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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/

Show MeSH
Changes in cell demographics can result in gene expression. Two scenarios which result in similar gene expression changes: (A) The cell type(s) within the sample are unchanged, but, over time, inactivated cells (colored blue) become activated and express a marker gene (colored red); (B) A second cell type already actively expressing the marker gene (red colored pentagons) migrates into the sample. The change in the marker gene expression is similar in both cases but results from a different reason.
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Figure 1: Changes in cell demographics can result in gene expression. Two scenarios which result in similar gene expression changes: (A) The cell type(s) within the sample are unchanged, but, over time, inactivated cells (colored blue) become activated and express a marker gene (colored red); (B) A second cell type already actively expressing the marker gene (red colored pentagons) migrates into the sample. The change in the marker gene expression is similar in both cases but results from a different reason.

Mentions: Microarray studies quantify genome wide changes in gene expression and have a variety of applications - from tracing allele ancestry as species evolve [1] to the development of genome-based personalized medicine [2]. A major challenge in the microarray analysis of tissue collected in vivo is that often the perceived gene regulation is the result of changes in the populations of particular cell types as opposed to an actual change in transcriptional activity (see Figure 1). Particularly in situations which invoke the immune response, as the cell count of various lymphocytes change within the tissue, they bring with them their own unique quantities of RNA [3]. This leads to large changes in the copy number of RNA transcripts and can lead to the false perception of increased transcriptional activity.


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)

Changes in cell demographics can result in gene expression. Two scenarios which result in similar gene expression changes: (A) The cell type(s) within the sample are unchanged, but, over time, inactivated cells (colored blue) become activated and express a marker gene (colored red); (B) A second cell type already actively expressing the marker gene (red colored pentagons) migrates into the sample. The change in the marker gene expression is similar in both cases but results from a different reason.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Changes in cell demographics can result in gene expression. Two scenarios which result in similar gene expression changes: (A) The cell type(s) within the sample are unchanged, but, over time, inactivated cells (colored blue) become activated and express a marker gene (colored red); (B) A second cell type already actively expressing the marker gene (red colored pentagons) migrates into the sample. The change in the marker gene expression is similar in both cases but results from a different reason.
Mentions: Microarray studies quantify genome wide changes in gene expression and have a variety of applications - from tracing allele ancestry as species evolve [1] to the development of genome-based personalized medicine [2]. A major challenge in the microarray analysis of tissue collected in vivo is that often the perceived gene regulation is the result of changes in the populations of particular cell types as opposed to an actual change in transcriptional activity (see Figure 1). Particularly in situations which invoke the immune response, as the cell count of various lymphocytes change within the tissue, they bring with them their own unique quantities of RNA [3]. This leads to large changes in the copy number of RNA transcripts and can lead to the false perception of increased transcriptional activity.

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