Combinatorial Expression Rules of Ion Channel Genes in Juvenile Rat (Rattus norvegicus) Neocortical Neurons
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{"title"=>"Combinatorial expression rules of ion channel genes in juvenile Rat (rattus norvegicus) neocortical neurons", "type"=>"journal", "authors"=>[{"first_name"=>"Georges", "last_name"=>"Khazen", "scopus_author_id"=>"24314780000"}, {"first_name"=>"Sean L.", "last_name"=>"Hill", "scopus_author_id"=>"8757126300"}, {"first_name"=>"Felix", "last_name"=>"Schürmann", "scopus_author_id"=>"23976834700"}, {"first_name"=>"Henry", "last_name"=>"Markram", "scopus_author_id"=>"56275180000"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84859570171", "doi"=>"10.1371/journal.pone.0034786", "issn"=>"19326203", "pui"=>"364606658", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "pmid"=>"22509357", "scopus"=>"2-s2.0-84859570171"}, "id"=>"11842f5c-0235-33bf-9d03-fd64fdaf7bfd", "abstract"=>"The electrical diversity of neurons arises from the expression of different combinations of ion channels. The gene expression rules governing these combinations are not known. We examined the expression of twenty-six ion channel genes in a broad range of single neocortical neuron cell types. Using expression data from a subset of twenty-six ion channel genes in ten different neocortical neuronal types, classified according to their electrophysiological properties, morphologies and anatomical positions, we first developed an incremental Support Vector Machine (iSVM) model that prioritizes the predictive value of single and combinations of genes for the rest of the expression pattern. With this approach we could predict the expression patterns for the ten neuronal types with an average 10-fold cross validation accuracy of 87% and for a further fourteen neuronal types not used in building the model, with an average accuracy of 75%. The expression of the genes for HCN4, Kv2.2, Kv3.2 and Caβ3 were found to be particularly strong predictors of ion channel gene combinations, while expression of the Kv1.4 and Kv3.3 genes has no predictive value. Using a logic gate analysis, we then extracted a spectrum of observed combinatorial gene expression rules of twenty ion channels in different neocortical neurons. We also show that when applied to a completely random and independent data, the model could not extract any rules and that it is only possible to extract them if the data has consistent expression patterns. This novel strategy can be used for predictive reverse engineering combinatorial expression rules from single-cell data and could help identify candidate transcription regulatory processes.", "link"=>"http://www.mendeley.com/research/combinatorial-expression-rules-ion-channel-genes-juvenile-rat-rattus-norvegicus-neocortical-neurons-1", "reader_count"=>40, "reader_count_by_academic_status"=>{"Researcher"=>18, "Student > Ph. D. Student"=>8, "Other"=>6, "Student > Master"=>3, "Student > Bachelor"=>2, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Researcher"=>18, "Student > Ph. D. Student"=>8, "Other"=>6, "Student > Master"=>3, "Student > Bachelor"=>2, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>3, "Agricultural and Biological Sciences"=>20, "Medicine and Dentistry"=>2, "Philosophy"=>2, "Neuroscience"=>4, "Physics and Astronomy"=>2, "Psychology"=>1, "Computer Science"=>5}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Neuroscience"=>{"Neuroscience"=>4}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>20}, "Computer Science"=>{"Computer Science"=>5}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}, "Philosophy"=>{"Philosophy"=>2}}, "reader_count_by_country"=>{"Austria"=>1, "United States"=>5, "Luxembourg"=>1, "United Kingdom"=>3, "Portugal"=>1, "Germany"=>1}, "group_count"=>3}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/655331"], "description"=>"<p>The indegree represents the number of input channels used in the iSVM model of a given channel, and the outdegree, number of times a given channel was used as an input for another channel.</p>", "links"=>[], "tags"=>["outdegree", "twenty-six", "ion"], "article_id"=>325822, "categories"=>["Biological Sciences", "Neuroscience", "Genetics"], "users"=>["Georges Khazen", "Sean L. Hill", "Felix Schürmann", "Henry Markram"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0034786.g005", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Indegree_and_outdegree_of_the_twenty_six_ion_channel_genes_/325822", "title"=>"Indegree and outdegree of the twenty-six ion channel genes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-04-11 01:37:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/336424", "https://ndownloader.figshare.com/files/336460", "https://ndownloader.figshare.com/files/336488", "https://ndownloader.figshare.com/files/336545", "https://ndownloader.figshare.com/files/336602", "https://ndownloader.figshare.com/files/336656", "https://ndownloader.figshare.com/files/336735", "https://ndownloader.figshare.com/files/336807", "https://ndownloader.figshare.com/files/336860", "https://ndownloader.figshare.com/files/336908", "https://ndownloader.figshare.com/files/336958"], "description"=>"<div><p>The electrical diversity of neurons arises from the expression of different combinations of ion channels. The gene expression rules governing these combinations are