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Keywords {🔍}

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Topics {✒️}

yoshihiro kawaoka & hiroaki kitano b-cells versus t-cells cell-specific gene expression article download pdf human protein-encoding transcriptomes innate host responses high-density oligonucleotide arrays web-based platforms human single-nucleotide polymorphisms relate user-generated sets highly expressed cell-specific web-based analytical tool t-cell related cluster biological module-centric algorithm immune related cell-types heterogeneous microarray data open software development human cd8+ t-cells influenza virus-infected macaques influenza-infected lung tissue privacy choices/manage cookies bone marrow derived gene expression data conserved expression patterns differential expression testing entrez gene ids vivo microarray data microarray data unpublished gene expression due systems biology institute differential expression criteria put gene expression gene expression measurements linking gene expression separating gene expression gene expression barcode stringent expression requirement gene expression analysis gene clustering studies gene network inference web-based platform authors’ original file specific cell types gene expression clustering human datasets belong gene lists differ biomed central full size image similar gene expression marker gene expression

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WebPage:
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         headline:CTen: a web-based platform for identifying enriched cell types from heterogeneous microarray data
         description: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. CTen (c ell t ype en richment) 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. 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/
         datePublished:2012-09-06T00:00:00Z
         dateModified:2012-09-06T00:00:00Z
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            Cell type enrichment
            Microarray data
            Deconvolution
            Influenza
            Systems immunology
            Life Sciences
            general
            Microarrays
            Proteomics
            Animal Genetics and Genomics
            Microbial Genetics and Genomics
            Plant Genetics and Genomics
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      headline:CTen: a web-based platform for identifying enriched cell types from heterogeneous microarray data
      description: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. CTen (c ell t ype en richment) 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. 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/
      datePublished:2012-09-06T00:00:00Z
      dateModified:2012-09-06T00:00:00Z
      pageStart:1
      pageEnd:11
      license:http://creativecommons.org/licenses/by/2.0
      sameAs:https://doi.org/10.1186/1471-2164-13-460
      keywords:
         Cell type enrichment
         Microarray data
         Deconvolution
         Influenza
         Systems immunology
         Life Sciences
         general
         Microarrays
         Proteomics
         Animal Genetics and Genomics
         Microbial Genetics and Genomics
         Plant Genetics and Genomics
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      author:
            name:Jason E Shoemaker
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            name:Tiago JS Lopes
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                  address:
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            name:JST ERATO KAWAOKA Infection-induced Host Responses Project
            address:
               name:JST ERATO KAWAOKA Infection-induced Host Responses Project, Tokyo, Japan
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      name:Hiroaki Kitano
      affiliation:
            name:JST ERATO KAWAOKA Infection-induced Host Responses Project
            address:
               name:JST ERATO KAWAOKA Infection-induced Host Responses Project, Tokyo, Japan
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            address:
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               type:PostalAddress
            type:Organization
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      name:JST ERATO KAWAOKA Infection-induced Host Responses Project, Tokyo, Japan
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      name:JST ERATO KAWAOKA Infection-induced Host Responses Project, Tokyo, Japan
      name:Influenza Research Institute, Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, USA
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      name:JST ERATO KAWAOKA Infection-induced Host Responses Project, Tokyo, Japan
      name:The Systems Biology Institute, Tokyo, Japan
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