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{"title"=>"A Graphical Modelling Approach to the Dissection of Highly Correlated Transcription Factor Binding Site Profiles", "type"=>"journal", "authors"=>[{"first_name"=>"Robert", "last_name"=>"Stojnic", "scopus_author_id"=>"55513972300"}, {"first_name"=>"Audrey Qiuyan", "last_name"=>"Fu", "scopus_author_id"=>"55109713100"}, {"first_name"=>"Boris", "last_name"=>"Adryan", "scopus_author_id"=>"15057657300"}], "year"=>2012, "source"=>"PLoS Computational Biology", "identifiers"=>{"pmid"=>"23144600", "issn"=>"1553734X", "sgr"=>"84870703160", "pui"=>"366216195", "scopus"=>"2-s2.0-84870703160", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "doi"=>"10.1371/journal.pcbi.1002725"}, "id"=>"c5ccb464-257d-3a26-955d-2d986685de92", "abstract"=>"Inferring the combinatorial regulatory code of transcription factors (TFs) from genome-wide TF binding profiles is challenging. A major reason is that TF binding profiles significantly overlap and are therefore highly correlated. Clustered occurrence of multiple TFs at genomic sites may arise from chromatin accessibility and local cooperation between TFs, or binding sites may simply appear clustered if the profiles are generated from diverse cell populations. Overlaps in TF binding profiles may also result from measurements taken at closely related time intervals. It is thus of great interest to distinguish TFs that directly regulate gene expression from those that are indirectly associated with gene expression. Graphical models, in particular Bayesian networks, provide a powerful mathematical framework to infer different types of dependencies. However, existing methods do not perform well when the features (here: TF binding profiles) are highly correlated, when their association with the biological outcome is weak, and when the sample size is small. Here, we develop a novel computational method, the Neighbourhood Consistent PC (NCPC) algorithms, which deal with these scenarios much more effectively than existing methods do. We further present a novel graphical representation, the Direct Dependence Graph (DDGraph), to better display the complex interactions among variables. NCPC and DDGraph can also be applied to other problems involving highly correlated biological features. Both methods are implemented in the R package ddgraph, available as part of Bioconductor (http://bioconductor.org/packages/2.11/bioc/html/ddgraph.html). Applied to real data, our method identified TFs that specify different classes of cis-regulatory modules (CRMs) in Drosophila mesoderm differentiation. Our analysis also found depletion of the early transcription factor Twist binding at the CRMs regulating expression in visceral and somatic muscle cells at later stages, which suggests a CRM-specific repression mechanism that so far has not been characterised for this class of mesodermal CRMs.", "link"=>"http://www.mendeley.com/research/graphical-modelling-approach-dissection-highly-correlated-transcription-factor-binding-site-profiles", "reader_count"=>54, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Researcher"=>23, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>14, "Student > Postgraduate"=>2, "Student > Master"=>7, "Other"=>1, "Student > Bachelor"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Researcher"=>23, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>14, "Student > Postgraduate"=>2, "Student > Master"=>7, "Other"=>1, "Student > Bachelor"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>6, "Agricultural and Biological Sciences"=>38, "Veterinary Science and Veterinary Medicine"=>1, "Physics and Astronomy"=>2, "Computer Science"=>5, "Decision Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>38}, "Computer Science"=>{"Computer Science"=>5}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>6}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>1}}, "reader_count_by_country"=>{"Canada"=>1, "United States"=>3, "Norway"=>1, "United Kingdom"=>2, "Italy"=>1, "France"=>2, "Spain"=>1}, "group_count"=>2}

