How Difficult Is Inference of Mammalian Causal Gene Regulatory Networks?
Publication Date
November 04, 2014
Journal
PLOS ONE
Authors
Djordje Djordjevic, Andrian Yang, Armella Zadoorian, Kevin Rungrugeecharoen, et al
Volume
9
Issue
11
Pages
e111661
DOI
http://doi.org/10.1371/journal.pone.0111661
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0111661
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/25369032
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4219746
Europe PMC
http://europepmc.org/abstract/MED/25369032
Web of Science
000344402000083
Scopus
84932093797
Mendeley
http://www.mendeley.com/research/difficult-inference-mammalian-causal-gene-regulatory-networks
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Mendeley | Further Information

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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1934081"], "description"=>"<p>Note: the TP and FP rates for the first 4 methods were calculated based on the subset of 686 RTPs that were represented in the microarray, PPI and pathway data. The TP and FP rates for perturbation data were based on the subset of 39 RTPs with a regulator matching the pathway being perturbed.</p>", "links"=>[], "tags"=>["organ development", "GRN inference methods", "microarray gene expression profiles", "perturbation evidence records", "Mammalian Causal Gene Regulatory Networks", "mouse GRN datasets"], "article_id"=>1324734, "categories"=>["Biological Sciences"], "users"=>["Djordje Djordjevic", "Andrian Yang", "Armella Zadoorian", "Kevin Rungrugeecharoen", "Joshua W. K. Ho"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0111661.g006", "stats"=>{"downloads"=>5, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_the_true_positive_and_false_positive_rates_as_determined_by_different_network_inference_approaches_on_the_tooth_dataset_Pearson_correlation_Pathway_Commons_database_protein_protein_interactions_PPI_the_union_of_the_previous_three_methods_an/1324734", "title"=>"Comparison of the true positive and false positive rates as determined by different network inference approaches on the tooth dataset: Pearson correlation, Pathway Commons database, protein-protein interactions (PPI), the union of the previous three methods and direct effect on genetic perturbation (log fold change cut-off or 0.5 and 1).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-04 21:59:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/1934078"], "description"=>"<p>To account for the possibility that our literature-curated RTP may represent indirect regulatory interactions, we allow matching of a RTP with a linear path of multiple edges (x-axis). The bar chart above each plot shows the size of the network. Dotted lines shows control background of 1,000 node-label-permuted randomised networks.</p>", "links"=>[], "tags"=>["organ development", "GRN inference methods", "microarray gene expression profiles", "perturbation evidence records", "Mammalian Causal Gene Regulatory Networks", "mouse GRN datasets"], "article_id"=>1324731, "categories"=>["Biological Sciences"], "users"=>["Djordje Djordjevic", "Andrian Yang", "Armella Zadoorian", "Kevin Rungrugeecharoen", "Joshua W. K. Ho"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0111661.g003", "stats"=>{"downloads"=>2, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evaluation_of_sensitivity_true_positive_rate_and_specificity_1_false_positive_rate_of_edge_discovery_by_GENIE3_A_8211_C_and_ARANCE_D_8211_F_using_the_tooth_and_heart_microarray_datasets_/1324731", "title"=>"Evaluation of sensitivity (true positive rate) and specificity (1-false positive rate) of edge discovery by GENIE3 (A–C) and ARANCE (D–F) using the tooth and heart microarray datasets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-04 21:59:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/1934077"], "description"=>"<p>RTP classes are activating (Act.), no effect (No.) and inhibitory (Inhib.).</p>", "links"=>[], "tags"=>["organ development", "GRN inference methods", "microarray gene expression profiles", "perturbation evidence records", "Mammalian Causal Gene Regulatory Networks", "mouse GRN datasets"], "article_id"=>1324730, "categories"=>["Biological Sciences"], "users"=>["Djordje Djordjevic", "Andrian Yang", "Armella Zadoorian", "Kevin Rungrugeecharoen", "Joshua W. K. Ho"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0111661.g002", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spearman_correlation_of_different_classes_of_RTP_in_the_tooth_data_A_and_the_heart_data_B_/1324730", "title"=>"Spearman correlation of different classes of RTP in the tooth data (A) and the heart data (B).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-04 21:59:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/1934080"], "description"=>"<p>Coloured points represent probes of differentially responsive genes between the two tissues. Pearson correlation is also shown.</p>", "links"=>[], "tags"=>["organ development", "GRN inference methods", "microarray gene expression profiles", "perturbation evidence records", "Mammalian Causal Gene Regulatory Networks", "mouse GRN datasets"], "article_id"=>1324733, "categories"=>["Biological Sciences"], "users"=>["Djordje Djordjevic", "Andrian Yang", "Armella Zadoorian", "Kevin Rungrugeecharoen", "Joshua W. K. Ho"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0111661.g005", "stats"=>{"downloads"=>2, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scatter_plots_show_the_extent_of_tissue_specific_differential_expression_in_dental_epithelium_y_axis_and_dental_mesenchyme_x_axis_as_a_result_of_Pax9_knockout_A_Msx1_knockout_B_Bmp4_stimulation_C_and_Wnt_stimulation_D_/1324733", "title"=>"Scatter plots show the extent of tissue-specific differential expression in dental epithelium (y-axis) and dental mesenchyme (x-axis) as a result of <i>Pax9</i> knockout (A), <i>Msx1</i> knockout (B), <i>Bmp4</i> stimulation (C) and <i>Wnt</i> stimulation (D).