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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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Overview of a CTen session. CTen has been designed with a user-friendly interface to allow for rapid analysis of several gene lists, simultaneously. Panel (A) illustrates the workflow between the user, CTen interface and the HECS database. (B) At the upload screen, users can copy and paste their gene lists straight from spreadsheet software (e.g., Excel) and select the appropriate parameters (species, gene identifier and the separator used if multiple lists are being uploaded). (C) The data goes through preliminary processing to ensure the gene list(s) is parsed properly and that the supplied genes are found in the HECS database. Once the user list passes preliminary processing, the enrichment of all cell types in the HECS database is calculated.
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Figure 4: Overview of a CTen session. CTen has been designed with a user-friendly interface to allow for rapid analysis of several gene lists, simultaneously. Panel (A) illustrates the workflow between the user, CTen interface and the HECS database. (B) At the upload screen, users can copy and paste their gene lists straight from spreadsheet software (e.g., Excel) and select the appropriate parameters (species, gene identifier and the separator used if multiple lists are being uploaded). (C) The data goes through preliminary processing to ensure the gene list(s) is parsed properly and that the supplied genes are found in the HECS database. Once the user list passes preliminary processing, the enrichment of all cell types in the HECS database is calculated.

Mentions: A minimal amount of preprocessing is applied to the user supplied gene list to ensure that, first, the list is properly parsed, and second, the user supplied genes are found in the HECS database. The workflow of the CTen website is shown in Figure 4A. At the upload screen (Figure 4B), users can upload a list of either gene symbols or Entrez gene IDs, and optionally upload multiple lists at once by choosing the appropriate format (the CTen webpage provides a single and multi-list example). The gene list is processed to determine the number of unique user genes found in the database and if the list does not appear to be one of the two gene identifiers stated above or the inappropriate format was selected, the website shows a parsing error screen and asks the user to ensure that the proper identifier is selected. If there are no parsing errors, CTen produces a table showing the number of unique user genes mapped in the CTen database for each uploaded list (Figure 4C). If no user genes are found in the database, CTen produces another error, "No genes found in the database" and the user is asked to reevaluate the uploaded gene list. Should CTen not detect either of these errors, the option to continue to enrichment appears and the user can complete their analysis.


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)

Overview of a CTen session. CTen has been designed with a user-friendly interface to allow for rapid analysis of several gene lists, simultaneously. Panel (A) illustrates the workflow between the user, CTen interface and the HECS database. (B) At the upload screen, users can copy and paste their gene lists straight from spreadsheet software (e.g., Excel) and select the appropriate parameters (species, gene identifier and the separator used if multiple lists are being uploaded). (C) The data goes through preliminary processing to ensure the gene list(s) is parsed properly and that the supplied genes are found in the HECS database. Once the user list passes preliminary processing, the enrichment of all cell types in the HECS database is calculated.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Overview of a CTen session. CTen has been designed with a user-friendly interface to allow for rapid analysis of several gene lists, simultaneously. Panel (A) illustrates the workflow between the user, CTen interface and the HECS database. (B) At the upload screen, users can copy and paste their gene lists straight from spreadsheet software (e.g., Excel) and select the appropriate parameters (species, gene identifier and the separator used if multiple lists are being uploaded). (C) The data goes through preliminary processing to ensure the gene list(s) is parsed properly and that the supplied genes are found in the HECS database. Once the user list passes preliminary processing, the enrichment of all cell types in the HECS database is calculated.
Mentions: A minimal amount of preprocessing is applied to the user supplied gene list to ensure that, first, the list is properly parsed, and second, the user supplied genes are found in the HECS database. The workflow of the CTen website is shown in Figure 4A. At the upload screen (Figure 4B), users can upload a list of either gene symbols or Entrez gene IDs, and optionally upload multiple lists at once by choosing the appropriate format (the CTen webpage provides a single and multi-list example). The gene list is processed to determine the number of unique user genes found in the database and if the list does not appear to be one of the two gene identifiers stated above or the inappropriate format was selected, the website shows a parsing error screen and asks the user to ensure that the proper identifier is selected. If there are no parsing errors, CTen produces a table showing the number of unique user genes mapped in the CTen database for each uploaded list (Figure 4C). If no user genes are found in the database, CTen produces another error, "No genes found in the database" and the user is asked to reevaluate the uploaded gene list. Should CTen not detect either of these errors, the option to continue to enrichment appears and the user can complete their analysis.

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