A Network-Based Method to Assess the Statistical Significance of Mild Co-Regulation Effects
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{"title"=>"A Network-Based Method to Assess the Statistical Significance of Mild Co-Regulation Effects", "type"=>"journal", "authors"=>[{"first_name"=>"Emoke Ágnes", "last_name"=>"Horvát", "scopus_author_id"=>"55014037100"}, {"first_name"=>"Jitaovid", "last_name"=>"Da Zhang", "scopus_author_id"=>"55848107600"}, {"first_name"=>"Stefan", "last_name"=>"Uhlmann", "scopus_author_id"=>"36698453700"}, {"first_name"=>"Özgür", "last_name"=>"Sahin", "scopus_author_id"=>"16175956300"}, {"first_name"=>"Katharina Anna", "last_name"=>"Zweig", "scopus_author_id"=>"25928162000"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "pui"=>"369770329", "sgr"=>"84883642750", "doi"=>"10.1371/journal.pone.0073413", "scopus"=>"2-s2.0-84883642750", "pmid"=>"24039936"}, "id"=>"261618c8-71ca-319a-86be-4446b58bd04e", "abstract"=>"Recent development of high-throughput, multiplexing technology has initiated projects that systematically investigate interactions between two types of components in biological networks, for instance transcription factors and promoter sequences, or microRNAs (miRNAs) and mRNAs. In terms of network biology, such screening approaches primarily attempt to elucidate relations between biological components of two distinct types, which can be represented as edges between nodes in a bipartite graph. However, it is often desirable not only to determine regulatory relationships between nodes of different types, but also to understand the connection patterns of nodes of the same type. Especially interesting is the co-occurrence of two nodes of the same type, i.e., the number of their common neighbours, which current high-throughput screening analysis fails to address. The co-occurrence gives the number of circumstances under which both of the biological components are influenced in the same way. Here we present SICORE, a novel network-based method to detect pairs of nodes with a statistically significant co-occurrence. We first show the stability of the proposed method on artificial data sets: when randomly adding and deleting observations we obtain reliable results even with noise exceeding the expected level in large-scale experiments. Subsequently, we illustrate the viability of the method based on the analysis of a proteomic screening data set to reveal regulatory patterns of human microRNAs targeting proteins in the EGFR-driven cell cycle signalling system. Since statistically significant co-occurrence may indicate functional synergy and the mechanisms underlying canalization, and thus hold promise in drug target identification and therapeutic development, we provide a platform-independent implementation of SICORE with a graphical user interface as a novel tool in the arsenal of high-throughput screening analysis.", "link"=>"http://www.mendeley.com/research/networkbased-method-assess-statistical-significance-mild-coregulation-effects", "reader_count"=>16, "reader_count_by_academic_status"=>{"Researcher"=>4, "Student > Ph. D. Student"=>7, "Student > Master"=>2, "Other"=>1, "Student > Bachelor"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Researcher"=>4, "Student > Ph. D. Student"=>7, "Student > Master"=>2, "Other"=>1, "Student > Bachelor"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Biochemistry, Genetics and Molecular Biology"=>1, "Agricultural and Biological Sciences"=>7, "Medicine and Dentistry"=>2, "Business, Management and Accounting"=>1, "Physics and Astronomy"=>1, "Chemistry"=>1, "Computer Science"=>3}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Chemistry"=>{"Chemistry"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Computer Science"=>{"Computer Science"=>3}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}}, "reader_count_by_country"=>{"Netherlands"=>1, "Denmark"=>1, "Germany"=>1, "India"=>1}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1198780"], "description"=>"<p>(A) Exemplary array depicting the normalized -scores of the change in expression level for proteins , , and when cells are transfected with miRNAs , , and . The -scores are specified as white labels. (B) The corresponding bipartite graph where -scores are represented by weighted edges; the weights are shown as labels on the edges. (C) After applying a threshold to the weights, only some relationships are retained. In this case equals , corresponding to a -value of . Edges with a positive weight (up-regulation) are shown in red, edges with a negative weight (down-regulation) in green. (D) Protein co-regulation graph based on the co-regulation patterns as described in the text. Colours denote the co-regulation pattern: the red edge denotes co-up-regulation, the green edge denotes co-down-regulation; the blue, directed edge from to indicates that is down-regulated while is up-regulated by the same miRNA.