Probe Selection and Expression Index Computation of Affymetrix Exon Arrays
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{"title"=>"Probe selection and expression index computation of affymetrix exon arrays", "type"=>"journal", "authors"=>[{"first_name"=>"Yi", "last_name"=>"Xing", "scopus_author_id"=>"7201622967"}, {"first_name"=>"Karen", "last_name"=>"Kapur", "scopus_author_id"=>"57200926900"}, {"first_name"=>"Wing Hung", "last_name"=>"Wong", "scopus_author_id"=>"7403972659"}], "year"=>2006, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"34250756005", "issn"=>"19326203", "scopus"=>"2-s2.0-34250756005", "pmid"=>"17183719", "pui"=>"352571165", "isbn"=>"1932-6203 (Electronic)", "doi"=>"10.1371/journal.pone.0000088"}, "id"=>"d63aef27-aa6c-3000-ac36-cebca95d3148", "abstract"=>"BACKGROUND: There is great current interest in developing microarray platforms for measuring mRNA abundance at both gene level and exon level. The Affymetrix Exon Array is a new high-density gene expression microarray platform, with over six million probes targeting all annotated and predicted exons in a genome. An important question for the analysis of exon array data is how to compute overall gene expression indexes. Because of the complexity of the design of exon array probes, this problem is different in nature from summarizing gene-level expression from traditional 3' expression arrays.\\n\\nMETHODOLOGY/PRINCIPAL FINDINGS: In this manuscript, we use exon array data from 11 human tissues to study methods for computing gene-level expression. We showed that for most genes there is a subset of exon array probes having highly correlated intensities across multiple samples. We suggest that these probes could be used as reliable indicators of overall gene expression levels. We developed a probe selection algorithm to select such a subset of highly correlated probes for each gene, and computed gene expression indexes using the selected probes.\\n\\nCONCLUSIONS/SIGNIFICANCE: Our results demonstrate that probe selection improves gene expression estimates from exon arrays. The selected probes can be used in future analyses of other exon array datasets to compute gene expression indexes.", "link"=>"http://www.mendeley.com/research/probe-selection-expression-index-computation-affymetrix-exon-arrays", "reader_count"=>56, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>7, "Researcher"=>27, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>10, "Student > Postgraduate"=>1, "Student > Master"=>4, "Other"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>7, "Researcher"=>27, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>10, "Student > Postgraduate"=>1, "Student > Master"=>4, "Other"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Unspecified"=>2, "Biochemistry, Genetics and Molecular Biology"=>6, "Mathematics"=>2, "Agricultural and Biological Sciences"=>34, "Medicine and Dentistry"=>7, "Computer Science"=>3, "Decision Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>7}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>34}, "Computer Science"=>{"Computer Science"=>3}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>6}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>2}}, "reader_count_by_country"=>{"United States"=>3, "Italy"=>1, "United Kingdom"=>2, "Germany"=>3}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/954970"], "description"=>"<div><p>(<b>A</b>) Exon-intron structure of a gene.</p>\n <p>Black boxes represent exons. Gray boxes represent introns. Introns are not drawn to scale.</p>\n <p>(<b>B</b>) Probe design of exon arrays.</p>\n <p>Exon arrays have four probes targeting each exon of the gene.</p>\n <p>(<b>C</b>) Probe design of 3′ expression arrays.</p>\n <p>Probes on 3′ expression arrays target 3′ end of the mRNA sequence.</p></div>", "links"=>[], "tags"=>["exon"], "article_id"=>625264, "categories"=>["Genetics"], "users"=>["Yi Xing", "Karen Kapur", "Wing Hung Wong"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000088.g001", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Probe_design_of_exon_arrays_/625264", "title"=>"Probe design of exon arrays.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 13:02:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/955264"], "description"=>"<div><p>(<b>A</b>) 438 transcript clusters were defined as overexpressed in liver relative to muscle, based on 3′ expression array data (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000088#s2\" target=\"_blank\">Materials and Methods</a>).