An Integrated Approach for the Analysis of Biological Pathways using Mixed Models
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{"title"=>"An integrated approach for the analysis of biological pathways using mixed models", "type"=>"journal", "authors"=>[{"first_name"=>"Lily", "last_name"=>"Wang", "scopus_author_id"=>"35293641400"}, {"first_name"=>"Bing", "last_name"=>"Zhang", "scopus_author_id"=>"56658811900"}, {"first_name"=>"Russell D.", "last_name"=>"Wolfinger", "scopus_author_id"=>"7003568602"}, {"first_name"=>"Xi", "last_name"=>"Chen", "scopus_author_id"=>"57189989139"}], "year"=>2008, "source"=>"PLoS Genetics", "identifiers"=>{"pmid"=>"18852846", "scopus"=>"2-s2.0-48249108773", "issn"=>"15537390", "pui"=>"352076475", "doi"=>"10.1371/journal.pgen.1000115", "isbn"=>"1553-7404", "sgr"=>"48249108773"}, "id"=>"ea671b49-485d-3e77-9b52-8a980df974fa", "abstract"=>"Gene class, ontology, or pathway testing analysis has become increasingly popular in microarray data analysis. Such approaches allow the integration of gene annotation databases, such as Gene Ontology and KEGG Pathway, to formally test for subtle but coordinated changes at a system level. Higher power in gene class testing is gained by combining weak signals from a number of individual genes in each pathway. We propose an alternative approach for gene-class testing based on mixed models, a class of statistical models that: a) provides the ability to model and borrow strength across genes that are both up and down in a pathway, b) operates within a well-established statistical framework amenable to direct control of false positive or false discovery rates, c) exhibits improved power over widely used methods under normal location-based alternative hypotheses, and d) handles complex experimental designs for which permutation resampling is difficult. We compare the properties of this mixed models approach with nonparametric method GSEA and parametric method PAGE using a simulation study, and illustrate its application with a diabetes data set and a dose-response data set.", "link"=>"http://www.mendeley.com/research/integrated-approach-analysis-biological-pathways-using-mixed-models-2", "reader_count"=>57, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>10, "Researcher"=>20, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>3, "Other"=>1, "Student > Master"=>3, "Student > Bachelor"=>2, "Lecturer > Senior Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>10, "Researcher"=>20, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>3, "Other"=>1, "Student > Master"=>3, "Student > Bachelor"=>2, "Lecturer > Senior Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>3, "Biochemistry, Genetics and Molecular Biology"=>2, "Mathematics"=>5, "Agricultural and Biological Sciences"=>30, "Medicine and Dentistry"=>5, "Neuroscience"=>1, "Chemistry"=>1, "Social Sciences"=>1, "Computer Science"=>9}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>5}, "Neuroscience"=>{"Neuroscience"=>1}, "Chemistry"=>{"Chemistry"=>1}, "Social Sciences"=>{"Social Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>30}, "Computer Science"=>{"Computer Science"=>9}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Mathematics"=>{"Mathematics"=>5}}, "reader_count_by_country"=>{"Greece"=>1, "Canada"=>1, "Austria"=>1, "Netherlands"=>1, "United States"=>6, "United Kingdom"=>1, "Portugal"=>1, "India"=>1}, "group_count"=>2}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/927236"], "description"=>"<p>When individual tests were conducted with 6 hr treatment samples and 24 hr samples separately, only 5 and 1 gene-sets were significant at 0.01 FDR level. However, when all samples were used, for testing gene-sets with non-significant Duration×Linear Trend interaction, 40 gene-sets were significant at 0.01 FDR level. This shows pooling data with similar trends from both treatment durations improves the statistical power for identifying biologically meaningful gene sets.