</p>", "links"=>[], "tags"=>["normalized", "bipartite", "graph", "illustrating", "co-regulation", "patterns"], "article_id"=>795123, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Emöke-Ágnes Horvát", "Jitao David Zhang", "Stefan Uhlmann", "Özgür Sahin", "Katharina Anna Zweig"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0073413.g001", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Converting_the_normalized_z_score_array_into_a_bipartite_graph_and_illustrating_the_co_regulation_patterns_of_interest_/795123", "title"=>"Converting the normalized <i>z</i>-score array into a bipartite graph and illustrating the co-regulation patterns of interest.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-09-09 03:11:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1198781"], "description"=>"<p>(A) Defining the initial bipartite graph, (B) counting the observed number of co-regulations, (C) simulating the set of random bipartite graphs which define the expected number of co-regulations, (D) building the protein co-regulation graph where the weight of the edges indicates the -value assigned to the co-regulation of a given protein pair, (E) considering each co-regulation with a -value smaller or equal than a threshold (e.g., ) statistically significant.</p>", "links"=>[], "tags"=>["performed", "sicore"], "article_id"=>795124, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Emöke-Ágnes Horvát", "Jitao David Zhang", "Stefan Uhlmann", "Özgür Sahin", "Katharina Anna Zweig"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0073413.g002", "stats"=>{"downloads"=>2, "page_views"=>49, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Steps_performed_by_the_SICORE_algorithm_/795124", "title"=>"Steps performed by the SICORE algorithm.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-09-09 03:11:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1198782"], "description"=>"<p>Shown are the degree distributions of proteins (top panel) and miRNAs (bottom panel) at thresholds which correspond to -values of , and .</p>", "links"=>[], "tags"=>["distributions", "bipartite"], "article_id"=>795125, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Emöke-Ágnes Horvát", "Jitao David Zhang", "Stefan Uhlmann", "Özgür Sahin", "Katharina Anna Zweig"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0073413.g003", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Degree_distributions_of_the_bipartite_biological_data_/795125", "title"=>"Degree distributions of the bipartite biological data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-09-09 03:11:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1198783"], "description"=>"<p>The weighted scatterplot shows the number of miRNAs for each combination of down- and up-regulation degrees.</p>", "links"=>[], "tags"=>["down-regulation"], "article_id"=>795126, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Emöke-Ágnes Horvát", "Jitao David Zhang", "Stefan Uhlmann", "Özgür Sahin", "Katharina Anna Zweig"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0073413.g004", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Up_and_down_regulation_effect_of_miRNAs_/795126", "title"=>"Up- and down-regulation effect of miRNAs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-09-09 03:11:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1198784"], "description"=>"<p>(A) Sketch of one module of an artificial graph. The degree of artificial proteins/miRNAs is proportional to size of circles/squares. (B) Decision tree illustrating the principle behind the construction of the gold standard.</p>", "links"=>[], "tags"=>[], "article_id"=>795127, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Emöke-Ágnes Horvát", "Jitao David Zhang", "Stefan Uhlmann", "Özgür Sahin", "Katharina Anna Zweig"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0073413.g005", "stats"=>{"downloads"=>4, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Structure_of_the_artificial_data_/795127", "title"=>"Structure of the artificial data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-09-09 03:11:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1198785"], "description"=>"<p>Results are shown for eliminated and added edges when projecting onto the artificial protein and the artificial miRNA side. Red data points represent the performance of predicting co-up-regulation, green data points refer to co-down-regulation, and blue ones to antagonistic co-regulation.