</p>\n <p>Using gene expression indexes computed from all core probes or the selected core probes, we calculated the average gene expression fold change in three liver samples over three muscle samples.</p>\n <p>The X-axis shows the fold change using all core probes, and the Y-axis shows the fold change using selected core probes.</p>\n <p>The red line indicates the 45-degree line (Y = X).</p>\n <p>(<b>B</b>) A magnification of (A) when the fold change computed from all core probes was less than 6.</p>\n <p>(<b>C</b>) 500 transcript clusters were defined as overexpressed in muscle relative to liver, based on 3′ expression array data (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000088#s2\" target=\"_blank\">Materials and Methods</a>).</p>\n <p>Using gene expression indexes computed from all core probes or the selected core probes, we calculated the average gene expression fold change in three muscle samples over three liver samples.</p>\n <p>The X-axis shows the fold change using all core probes, and the Y-axis shows the fold change using selected core probes.</p>\n <p>The red line indicates the 45-degree line (Y = X).</p>\n <p>(<b>D</b>) A magnification of (C) when the fold change computed from all core probes was less than 6.</p></div>", "links"=>[], "tags"=>["probe", "genes", "differentially"], "article_id"=>625556, "categories"=>["Genetics"], "users"=>["Yi Xing", "Karen Kapur", "Wing Hung Wong"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000088.g005", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Effects_of_probe_selection_on_genes_differentially_expressed_between_liver_and_muscle_/625556", "title"=>"Effects of probe selection on genes differentially expressed between liver and muscle.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 13:04:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/955116"], "description"=>"<div><p>Core probes of CD44 are clustered by average linkage hierarchical clustering based on their intensities in 11 tissues (a total of 33 samples).</p>\n <p>The distance metric is (1-Pearson correlation coefficient).</p>\n <p>A total of 44 core probes are selected when we cut the clustering dendrogram at <i>h</i> = 0.1 (indicated by the dashed horizontal line).</p></div>", "links"=>[], "tags"=>["clustering", "probe", "intensities", "cd44"], "article_id"=>625413, "categories"=>["Genetics"], "users"=>["Yi Xing", "Karen Kapur", "Wing Hung Wong"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000088.g003", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Hierarchical_clustering_of_probe_intensities_of_CD44_core_probes_/625413", "title"=>"Hierarchical clustering of probe intensities of CD44 core probes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 13:03:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/470128", "https://ndownloader.figshare.com/files/470161", "https://ndownloader.figshare.com/files/470198"], "description"=>"<div><h3>Background</h3><p>There is great current interest in developing microarray platforms for measuring mRNA abundance at both gene level and exon level. The Affymetrix Exon Array is a new high-density gene expression microarray platform, with over six million probes targeting all annotated and predicted exons in a genome. An important question for the analysis of exon array data is how to compute overall gene expression indexes. Because of the complexity of the design of exon array probes, this problem is different in nature from summarizing gene-level expression from traditional 3′ expression arrays.</p> <h3>Methodology/Principal Findings</h3><p>In this manuscript, we use exon array data from 11 human tissues to study methods for computing gene-level expression. We showed that for most genes there is a subset of exon array probes having highly correlated intensities across multiple samples. We suggest that these probes could be used as reliable indicators of overall gene expression levels. We developed a probe selection algorithm to select such a subset of highly correlated probes for each gene, and computed gene expression indexes using the selected probes.</p> <h3>Conclusions/Significance</h3><p>Our results demonstrate that probe selection improves gene expression estimates from exon arrays. The selected probes can be used in future analyses of other exon array datasets to compute gene expression indexes.