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• {"type"=>"COMMENT", "annotationUri"=>"info:doi/10.1371/annotation/e9869bef-8584-4f6d-91be-c24e069859eb", "title"=>"Update to the link for the R package ddgraph", "body"=>"The R package ddgraph is available as part of Bioconductor. The latest release version can be accessed via the following link: http://www.bioconductor.org/packages/release/bioc/html/ddgraph.html.\n\nPlease note that this update also applies to the links in the penultimate paragraph of the Introduction and the final paragraph of the Discussion.\n", "highlightedText"=>"''http://bioconductor.org/packages/2.11/bioc/html/ddgraph.html)''\nhttp://ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002725#article1.front1.article-meta1.abstract1.p1", "isRemoved"=>false, "created"=>"2012-12-04T12:23:29Z", "lastModified"=>"2012-12-04T12:23:29Z", "creator"=>{"userId"=>"40493"}, "competingInterestStatement"=>{"creatorWasPrompted"=>true, "hasCompetingInterests"=>false}, "parentArticle"=>{"doi"=>"info:doi/10.1371/journal.pcbi.1002725", "state"=>"published", "journals"=>{"PLoSCompBiol"=>{"journalKey"=>"PLoSCompBiol", "eIssn"=>"1553-7358", "title"=>"PLOS Computational Biology"}}}, "replyTreeSize"=>0, "mostRecentActivity"=>"2012-12-04T12:23:29Z", "replies"=>[]}

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• {"id"=>"266662729299922944", "text"=>"Be the first to read the new @BorisAdryan lab paper on analysis of TF binding that came out 4 minutes ago @PLOSCompBiol http://t.co/f5h9P7se", "created_at"=>"2012-11-08T22:05:23Z", "user"=>"PavelTomancak", "user_name"=>"Pavel Tomancak", "user_profile_image"=>"http://a0.twimg.com/profile_images/2612422002/2jn1eaf2u5v2ampkzd0e_normal.png"}
• {"id"=>"266663776139497472", "text"=>"A Graphical Modelling Approach to the Dissection of Highly Correlated Transcription Factor Binding Site Profiles (http://t.co/M3nciVQ5)", "created_at"=>"2012-11-08T22:09:33Z", "user"=>"caseybergman", "user_name"=>"Casey Bergman", "user_profile_image"=>"http://a0.twimg.com/profile_images/2092288245/Untitled-1_normal.png"}
• {"id"=>"266663915386195968", "text"=>"RT @PavelTomancak: Be the first to read the new @BorisAdryan lab paper on analysis of TF binding that came out 4 minutes ago @PLOSCompBi ...", "created_at"=>"2012-11-08T22:10:06Z", "user"=>"FlyJedi", "user_name"=>"fly-jedi.org", "user_profile_image"=>"http://a0.twimg.com/profile_images/1666275297/yoda_normal.png"}
• {"id"=>"266674875962363908", "text"=>"RT @PavelTomancak: Be the first to read the new @BorisAdryan lab paper on analysis of TF binding that came out 4 minutes ago @PLOSCompBi ...", "created_at"=>"2012-11-08T22:53:39Z", "user"=>"Jelena_Aleksic1", "user_name"=>"Jelena Aleksic", "user_profile_image"=>"http://a0.twimg.com/profile_images/1880840640/JA_normal.jpg"}
• {"id"=>"266676703831670786", "text"=>"Congrats to Robert Stojnic! New @BorisAdryan lab paper on analysis of TF binding @PLOSCompBiol http://t.co/OQ4AfD5C via @PavelTomancak", "created_at"=>"2012-11-08T23:00:55Z", "user"=>"SarahHCarl", "user_name"=>"Sarah Carl", "user_profile_image"=>"http://a0.twimg.com/profile_images/1878888153/IMG_0134_normal.JPG"}
• {"id"=>"266795437841842176", "text"=>"The opus magnum of my first PhD student Robert Stojnic: http://t.co/QcXjZFTK on #TF #combinatorics, including an easy to use #R package.", "created_at"=>"2012-11-09T06:52:43Z", "user"=>"BorisAdryan", "user_name"=>"Boris Adryan", "user_profile_image"=>"http://a0.twimg.com/profile_images/2744773916/1738bf480dde7fb2ab0ad7312b87c63e_normal.png"}
• {"id"=>"266826423816302592", "text"=>"RT @BorisAdryan: The opus magnum of my first PhD student Robert Stojnic: http://t.co/QcXjZFTK on #TF #combinatorics, including an easy t ...", "created_at"=>"2012-11-09T08:55:51Z", "user"=>"Drosophilic", "user_name"=>"Pete", "user_profile_image"=>"http://a0.twimg.com/profile_images/1650655282/somaticmuscle_normal.jpg"}
• {"id"=>"267677475444764672", "text"=>"#cb #sy ~App to other areas w/ HiCo variables:Graphical modelling to dissect highly correlated TF binding site profiles http://t.co/RToFB82t", "created_at"=>"2012-11-11T17:17:38Z", "user"=>"BiomedRevon", "user_name"=>"Mirko ", "user_profile_image"=>"http://a0.twimg.com/profile_images/1518893315/Arcimboldo_Librarian_Stokholm_normal.jpg"}
• {"id"=>"269093961296130049", "text"=>"A Graphical Modelling Approach to the Dissection of Highly Correlated Transcription Factor Binding Site Profiles http://t.co/GRhgvOnw", "created_at"=>"2012-11-15T15:06:14Z", "user"=>"razZ0r", "user_name"=>"razor", "user_profile_image"=>"http://a0.twimg.com/profile_images/1369601363/cosplay_g02_normal.jpg"}
