Impact of Noise on Molecular Network Inference
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{"title"=>"Impact of noise on molecular network inference", "type"=>"journal", "authors"=>[{"first_name"=>"Radhakrishnan", "last_name"=>"Nagarajan", "scopus_author_id"=>"23009591900"}, {"first_name"=>"Marco", "last_name"=>"Scutari", "scopus_author_id"=>"55865783400"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84891928169", "doi"=>"10.1371/journal.pone.0080735", "pui"=>"372094742", "pmid"=>"24339879", "scopus"=>"2-s2.0-84891928169", "issn"=>"19326203"}, "id"=>"3c1411fd-47b3-3da2-91b0-14690900ed85", "abstract"=>"Molecular entities work in concert as a system and mediate phenotypic outcomes and disease states. There has been recent interest in modelling the associations between molecular entities from their observed expression profiles as networks using a battery of algorithms. These networks have proven to be useful abstractions of the underlying pathways and signalling mechanisms. Noise is ubiquitous in molecular data and can have a pronounced effect on the inferred network. Noise can be an outcome of several factors including: inherent stochastic mechanisms at the molecular level, variation in the abundance of molecules, heterogeneity, sensitivity of the biological assay or measurement artefacts prevalent especially in high-throughput settings. The present study investigates the impact of discrepancies in noise variance on pair-wise dependencies, conditional dependencies and constraint-based Bayesian network structure learning algorithms that incorporate conditional independence tests as a part of the learning process. Popular network motifs and fundamental connections, namely: (a) common-effect, (b) three-chain, and (c) coherent type-I feed-forward loop (FFL) are investigated. The choice of these elementary networks can be attributed to their prevalence across more complex networks. Analytical expressions elucidating the impact of discrepancies in noise variance on pairwise dependencies and conditional dependencies for special cases of these motifs are presented. Subsequently, the impact of noise on two popular constraint-based Bayesian network structure learning algorithms such as Grow-Shrink (GS) and Incremental Association Markov Blanket (IAMB) that implicitly incorporate tests for conditional independence is investigated. Finally, the impact of noise on networks inferred from publicly available single cell molecular expression profiles is investigated. While discrepancies in noise variance are overlooked in routine molecular network inference, the results presented clearly elucidate their non-trivial impact on the conclusions that in turn can challenge the biological significance of the findings. The analytical treatment and arguments presented are generic and not restricted to molecular data sets.", "link"=>"http://www.mendeley.com/research/impact-noise-molecular-network-inference-3", "reader_count"=>12, "reader_count_by_academic_status"=>{"Librarian"=>1, "Researcher"=>2, "Student > Ph. D. Student"=>4, "Other"=>1, "Student > Master"=>3, "Professor"=>1}, "reader_count_by_user_role"=>{"Librarian"=>1, "Researcher"=>2, "Student > Ph. D. Student"=>4, "Other"=>1, "Student > Master"=>3, "Professor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>2, "Agricultural and Biological Sciences"=>4, "Medicine and Dentistry"=>1, "Physics and Astronomy"=>1, "Social Sciences"=>1, "Computer Science"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>4}, "Computer Science"=>{"Computer Science"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Environmental Science"=>{"Environmental Science"=>1}}, "reader_count_by_country"=>{"United States"=>1}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1305897"], "description"=>"<p>Popular three-gene network motifs: common-effect, three-chain and coherent type-I feed-forward loop are shown in (a), (b) and (c) respectively.</p>", "links"=>[], "tags"=>["three-gene", "three-chain", "coherent", "type-i", "feed-forward"], "article_id"=>870373, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Science Policy"], "users"=>["Radhakrishnan Nagarajan", "Marco Scutari"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0080735.g001", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Popular_three_gene_network_motifs_common_effect_three_chain_and_coherent_type_I_feed_forward_loop_are_shown_in_a_b_and_c_respectively_/870373", "title"=>"Popular three-gene network motifs: common-effect, three-chain and coherent type-I feed-forward loop are shown in (a), (b) and (c) respectively.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-05 05:13:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1305898"], "description"=>"<p>The <i>x</i>-axis labels correspond to the correlation coefficients in (a, b, c) and partial correlations in (d, e, f) respectively. The (circles, squares and triangles) in each of the subplots correspond to noise variances with magnitudes , and respectively. The points bounded by the dotted rectangle represent cases that occurred much lesser than 80% of the time as significant ( = 0.001) across 200 independent realizations.