</p> </div>", "links"=>[], "tags"=>["probe", "computation", "affymetrix", "exon", "arrays"], "article_id"=>152660, "categories"=>["Genetics"], "users"=>["Yi Xing", "Karen Kapur", "Wing Hung Wong"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0000088.s001", "https://dx.doi.org/10.1371/journal.pone.0000088.s002", "https://dx.doi.org/10.1371/journal.pone.0000088.s003"], "stats"=>{"downloads"=>5, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Probe_Selection_and_Expression_Index_Computation_of_Affymetrix_Exon_Arrays/152660", "title"=>"Probe Selection and Expression Index Computation of Affymetrix Exon Arrays", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2006-12-20 00:44:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/955191"], "description"=>"<div><p>(<b>A</b>) Gene expression indexes of transcript cluster 2899110 (HFE) are computed using all probes (black circles), all core probes (red triangles) and selected probes (green rectangles).</p>\n <p>Probe selection increases the gene expression indexes computed for all the samples.</p>\n <p>The relative expression levels in different tissues remain unaltered.</p>\n <p>(<b>B</b>) Gene expression indexes of transcript cluster 3833500 (SPTBN4, a gene known to be overexpressed in brain and cerebellum).</p>\n <p>Probe selection strengthens the pattern of overexpression in cerebellum (sample #4, #5 and #6).</p></div>", "links"=>[], "tags"=>["indexes", "computed"], "article_id"=>625481, "categories"=>["Genetics"], "users"=>["Yi Xing", "Karen Kapur", "Wing Hung Wong"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000088.g004", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Gene_expression_indexes_computed_using_all_probes_all_core_probes_and_selected_core_probes_/625481", "title"=>"Gene expression indexes computed using all probes, all core probes, and selected core probes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 13:04:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/955340"], "description"=>"<div><p>We grouped 17056 transcript clusters into ten distinct bins according to the height where we cut the clustering dendrogram (<i>h_final</i>).</p>\n <p>For each transcript cluster, we calculated its peak gene expression index in the 11 tissues using selected probes.</p>\n <p>Then we took an average for each bin.</p>\n <p>The values of tree cutoff (X-axis) were negatively correlated with average peak gene expression levels in the 11 tissues (Y-axis).</p></div>", "links"=>[], "tags"=>["probe"], "article_id"=>625630, "categories"=>["Genetics"], "users"=>["Yi Xing", "Karen Kapur", "Wing Hung Wong"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000088.g006", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_impact_of_gene_expression_levels_on_probe_selection_/625630", "title"=>"The impact of gene expression levels on probe selection.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 13:05:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/955037"], "description"=>"<div><p>(<b>A</b>) Heatmap visualization of probe intensities of HLA-DMB (transcript cluster 2950263).</p>\n <p>Each cell of the heatmap shows the correlation of two probes in 11 tissues.</p>\n <p>The top color bar indicates the probe type.</p>\n <p>Core probes are colored in red.</p>\n <p>Extended probes are colored in blue.</p>\n <p>The signal intensities of core probes usually have a high correlation (the top right corner of the heatmap).</p>\n <p>(<b>B</b>) Heatmap visualization of probe intensities of core probes in CD44 (transcript cluster 3326635).</p>\n <p>Probes targeting the 5′ and 3′ regions (constitutive exons) of CD44 show highly correlated signals in 11 tissues (the top right corner of the heatmap).</p>\n <p>(<b>C</b>) Heatmap visualization of probe intensities of core probes in CD44 (transcript cluster 3326635).</p>\n <p>Probes are ordered from top to bottom based on their genomic coordinates (5′ to 3′).</p></div>", "links"=>[], "tags"=>["visualization", "exon", "pairwise", "probe"], "article_id"=>625333, "categories"=>["Genetics"], "users"=>["Yi Xing", "Karen Kapur", "Wing Hung Wong"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000088.g002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Heatmap_visualization_of_exon_array_pairwise_probe_correlations_/625333", "title"=>"Heatmap visualization of exon array pairwise probe correlations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 13:02:56"}

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Relative Metric

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