</p>", "links"=>[], "tags"=>["hne"], "article_id"=>597696, "categories"=>["Computational Biology", "Infectious Diseases", "Molecular Biology"], "users"=>["Lily Wang", "Bing Zhang", "Russell D. Wolfinger", "Xi Chen"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000115.g003", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Workflow_and_results_for_Mixed_Model_analysis_of_HNE_dataset_/597696", "title"=>"Workflow and results for Mixed Model analysis of HNE dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-07-04 02:08:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/457449", "https://ndownloader.figshare.com/files/457582"], "description"=>"<div><p>Gene class, ontology, or pathway testing analysis has become increasingly popular in microarray data analysis. Such approaches allow the integration of gene annotation databases, such as Gene Ontology and KEGG Pathway, to formally test for subtle but coordinated changes at a system level. Higher power in gene class testing is gained by combining weak signals from a number of individual genes in each pathway. We propose an alternative approach for gene-class testing based on mixed models, a class of statistical models that:</p><h3></h3><p></p><p></p><p>provides the ability to model and borrow strength across genes that are both up and down in a pathway,</p><p></p><p></p><p>operates within a well-established statistical framework amenable to direct control of false positive or false discovery rates,</p><p></p><p></p><p>exhibits improved power over widely used methods under normal location-based alternative hypotheses, and</p><p></p><p></p><p>handles complex experimental designs for which permutation resampling is difficult.</p><p></p><p></p><p>We compare the properties of this mixed models approach with nonparametric method GSEA and parametric method PAGE using a simulation study, and illustrate its application with a diabetes data set and a dose-response data set.</p></div>", "links"=>[], "tags"=>["pathways", "models"], "article_id"=>150115, "categories"=>["Biological Sciences", "Cancer", "Molecular Biology"], "users"=>["Lily Wang", "Bing Zhang", "Russell D. Wolfinger", "Xi Chen"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1000115.s001", "https://dx.doi.org/10.1371/journal.pgen.1000115.s002"], "stats"=>{"downloads"=>8, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/An_Integrated_Approach_for_the_Analysis_of_Biological_Pathways_using_Mixed_Models/150115", "title"=>"An Integrated Approach for the Analysis of Biological Pathways using Mixed Models", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2008-07-04 00:01:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/927413"], "description"=>"<p>Size refers to the number of genes in the gene-set. Both mixed model and GSEA selected the pathway “OXPHOS_HG_U133A_probes” as the most significantly changed pathway and ranked the pathways “human_mitoDB_6_2002_HG_U133A_pro”, “mitochondr_HG_U133A_probes” high on their significant pathways list. Mixed model selected 6 additional gene-sets at 5% FDR level.</p>", "links"=>[], "tags"=>["gsea", "dataset", "mootha", "et"], "article_id"=>597860, "categories"=>["Computational Biology", "Infectious Diseases", "Molecular Biology"], "users"=>["Lily Wang", "Bing Zhang", "Russell D. Wolfinger", "Xi Chen"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000115.t002", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_Mixed_Model_and_GSEA_Results_for_the_Analysis_of_Diabetes_Dataset_from_Mootha_et_al_2003_/597860", "title"=>"Comparison of Mixed Model and GSEA Results for the Analysis of Diabetes Dataset from Mootha et al. (2003).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2008-07-04 02:11:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/927372"], "description"=>"<p>tot_p = proportion of genes with treatment effect added to treatment group.</p><p>up_p = among treated genes, the proportion of genes for which positive treatment effect <i>mu</i> was added.</p><p>1-up = among treated genes, the proportion of genes for which negative treatment effect - <i>mu</i> was added.</p>", "links"=>[], "tags"=>["roc", "gsea", "methods", "simulated"], "article_id"=>597818, "categories"=>["Computational Biology", "Infectious Diseases", "Molecular Biology"], "users"=>["Lily Wang", "Bing Zhang", "Russell D. Wolfinger", "Xi Chen"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000115.t001", "stats"=>{"downloads"=>4, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Area_Under_ROC_Curve_AUC_for_the_comparison_of_Mixed_Model_PAGE_and_GSEA_methods_using_simulated_data_/597818", "title"=>"Area Under ROC Curve (AUC) for the comparison of Mixed Model, PAGE and GSEA methods using simulated data.