not known. We examined the expression of twenty-six ion channel genes in a broad range of single neocortical neuron cell types. Using expression data from a subset of twenty-six ion channel genes in ten different neocortical neuronal types, classified according to their electrophysiological properties, morphologies and anatomical positions, we first developed an incremental Support Vector Machine (iSVM) model that prioritizes the predictive value of single and combinations of genes for the rest of the expression pattern. With this approach we could predict the expression patterns for the ten neuronal types with an average 10-fold cross validation accuracy of 87% and for a further fourteen neuronal types not used in building the model, with an average accuracy of 75%. The expression of the genes for <em>HCN4</em>, <em>Kv2.2</em>, <em>Kv3.2</em> and <em>Caβ3</em> were found to be particularly strong predictors of ion channel gene combinations, while expression of the <em>Kv1.4</em> and <em>Kv3.3</em> genes has no predictive value. Using a logic gate analysis, we then extracted a spectrum of observed combinatorial gene expression rules of twenty ion channels in different neocortical neurons. We also show that when applied to a completely random and independent data, the model could not extract any rules and that it is only possible to extract them if the data has consistent expression patterns. This novel strategy can be used for predictive reverse engineering combinatorial expression rules from single-cell data and could help identify candidate transcription regulatory processes.</p> </div>", "links"=>[], "tags"=>["combinatorial", "rules", "ion", "genes", "juvenile", "neocortical", "neurons"], "article_id"=>126490, "categories"=>["Biological Sciences", "Neuroscience", "Genetics"], "users"=>["Georges Khazen", "Sean L. Hill", "Felix Schürmann", "Henry Markram"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0034786.s001", "https://dx.doi.org/10.1371/journal.pone.0034786.s002", "https://dx.doi.org/10.1371/journal.pone.0034786.s003", "https://dx.doi.org/10.1371/journal.pone.0034786.s004", "https://dx.doi.org/10.1371/journal.pone.0034786.s005", "https://dx.doi.org/10.1371/journal.pone.0034786.s006", "https://dx.doi.org/10.1371/journal.pone.0034786.s007", "https://dx.doi.org/10.1371/journal.pone.0034786.s008", "https://dx.doi.org/10.1371/journal.pone.0034786.s009", "https://dx.doi.org/10.1371/journal.pone.0034786.s010", "https://dx.doi.org/10.1371/journal.pone.0034786.s011"], "stats"=>{"downloads"=>24, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Combinatorial_Expression_Rules_of_Ion_Channel_Genes_in_Juvenile_Rat_Rattus_norvegicus_Neocortical_Neurons/126490", "title"=>"Combinatorial Expression Rules of Ion Channel Genes in Juvenile Rat (<em>Rattus norvegicus</em>) Neocortical Neurons", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-04-11 01:48:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/655440"], "description"=>"<p>Layer, morphology and electrical type combinations (LME) of the model dataset with the corresponding neurons counts.</p>", "links"=>[], "tags"=>["morphology", "combinations", "dataset", "corresponding", "neurons"], "article_id"=>325928, "categories"=>["Biological Sciences", "Neuroscience", "Genetics"], "users"=>["Georges Khazen", "Sean L. Hill", "Felix Schürmann", "Henry Markram"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0034786.t002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Layer_morphology_and_electrical_type_combinations_LME_of_the_model_dataset_with_the_corresponding_neurons_counts_/325928", "title"=>"Layer, morphology and electrical type combinations (LME) of the model dataset with the corresponding neurons counts.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-04-11 01:38:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/655220"], "description"=>"<p>The edges are colored in red, blue, green, orange, and black based on the predictive value of the input ion channel gene. Red edges have the highest predictive value and represent the genes that were selected as inputs at the first incremental step while the black edges have the lowest predictive value and represent the genes that were selected at the last incremental step.</p>", "links"=>[], "tags"=>["diagram", "links", "ion", "genes", "corresponding"], "article_id"=>325712, "categories"=>["Biological Sciences", "Neuroscience", "Genetics"], "users"=>["Georges Khazen", "Sean L. Hill", "Felix Schürmann", "Henry Markram"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0034786.g004", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Directed_network_diagram_that_links_the_input_ion_channel_genes_to_their_corresponding_output_channels_/325712", "title"=>"Directed network diagram that links the input ion channel genes to their corresponding output channels.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-04-11 01:35:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/655504"], "description"=>"<p>Different layers, morphological and electrical phenotypic profiles of the 65 neurons.</p>", "links"=>[], "tags"=>["morphological", "phenotypic", "profiles", "65"], "article_id"=>325995, "categories"=>["Biological Sciences", "Neuroscience", "Genetics"], "users"=>["Georges Khazen", "Sean L. Hill", "Felix Schürmann", "Henry Markram"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0034786.t001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Different_layers_morphological_and_electrical_phenotypic_profiles_of_the_65_neurons_/325995", "title"=>"Different layers, morphological and electrical phenotypic profiles of the 65 neurons.