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

HECS genes shared between different cell types. A heatmap of the percentage of HECS genes shared between any two cell types in the mouse (upper triangle) and human (lower triangle) databases. Ticks adjacent to the heatmap denote cell types belonging to the nervous (red), immune (blue), or reproductive systems (purple).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: HECS genes shared between different cell types. A heatmap of the percentage of HECS genes shared between any two cell types in the mouse (upper triangle) and human (lower triangle) databases. Ticks adjacent to the heatmap denote cell types belonging to the nervous (red), immune (blue), or reproductive systems (purple).

Mentions: We also determined the percentage of HECS genes shared by any two cell types within the human and mouse databases. As seen in Figure 3, the vast majority of cell types have highly distinct sets of HECS genes, with two mouse cell types sharing an average of only 16.1% HECS genes, while human cell types share an average of 11.6%. The two groups of cell types which share the most HECS genes in both mouse and human datasets belong to the nervous and reproductive systems (denoted by red and purple ticks beside the heatmap in Figure 3). Immune cells in different cell states also share the majority of their HECS genes (e.g., human CD8+ T-cells and CD4+ T-cells share 90.4% of their HECS genes) but the number of HECS genes shared between two different immune cells (e.g., B-cells versus T-cells) is generally less than 50% ( Additional file 4 provides a more detailed heatmap). In all, the strategy behind the development of the HECS database ensures that HECS genes are limited to a few cell types - characterizing a signature for each tissue. Therefore, the HECS database provides a powerful means of identifying cell/tissue specific enrichment in user gene lists.


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)

HECS genes shared between different cell types. A heatmap of the percentage of HECS genes shared between any two cell types in the mouse (upper triangle) and human (lower triangle) databases. Ticks adjacent to the heatmap denote cell types belonging to the nervous (red), immune (blue), or reproductive systems (purple).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: HECS genes shared between different cell types. A heatmap of the percentage of HECS genes shared between any two cell types in the mouse (upper triangle) and human (lower triangle) databases. Ticks adjacent to the heatmap denote cell types belonging to the nervous (red), immune (blue), or reproductive systems (purple).
Mentions: We also determined the percentage of HECS genes shared by any two cell types within the human and mouse databases. As seen in Figure 3, the vast majority of cell types have highly distinct sets of HECS genes, with two mouse cell types sharing an average of only 16.1% HECS genes, while human cell types share an average of 11.6%. The two groups of cell types which share the most HECS genes in both mouse and human datasets belong to the nervous and reproductive systems (denoted by red and purple ticks beside the heatmap in Figure 3). Immune cells in different cell states also share the majority of their HECS genes (e.g., human CD8+ T-cells and CD4+ T-cells share 90.4% of their HECS genes) but the number of HECS genes shared between two different immune cells (e.g., B-cells versus T-cells) is generally less than 50% ( Additional file 4 provides a more detailed heatmap). In all, the strategy behind the development of the HECS database ensures that HECS genes are limited to a few cell types - characterizing a signature for each tissue. Therefore, the HECS database provides a powerful means of identifying cell/tissue specific enrichment in user gene lists.

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