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2008-07-04 02:10:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/927111"], "description"=>"<p>tot_p = proportion of genes with treatment effect added to treatment group in gene–set 1; up_p = among treated genes, the proportion of genes for which positive treatment effect mu was added; 1−up = among treated genes, the proportion of genes for which negative treatment effect –mu was added. See text for details of simulation experiment.</p>", "links"=>[], "tags"=>["curves", "gsea", "simulated"], "article_id"=>597563, "categories"=>["Computational Biology", "Infectious Diseases", "Molecular Biology"], "users"=>["Lily Wang", "Bing Zhang", "Russell D. Wolfinger", "Xi Chen"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000115.g002", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Receiver_Operating_Characteristic_Curves_for_Mixed_Model_GSEA_and_PAGE_using_simulated_data_/597563", "title"=>"Receiver Operating Characteristic Curves for Mixed Model, GSEA and PAGE using simulated data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-07-04 02:06:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/927003"], "description"=>"<p>For each scene, there were 20 simulated datasets, each with 1500 genes assigned to 50 gene-sets, among them only the first gene-set (gene-set 1) include genes associated with outcome by design. The test results from each method were compared with true classification of the gene-sets. The AUC measures the ability of a test to correctly classify whether a gene-set is associated with outcome. In scenes 1–9, when genes were shifted in both directions equally (up_p = 0.5), mixed model outperformed both GSEA and PAGE. In scenes 10–18, when most of genes were shifted in one direction (up_p = 0.8), mixed model and PAGE performed similar, and they both outperformed GSEA, especially when the magnitude of differential expression in gene-set 1 is small (scenes 10, 13, 16).</p>", "links"=>[], "tags"=>["roc", "curves", "gsea", "methods", "simulated"], "article_id"=>597455, "categories"=>["Computational Biology", "Infectious Diseases", "Molecular Biology"], "users"=>["Lily Wang", "Bing Zhang", "Russell D. Wolfinger", "Xi Chen"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000115.g001", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Area_under_ROC_Curves_AUC_for_the_comparison_of_Mixed_Model_PAGE_and_GSEA_methods_using_simulated_data_/597455", "title"=>"Area under ROC Curves (AUC) for the comparison of Mixed Model, PAGE and GSEA methods using simulated data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-07-04 02:04:15"}

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  • {"unique-ip"=>"5", "full-text"=>"4", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"6"}

Relative Metric

{"start_date"=>"2008-01-01T00:00:00Z", "end_date"=>"2008-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[324, 674, 863, 1015, 1146, 1259, 1360, 1452, 1540, 1621, 1704, 1776, 1843, 1905, 1968, 2029, 2097, 2162, 2233, 2289, 2352, 2412, 2476, 2538, 2601, 2655, 2714, 2777, 2841, 2894, 2946, 2998, 3047, 3103, 3151, 3201, 3258, 3317, 3369, 3424, 3471, 3521, 3583, 3625, 3687, 3743, 3788, 3838, 3896, 3953, 4015, 4075, 4148, 4201, 4275, 4347, 4420, 4486, 4535, 4592, 4660, 4731, 4792, 4845, 4901, 4971, 5027, 5089, 5148, 5200, 5261, 5318, 5374]}, {"subject_area"=>"/Medicine and health sciences/Metabolic disorders", "average_usage"=>[321, 630, 811, 972, 1155, 1311, 1392, 1489, 1582, 1676, 1745, 1832, 1909, 1996, 2066, 2116, 2167, 2225, 2297, 2351, 2441, 2512, 2585, 2654, 2739, 2794, 2852, 2909, 2971, 3013, 3056, 3140, 3209, 3269, 3323, 3412, 3468, 3516, 3570, 3616, 3763, 3839, 3897, 3979, 4072, 4167, 4258, 4349, 4433, 4509, 4581, 4654, 4731, 4800, 4916, 4986, 5045, 5129, 5193, 5269, 5313, 5358, 5398, 5430, 5466, 5506, 5529, 5562, 5622, 5670, 6003, 6063, 6112, 6149]}, {"subject_area"=>"/Physical sciences/Mathematics", "average_usage"=>[363, 721, 930, 1104, 1255, 1348, 1433, 1534, 1606, 1669, 1762, 1803, 1875, 1942, 2013, 2068, 2149, 2213, 2280, 2351, 2390, 2439, 2507, 2579, 2644, 2680, 2723, 2786, 2845, 2899, 2943, 2969, 3021, 3084, 3131, 3180, 3220, 3244, 3268, 3333, 3370, 3438, 3491, 3529, 3575, 3618, 3677, 3718, 3807, 3851, 3957, 4082, 4144, 4197, 4231, 4279, 4340, 4383, 4461, 4505, 4555, 4643, 4686, 4771, 4827, 4886, 4932, 4997, 5051, 5123, 5188, 5243, 5314]}]}
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