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-04-11 01:39:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/655134"], "description"=>"<p><b><i>A</i></b> 10-fold cross validation accuracies of the Logistic Regression (black), base SVM models (blue), and the iSVM models (red) of the twenty-six ion channel genes. <b><i>B</i></b>, left, Receiver Operating Characteristic (ROC) curves of the iSVM models for the twenty-six ion channel genes. <b><i>B</i></b>, right, boxplots of the 10-fold cross validation accuracies of the iSVM model (red), random inputs iSVM (riiSVM) (blue), and random data iSVM (rdiSVM) (green) after 1000 iterations.</p>", "links"=>[], "tags"=>["genetics and genomics", "Computational biology", "neuroscience"], "article_id"=>325625, "categories"=>["Biological Sciences", "Neuroscience", "Genetics"], "users"=>["Georges Khazen", "Sean L. Hill", "Felix Schürmann", "Henry Markram"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0034786.g003", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Models_assessment_and_accuracy_/325625", "title"=>"Models assessment and accuracy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-04-11 01:33:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/654951"], "description"=>"<p><b><i>A</i></b> Reconstructed morphologies of three L2/3 MC-cAD neurons. <b><i>B</i></b> Electrical response of the same three L2/3 MC-cAD neurons. <b><i>C</i></b> Genetic profiles of the twenty-six ion channel genes in the nine L2/3 MC-cAD neurons.</p>", "links"=>[], "tags"=>["mc-cad"], "article_id"=>325445, "categories"=>["Biological Sciences", "Neuroscience", "Genetics"], "users"=>["Georges Khazen", "Sean L. Hill", "Felix Schürmann", "Henry Markram"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0034786.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Diversity_within_layer_L2_3_MC_cAD_neurons_/325445", "title"=>"Diversity within layer L2/3 MC-cAD neurons.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-04-11 01:30:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/655465"], "description"=>"<p>Layer, morphology and electrical type combinations (LME) of the generalization dataset with the corresponding neurons counts.</p>", "links"=>[], "tags"=>["morphology", "combinations", "generalization", "dataset", "corresponding", "neurons"], "article_id"=>325957, "categories"=>["Biological Sciences", "Neuroscience", "Genetics"], "users"=>["Georges Khazen", "Sean L. Hill", "Felix Schürmann", "Henry Markram"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0034786.t003", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Layer_morphology_and_electrical_type_combinations_LME_of_the_generalization_dataset_with_the_corresponding_neurons_counts_/325957", "title"=>"Layer, morphology and electrical type combinations (LME) of the generalization dataset with the corresponding neurons counts.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-04-11 01:39:17"}
  • {"files"=>["https://ndownloader.figshare.com/files/655036"], "description"=>"<p><b><i>A</i></b> Overall expression frequency of the twenty-six ion channel genes in the 65 neurons of the experimental model dataset. <b><i>B</i></b> Expression frequencies of the twenty-six ion channel genes in the ten neuronal types of the experimental model dataset. <b><i>C</i></b> Overall expression frequency of the twenty-six ion channel genes in the 65 neurons of the random model dataset. <b><i>D</i></b> Expression frequencies of the twenty-six ion channel genes in the ten neuronal types of the random model dataset.</p>", "links"=>[], "tags"=>["frequencies", "twenty-six", "ion", "genes"], "article_id"=>325526, "categories"=>["Biological Sciences", "Neuroscience", "Genetics"], "users"=>["Georges Khazen", "Sean L. Hill", "Felix Schürmann", "Henry Markram"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0034786.g002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Gene_expression_frequencies_of_the_twenty_six_ion_channel_genes_in_the_model_dataset_/325526", "title"=>"Gene expression frequencies of the twenty-six ion channel genes in the model dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-04-11 01:32:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/655383"], "description"=>"<p>The generalization data set consists of eighteen neurons belonging to fourteen LME combinations.</p>", "links"=>[], "tags"=>["dataset", "isvm", "models", "twenty-six", "ion"], "article_id"=>325875, "categories"=>["Biological Sciences", "Neuroscience", "Genetics"], "users"=>["Georges Khazen", "Sean L. Hill", "Felix Schürmann", "Henry Markram"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0034786.g006", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Generalization_dataset_accuracy_for_iSVM_models_of_the_twenty_six_ion_channel_genes_/325875", "title"=>"Generalization dataset accuracy for iSVM models of the twenty-six ion channel genes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-04-11 01:37:55"}

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Relative Metric

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