• {"id"=>"450596383628996608", "text"=>"I recommend my ‘big data’ minded followers a look at http://t.co/vVm8vmQ7ZP This is the sort of analytics we will soon be offering for #IoT.", "created_at"=>"2014-03-31T11:32:04Z", "user"=>"BorisAdryan", "user_name"=>"Boris Adryan", "user_profile_image"=>"http://pbs.twimg.com/profile_images/2744773916/1738bf480dde7fb2ab0ad7312b87c63e_normal.png"}
• {"id"=>"784001374556225536", "text"=>"Finally @M_Feindt talks about causality structure. Similar to my previous work: https://t.co/rX4AHNkDbv, now available to our customers.", "created_at"=>"2016-10-06T12:04:28Z", "user"=>"BorisAdryan", "user_name"=>"Dr. Boris Adryan", "user_profile_image"=>"http://pbs.twimg.com/profile_images/743497010171383809/KMJsIGKs_normal.jpg"}

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/544701"], "description"=>"<p>(<b>A</b>) The causal neighbourhood of the target variable T consists of variables X1 and X2, while T's Markov blanket consists of X1, X2, X4 (in ovals). The remaining variables X3 and X5 have indirect dependence (in rectangles). The DDGraph (left) and the DAG (right) represent the same conditional dependencies. The causal neighbourhood/the Markov blanket and the variable in indirect dependence are distinguishable by the variable shapes in the DDGraph, but have to be inferred in the DAG by following the edges. (<b>B</b>) joint dependency patterns representable in the DDGraph (left) cannot be represented by DAGs (right). The DAG shown here represents the conditional independencies between X1 (or X2) and T given X2 (or X1), but it does not represent the marginal dependency between X1 (or X2) and T. Neither this DAG or any other DAG can represent the entire joint dependency pattern.</p>", "links"=>[], "tags"=>["ddgraphs"], "article_id"=>215202, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Genetics"], "users"=>["Robert Stojnic", "Audrey Qiuyan Fu", "Boris Adryan"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002725.g002", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_DDGraphs_and_DAGs_/215202", "title"=>"Comparison of DDGraphs and DAGs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-08 01:26:42"}
• {"files"=>["https://ndownloader.figshare.com/files/544870"], "description"=>"<p>Each cell shows the mean proportion of correct predictions (with 95% confidence intervals) averaged over 1000 data sets generated in each case. Highest prediction proportions accounting for variation in the data (pairwise T-tests with a cut-off of 0.001 for the P values) are shown in bold. See <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002725#s4\" target=\"_blank\">Materials and Methods</a> for the generation of the synthetic data and for the calculation of the correct prediction proportion.</p>", "links"=>[], "tags"=>["predictions"], "article_id"=>215365, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Genetics"], "users"=>["Robert Stojnic", "Audrey Qiuyan Fu", "Boris Adryan"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002725.g004", "stats"=>{"downloads"=>10, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Proportion_of_correct_predictions_for_the_8220_Time_8221_scenario_/215365", "title"=>"Proportion of correct predictions for the “Time” scenario.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-08 01:29:25"}
• {"files"=>["https://ndownloader.figshare.com/files/544807"], "description"=>"<p>While the synthetic data were generated for a network of 15 explanatory variables, only variables X1 and X2 have direct dependence with the target variable T, and therefore constitute the causal neighborhood of <i>T</i>. Variable X3 is included as the confounding variable. (<b>A</b>) The “Time” scenario in which X1, X2 and X3 correspond to three time points with stronger correlation between X1 and X2 and between X2 and X3 than between X1 and X3. (<b>B</b>) The “Hidden” scenario in which X1, X2 and X3 are correlated due to a common cause <i>H</i> in the network. This common cause is used in data generation, but is not available to algorithms.</p>", "links"=>[], "tags"=>["scenarios", "generating", "synthetic", "correlated"], "article_id"=>215305, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Genetics"], "users"=>["Robert Stojnic", "Audrey Qiuyan Fu", "Boris Adryan"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002725.g003", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Two_scenarios_for_generating_the_synthetic_data_with_correlated_variables_/215305", "title"=>"Two scenarios for generating the synthetic data with correlated variables.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-08 01:28:25"}