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-04 21:59:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/1934094", "https://ndownloader.figshare.com/files/1934095", "https://ndownloader.figshare.com/files/1934096", "https://ndownloader.figshare.com/files/1934097", "https://ndownloader.figshare.com/files/1934098", "https://ndownloader.figshare.com/files/1934099", "https://ndownloader.figshare.com/files/1934100", "https://ndownloader.figshare.com/files/1934101", "https://ndownloader.figshare.com/files/1934102", "https://ndownloader.figshare.com/files/1934103"], "description"=>"<div><p>Gene regulatory networks (GRNs) play a central role in systems biology, especially in the study of mammalian organ development. One key question remains largely unanswered: Is it possible to infer mammalian causal GRNs using observable gene co-expression patterns alone? We assembled two mouse GRN datasets (embryonic tooth and heart) and matching microarray gene expression profiles to systematically investigate the difficulties of mammalian causal GRN inference. The GRNs were assembled based on pieces of experimental genetic perturbation evidence from manually reading primary research articles. Each piece of perturbation evidence records the qualitative change of the expression of one gene following knock-down or over-expression of another gene. Our data have thorough annotation of tissue types and embryonic stages, as well as the type of regulation (activation, inhibition and no effect), which uniquely allows us to estimate both sensitivity and specificity of the inference of tissue specific causal GRN edges. Using these unprecedented datasets, we found that gene co-expression does not reliably distinguish true positive from false positive interactions, making inference of GRN in mammalian development very difficult. Nonetheless, if we have expression profiling data from genetic or molecular perturbation experiments, such as gene knock-out or signalling stimulation, it is possible to use the set of differentially expressed genes to recover causal regulatory relationships with good sensitivity and specificity. Our result supports the importance of using perturbation experimental data in causal network reconstruction. Furthermore, we showed that causal gene regulatory relationship can be highly cell type or developmental stage specific, suggesting the importance of employing expression profiles from homogeneous cell populations. This study provides essential datasets and empirical evidence to guide the development of new GRN inference methods for mammalian organ development.</p></div>", "links"=>[], "tags"=>["organ development", "GRN inference methods", "microarray gene expression profiles", "perturbation evidence records", "Mammalian Causal Gene Regulatory Networks", "mouse GRN datasets"], "article_id"=>1324747, "categories"=>["Biological Sciences"], "users"=>["Djordje Djordjevic", "Andrian Yang", "Armella Zadoorian", "Kevin Rungrugeecharoen", "Joshua W. K. Ho"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0111661.s001", "https://dx.doi.org/10.1371/journal.pone.0111661.s002", "https://dx.doi.org/10.1371/journal.pone.0111661.s003", "https://dx.doi.org/10.1371/journal.pone.0111661.s004", "https://dx.doi.org/10.1371/journal.pone.0111661.s005", "https://dx.doi.org/10.1371/journal.pone.0111661.s006", "https://dx.doi.org/10.1371/journal.pone.0111661.s007", "https://dx.doi.org/10.1371/journal.pone.0111661.s008", "https://dx.doi.org/10.1371/journal.pone.0111661.s009", "https://dx.doi.org/10.1371/journal.pone.0111661.s010"], "stats"=>{"downloads"=>17, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_How_Difficult_Is_Inference_of_Mammalian_Causal_Gene_Regulatory_Networks_/1324747", "title"=>"How Difficult Is Inference of Mammalian Causal Gene Regulatory Networks?", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-11-04 21:59:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/1934079"], "description"=>"<p>RTPs that are inhibiting (A), have no effect (B), or are activating (C) trend to have negative, close to zero and positive fold changes respectively. (D) shows the consistency of the literature based RTP type (Lit.) and microarray data (M.A.) as fold change cut-off varies between 0 and 3 (both up- or down-regulation).</p>", "links"=>[], "tags"=>["organ development", "GRN inference methods", "microarray gene expression profiles", "perturbation evidence records", "Mammalian Causal Gene Regulatory Networks", "mouse GRN datasets"], "article_id"=>1324732, "categories"=>["Biological Sciences"], "users"=>["Djordje Djordjevic", "Andrian Yang", "Armella Zadoorian", "Kevin Rungrugeecharoen", "Joshua W. K. Ho"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0111661.g004", "stats"=>{"downloads"=>2, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Fold_changes_log_from_tooth_microarray_perturbation_experiments_that_matched_the_perturbation_evidence_in_the_literature_show_consistency_with_expected_trends_/1324732", "title"=>"Fold changes (log) from tooth microarray perturbation experiments that matched the perturbation evidence in the literature show consistency with expected trends.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-04 21:59:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/1934076"], "description"=>"<p>CardiacCode is a public online resource allowing interactive visualisation of the heart GRN, and download of the heart data collected and used in this study.</p>", "links"=>[], "tags"=>["organ development", "GRN inference methods", "microarray gene expression profiles", "perturbation evidence records", "Mammalian Causal Gene Regulatory Networks", "mouse GRN datasets"], "article_id"=>1324729, "categories"=>["Biological Sciences"], "users"=>["Djordje Djordjevic", "Andrian Yang", "Armella Zadoorian", "Kevin Rungrugeecharoen", "Joshua W. K. Ho"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0111661.g001", "stats"=>{"downloads"=>0, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_CardiacCode_is_a_public_online_resource_allowing_interactive_visualisation_of_the_heart_GRN_and_download_of_the_heart_data_collected_and_used_in_this_study_/1324729", "title"=>"CardiacCode is a public online resource allowing interactive visualisation of the heart GRN, and download of the heart data collected and used in this study.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-04 21:59:21"}
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Relative Metric

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