</p>", "links"=>[], "tags"=>["evaluating", "sicore", "algorithm", "sets"], "article_id"=>795128, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Emöke-Ágnes Horvát", "Jitao David Zhang", "Stefan Uhlmann", "Özgür Sahin", "Katharina Anna Zweig"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0073413.g006", "stats"=>{"downloads"=>2, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_F_score_and_PPV_evaluating_the_performance_of_the_SICORE_algorithm_on_artificial_data_sets_for_increasing_noise_levels_/795128", "title"=>"<i>F</i>-score and PPV evaluating the performance of the SICORE algorithm on artificial data sets for increasing noise levels .", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-09-09 03:11:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1198786"], "description"=>"<p>Shown are four graph topological measures against the thresholds for the -values of the projection. The projections onto the miRNA side are constructed from the bipartite graphs with thresholds corresponding to a -value of , and .</p>", "links"=>[], "tags"=>["thresholds"], "article_id"=>795129, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Emöke-Ágnes Horvát", "Jitao David Zhang", "Stefan Uhlmann", "Özgür Sahin", "Katharina Anna Zweig"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0073413.g007", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Deducing_meaningful_significance_thresholds_from_structural_measures_/795129", "title"=>"Deducing meaningful significance thresholds from structural measures.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-09-09 03:11:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1198787"], "description"=>"<p>Each square represents a down-regulation in the bipartite graph. (A) Shown are the groups with one exclusive protein target (section I), with to targets (section II, coloured regulations), and with multiple targets (section III). The names of all shown miRNAs and their protein targets are listed in the order of their appearance in in the figure in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073413#pone.0073413.s006\" target=\"_blank\">Table S4</a>. (B) Magnification of section II containing nontrivial co-regulations. In accordance with (A), colours indicate the different SICORE groups. (C) The SICORE group containing hsa-miR-489, hsa-miR-522, hsa-miR-200c, hsa-miR-550, and hsa-miR-200b together with the co-regulating miRNAs. Co-down-regulation is shown in green, co-up-regulation in red, while antagonistic regulation is coloured blue (the miRNA at the source of the arrow significantly down-regulates and the miRNA at the head of the arrow significantly up-regulates).</p>", "links"=>[], "tags"=>["groups", "sicore", "algorithm", "bipartite", "graph"], "article_id"=>795130, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Emöke-Ágnes Horvát", "Jitao David Zhang", "Stefan Uhlmann", "Özgür Sahin", "Katharina Anna Zweig"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0073413.g008", "stats"=>{"downloads"=>2, "page_views"=>73, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_miRNA_groups_obtained_by_the_SICORE_algorithm_from_the_bipartite_graph_with_and_/795130", "title"=>"miRNA groups obtained by the SICORE algorithm from the bipartite graph with and .", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-09-09 03:11:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1198788"], "description"=>"<p>For analysis, we consider the seed sequences of the groups obtained at the regulation stringency threshold . The statistical significance of over-representation was assessed by a hypergeometric test. The complete list of over-represented families for all used stringency thresholds can be found in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073413#pone.0073413.s007\" target=\"_blank\">Table S5</a>.</p>", "links"=>[], "tags"=>["groups", "sicore", "algorithm", "precursor"], "article_id"=>795131, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Emöke-Ágnes Horvát", "Jitao David Zhang", "Stefan Uhlmann", "Özgür Sahin", "Katharina Anna Zweig"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0073413.t002", "stats"=>{"downloads"=>1, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_miRNA_groups_identified_by_the_SICORE_algorithm_in_which_the_precursor_families_are_significantly_over_represented_/795131", "title"=>"miRNA groups identified by the SICORE algorithm in which the precursor families are significantly over-represented.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-09-09 03:11:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1198789"], "description"=>"<p>Shown are the significance thresholds for the edges in the corresponding co-down-regulation graphs alongside the number of miRNAs and groups of size obtained at those thresholds.