• {"files"=>["https://ndownloader.figshare.com/files/544617"], "description"=>"<p>The vocabulary consists of five types of nodes and two types of edges. For the edges, directed edges ending with dots indicate conditional independences between <i>X_k</i> and the target variable <i>T</i> given <i>X_i</i>. Undirected edges indicate dependencies, which involve <i>T</i> in different ways, and for conditional independencies between <i>X_i</i> and <i>X_j</i> given <i>T</i>. Consider a case of non-faithful distribution where <i>T</i> is an XOR function of <i>X</i>1 and <i>X</i>2 with carefully set parameters so that from data it looks like <i>X</i>1 and <i>X</i>2 are marginally independent of <i>T</i>. In this case, <i>X</i>1 and <i>X</i>2 would be conditionally dependent when conditioning on each other. This distribution would be represented as two dotted nodes with a dotted line between them, but disconnected from <i>T</i>. This kind of graph signals a non-faithful distribution where the neighbourhood and Markov blanket are not defined by transversing undirected edges from <i>T</i>.</p>", "links"=>[], "tags"=>["graphical", "vocabulary"], "article_id"=>215114, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Genetics"], "users"=>["Robert Stojnic", "Audrey Qiuyan Fu", "Boris Adryan"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002725.g001", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_graphical_vocabulary_of_the_DDGraph_/215114", "title"=>"The graphical vocabulary of the DDGraph.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-08 01:25:14"}
• {"files"=>["https://ndownloader.figshare.com/files/293325"], "description"=>"<div><p>Inferring the combinatorial regulatory code of transcription factors (TFs) from genome-wide TF binding profiles is challenging. A major reason is that TF binding profiles significantly overlap and are therefore highly correlated. Clustered occurrence of multiple TFs at genomic sites may arise from chromatin accessibility and local cooperation between TFs, or binding sites may simply appear clustered if the profiles are generated from diverse cell populations. Overlaps in TF binding profiles may also result from measurements taken at closely related time intervals. It is thus of great interest to distinguish TFs that <em>directly</em> regulate gene expression from those that are <em>indirectly</em> associated with gene expression. Graphical models, in particular Bayesian networks, provide a powerful mathematical framework to infer different types of dependencies. However, existing methods do not perform well when the features (here: TF binding profiles) are highly correlated, when their association with the biological outcome is weak, and when the sample size is small. Here, we develop a novel computational method, the Neighbourhood Consistent PC (NCPC) algorithms, which deal with these scenarios much more effectively than existing methods do. We further present a novel graphical representation, the Direct Dependence Graph (DDGraph), to better display the complex interactions among variables. NCPC and DDGraph can also be applied to other problems involving highly correlated biological features. Both methods are implemented in the R package <em>ddgraph</em>, available as part of Bioconductor (<a href=\"http://bioconductor.org/packages/2.11/bioc/html/ddgraph.html\">http://bioconductor.org/packages/2.11/bioc/html/ddgraph.html</a>). Applied to real data, our method identified TFs that specify different classes of cis-regulatory modules (CRMs) in Drosophila mesoderm differentiation. Our analysis also found depletion of the early transcription factor Twist binding at the CRMs regulating expression in visceral and somatic muscle cells at later stages, which suggests a CRM-specific repression mechanism that so far has not been characterised for this class of mesodermal CRMs.