</p>", "links"=>[], "tags"=>["coefficient", "estimates", "200", "realizations", "three-chain", "coherent", "feed-forward", "motifs", "choices", "variances"], "article_id"=>870374, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Science Policy"], "users"=>["Radhakrishnan Nagarajan", "Marco Scutari"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0080735.g002", "stats"=>{"downloads"=>0, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_average_correlation_coefficient_and_partial_correlation_estimates_across_200_independent_realizations_of_the_common_effect_three_chain_and_coherent_Type_I_feed_forward_loop_network_motifs_for_various_choices_of_noise_variances_are_shown_in_a_d_b_e_an/870374", "title"=>"The average correlation coefficient and partial correlation estimates across 200 independent realizations of the common-effect, three-chain and coherent Type I feed-forward loop network motifs for various choices of noise variances are shown in (a, d), (b, e) and (c, f) respectively.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-05 05:13:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1305903"], "description"=>"<p>Pair-wise and conditional dependencies across the three network motifs in the asymptotic noise limits.</p>", "links"=>[], "tags"=>["conditional", "dependencies", "motifs", "asymptotic"], "article_id"=>870379, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Science Policy"], "users"=>["Radhakrishnan Nagarajan", "Marco Scutari"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0080735.t001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pair_wise_and_conditional_dependencies_across_the_three_network_motifs_in_the_asymptotic_noise_limits_/870379", "title"=>"Pair-wise and conditional dependencies across the three network motifs in the asymptotic noise limits.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-12-05 05:13:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1305901"], "description"=>"<p>The confidences of the edges are represented as percentage of the edges that persisted across 200 independent realizations. Edges with are shown by solid arrows whereas others are shown by dotted arrows. Edges with confidence are deemed noisy and excluded for clarity.</p>", "links"=>[], "tags"=>["networks", "inferred", "grow-shrink", "algorithm", "pearson", "three-gene", "common-effect", "three-chain", "coherent", "type-i", "feed-forward", "choices"], "article_id"=>870377, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Science Policy"], "users"=>["Radhakrishnan Nagarajan", "Marco Scutari"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0080735.g003", "stats"=>{"downloads"=>10, "page_views"=>199, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Bayesian_networks_inferred_using_Grow_Shrink_algorithm_along_with_Pearson_correlation__0_01_for_the_three_gene_network_motifs_namely_common_effect_a_c_three_chain_d_f_and_coherent_type_I_feed_forward_loop_g_i_for_various_choices_of_noise_variances_and_/870377", "title"=>"Bayesian networks inferred using Grow-Shrink algorithm along with Pearson correlation ( = 0.01) for the three-gene network motifs, namely: common-effect (a-c), three-chain (d-f) and coherent type-I feed-forward loop (g-i) for various choices of noise variances: and .", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-05 05:13:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1305902"], "description"=>"<p>Confidences estimated from 200 independent bootstrap realizations are shown along the edges. Edges with are shown by solid arrows whereas others are shown by dotted arrows. Edges with confidence are deemed noisy and excluded for clarity. The edge confidences of the networks inferred from the raw data are shown in (a), (b) and (c) respectively. Those inferred on the log-transformed data are shown in (d), (e) and (f) respectively.</p>", "links"=>[], "tags"=>["networks", "inferred", "grow-shrink", "algorithm", "molecular", "sample-size", "800", "pearson"], "article_id"=>870378, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Science Policy"], "users"=>["Radhakrishnan Nagarajan", "Marco Scutari"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0080735.g004", "stats"=>{"downloads"=>4, "page_views"=>43, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Bayesian_networks_inferred_using_Grow_Shrink_algorithm_from_the_molecular_expression_data_PIP2_PIP3_Plc_with_sample_size_800_and_Pearson_correlation__0_01_are_shown_in_a_f_/870378", "title"=>"Bayesian networks inferred using Grow-Shrink algorithm from the molecular expression data (PIP2, PIP3, Plcγ) with sample-size 800 and Pearson correlation ( = 0.01) are shown in (a–f).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-05 05:13:34"}

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Relative Metric

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