</p>", "links"=>[], "tags"=>["co-down-regulation", "projections", "bipartite", "graphs"], "article_id"=>795132, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Emöke-Ágnes Horvát", "Jitao David Zhang", "Stefan Uhlmann", "Özgür Sahin", "Katharina Anna Zweig"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0073413.t001", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Properties_of_the_co_down_regulation_projections_obtained_from_the_bipartite_graphs_with_different_thresholds_/795132", "title"=>"Properties of the co-down-regulation projections obtained from the bipartite graphs with different thresholds.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-09-09 03:11:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1198790", "https://ndownloader.figshare.com/files/1198791", "https://ndownloader.figshare.com/files/1198792", "https://ndownloader.figshare.com/files/1198793", "https://ndownloader.figshare.com/files/1198794", "https://ndownloader.figshare.com/files/1198795", "https://ndownloader.figshare.com/files/1198796", "https://ndownloader.figshare.com/files/1198797"], "description"=>"<div><p>Recent development of high-throughput, multiplexing technology has initiated projects that systematically investigate interactions between two types of components in biological networks, for instance transcription factors and promoter sequences, or microRNAs (miRNAs) and mRNAs. In terms of network biology, such screening approaches primarily attempt to elucidate relations between biological components of two distinct types, which can be represented as edges between nodes in a bipartite graph. However, it is often desirable not only to determine regulatory relationships between nodes of different types, but also to understand the connection patterns of nodes of the same type. Especially interesting is the co-occurrence of two nodes of the same type, i.e., the number of their common neighbours, which current high-throughput screening analysis fails to address. The co-occurrence gives the number of circumstances under which both of the biological components are influenced in the same way. Here we present SICORE, a novel network-based method to detect pairs of nodes with a statistically significant co-occurrence. We first show the stability of the proposed method on artificial data sets: when randomly adding and deleting observations we obtain reliable results even with noise exceeding the expected level in large-scale experiments. Subsequently, we illustrate the viability of the method based on the analysis of a proteomic screening data set to reveal regulatory patterns of human microRNAs targeting proteins in the EGFR-driven cell cycle signalling system. Since statistically significant co-occurrence may indicate functional synergy and the mechanisms underlying canalization, and thus hold promise in drug target identification and therapeutic development, we provide a platform-independent implementation of SICORE with a graphical user interface as a novel tool in the arsenal of high-throughput screening analysis.</p></div>", "links"=>[], "tags"=>["network-based", "co-regulation"], "article_id"=>795133, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Emöke-Ágnes Horvát", "Jitao David Zhang", "Stefan Uhlmann", "Özgür Sahin", "Katharina Anna Zweig"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0073413.s001", "https://dx.doi.org/10.1371/journal.pone.0073413.s002", "https://dx.doi.org/10.1371/journal.pone.0073413.s003", "https://dx.doi.org/10.1371/journal.pone.0073413.s004", "https://dx.doi.org/10.1371/journal.pone.0073413.s005", "https://dx.doi.org/10.1371/journal.pone.0073413.s006", "https://dx.doi.org/10.1371/journal.pone.0073413.s007", "https://dx.doi.org/10.1371/journal.pone.0073413.s008"], "stats"=>{"downloads"=>57, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_Network_Based_Method_to_Assess_the_Statistical_Significance_of_Mild_Co_Regulation_Effects_/795133", "title"=>"A Network-Based Method to Assess the Statistical Significance of Mild Co-Regulation Effects", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-09-09 03:11:57"}

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Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[269, 466, 588, 697, 800, 896, 988, 1076, 1165, 1254, 1340, 1417]}, {"subject_area"=>"/Computer and information sciences/Data visualization", "average_usage"=>[293, 478, 588, 673, 782, 884, 973, 1055, 1126, 1239, 1329, 1404, 1497]}, {"subject_area"=>"/Physical sciences/Mathematics", "average_usage"=>[259, 431, 541, 639, 727, 816, 898, 980, 1061, 1136, 1214, 1294, 1356]}]}
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