</p> </div>", "links"=>[], "tags"=>["graphical", "dissection", "correlated", "transcription", "binding", "profiles"], "article_id"=>117743, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Genetics"], "users"=>["Robert Stojnic", "Audrey Qiuyan Fu", "Boris Adryan"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002725", "stats"=>{"downloads"=>4, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/A_Graphical_Modelling_Approach_to_the_Dissection_of_Highly_Correlated_Transcription_Factor_Binding_Site_Profiles__/117743", "title"=>"A Graphical Modelling Approach to the Dissection of Highly Correlated Transcription Factor Binding Site Profiles", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-08 02:09:03"}
• {"files"=>["https://ndownloader.figshare.com/files/545089"], "description"=>"<p>Variables in green circles are target variables. Variables in ovals are inferred causal neighbours. Variables in rectangles are inferred to have indirect dependence with the target. Values on the edges are (unadjusted) P-values from conditional independence tests. The same NCPC algorithm with no multiple testing correction was used as in the synthetic data benchmark. See <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002725#pcbi-1002725-g001\" target=\"_blank\">Figure 1</a> for the graphical vocabulary.</p>", "links"=>[], "tags"=>["crm", "classes", "inferred", "ncpc", "algorithm"], "article_id"=>215578, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Genetics"], "users"=>["Robert Stojnic", "Audrey Qiuyan Fu", "Boris Adryan"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002725.g006", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_DDGraphs_for_the_5_CRM_classes_inferred_by_the_NCPC_algorithm_at_945__0_05_/215578", "title"=>"DDGraphs for the 5 CRM classes inferred by the NCPC algorithm at <i>α</i> = 0.05.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-08 01:32:58"}
• {"files"=>["https://ndownloader.figshare.com/files/545169"], "description"=>"<p>For each combinatorial pattern we show the number of CRMs with this pattern in the CRM class and that in the rest of CRMs (percentages are given in parenthesis). The difference in the two frequencies (CRM class vs rest) and the corresponding P-value are given in the last two columns. P-values were computed from Fisher's exact test for each combination and adjusted for multiple testing using the Benjamini-Hochberg method. See <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002725#s4\" target=\"_blank\">Materials and Methods</a> for details. Frequency differences are colour-coded: blue for decrease in the CRM class, and orange for increase in the CRM class.</p>", "links"=>[], "tags"=>["patterns", "tfs", "inferred", "causal"], "article_id"=>215663, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Genetics"], "users"=>["Robert Stojnic", "Audrey Qiuyan Fu", "Boris Adryan"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002725.g007", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Combinatorial_patterns_of_TFs_in_inferred_causal_neighbourhoods_/215663", "title"=>"Combinatorial patterns of TFs in inferred causal neighbourhoods.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-08 01:34:23"}
• {"files"=>["https://ndownloader.figshare.com/files/544961"], "description"=>"<p>Note that the cluster that consists of Mef2 8–12 h and Bin 6–12 h (lower left corner of the matrix) is anti-correlated with early Twi 2–4 h binding.</p>", "links"=>[], "tags"=>["pairwise", "matrix", "15", "transcription", "binding", "profiles", "310"], "article_id"=>215452, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Genetics"], "users"=>["Robert Stojnic", "Audrey Qiuyan Fu", "Boris Adryan"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002725.g005", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Clustered_pairwise_correlation_matrix_of_the_15_transcription_factor_binding_profiles_over_all_310_CRMs_/215452", "title"=>"Clustered pairwise correlation matrix of the 15 transcription factor binding profiles over all 310 CRMs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-08 01:30:52"}

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Relative Metric

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F1000Prime