD8+ T cells, B cells and macrophages that were unstimulated or collected 7 hours after simulation with LPS). Each list was analyzed in CTen and we show the top 5 enrichments scores for (A) the tissue (B) lymphocyte test lists. To evaluate the robustness of these results, we repeated this procedure for the top 2-10% most highly expressed genes in each cell type. The enrichment scores from CTen were ranked from highest to lowest, and (C) the heatmap illustrates the top 3 most enriched cell types identified by CTen (columns) for each lymphocyte data tested (row labels). The bar plot on the right hand side summarizes the number of genes per test list. The heatmap for the tissue data is available as Additional file 5.
Mentions: We found that CTen consistently ranked the correct cell type the highest for each tissue tested (Figure 5A) and, with the exception of bone marrow, there was a large difference in the scores between the first and second most enriched cell types. Not surprisingly, bone marrow was identified as being highly enriched for bone. For the lymphocyte gene lists (Figure 5B), CTen not only identified the correct lymphocyte but most often identified the correct cellular state of the lymphocyte as being the top ranked cell type. Only for the unstimulated macrophages did CTen rank the inappropriate cellular states the highest. Thus, from independent, cell-specific gene expression data, we confirmed that CTen provides clear guidance in relating gene expression data to the appropriate cell type.

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