Dispersion Estimation and Its Effect on Test Performance in RNA-seq Data Analysis: A Simulation-Based Comparison of Methods
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{"title"=>"Dispersion estimation and its effect on test performance in RNA-seq data analysis: A simulation-based comparison of methods", "type"=>"journal", "authors"=>[{"first_name"=>"William Michael", "last_name"=>"Landau", "scopus_author_id"=>"36192995900"}, {"first_name"=>"Peng", "last_name"=>"Liu", "scopus_author_id"=>"57190027870"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84892599763", "doi"=>"10.1371/journal.pone.0081415", "issn"=>"19326203", "pui"=>"372132640", "pmid"=>"24349066", "scopus"=>"2-s2.0-84892599763"}, "id"=>"d503228f-0052-3c73-8175-630f22ff6618", "abstract"=>"A central goal of RNA sequencing (RNA-seq) experiments is to detect differentially expressed genes. In the ubiquitous negative binomial model for RNA-seq data, each gene is given a dispersion parameter, and correctly estimating these dispersion parameters is vital to detecting differential expression. Since the dispersions control the variances of the gene counts, underestimation may lead to false discovery, while overestimation may lower the rate of true detection. After briefly reviewing several popular dispersion estimation methods, this article describes a simulation study that compares them in terms of point estimation and the effect on the performance of tests for differential expression. The methods that maximize the test performance are the ones that use a moderate degree of dispersion shrinkage: the DSS, Tagwise wqCML, and Tagwise APL. In practical RNA-seq data analysis, we recommend using one of these moderate-shrinkage methods with the QLShrink test in QuasiSeq R package.", "link"=>"http://www.mendeley.com/research/dispersion-estimation-effect-test-performance-rnaseq-data-analysis-simulationbased-comparison-method", "reader_count"=>33, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Researcher"=>6, "Student > Ph. D. Student"=>9, "Student > Postgraduate"=>2, "Other"=>3, "Student > Master"=>3, "Student > Bachelor"=>4, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Researcher"=>6, "Student > Ph. D. Student"=>9, "Student > Postgraduate"=>2, "Other"=>3, "Student > Master"=>3, "Student > Bachelor"=>4, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>3, "Mathematics"=>2, "Agricultural and Biological Sciences"=>16, "Medicine and Dentistry"=>1, "Chemical Engineering"=>2, "Physics and Astronomy"=>1, "Computer Science"=>4, "Immunology and Microbiology"=>2}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>16}, "Computer Science"=>{"Computer Science"=>4}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>1}, "Chemical Engineering"=>{"Chemical Engineering"=>2}}, "reader_count_by_country"=>{"United States"=>1, "Brazil"=>1}, "group_count"=>3}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1308444"], "description"=>"<p>Boxplots of AUC calculated based on 30 pseudo-datasets are shown for each combination of dispersion estimation method and test for differential expression.</p>", "links"=>[], "tags"=>["areas", "roc"], "article_id"=>872416, "categories"=>["Biological Sciences"], "users"=>["William Michael Landau", "Peng Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0081415.g006", "stats"=>{"downloads"=>2, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulation_setting_I_areas_under_ROC_curves_/872416", "title"=>"Simulation setting I: areas under ROC curves.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-09 03:44:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/1308446"], "description"=>"<p>Boxplots of AUC calculated based on 30 pseudo-datasets are shown for each combination of dispersion estimation method and test for differential expression.</p>", "links"=>[], "tags"=>["areas", "roc"], "article_id"=>872418, "categories"=>["Biological Sciences"], "users"=>["William Michael Landau", "Peng Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0081415.g007", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulation_setting_II_areas_under_ROC_curves_/872418", "title"=>"Simulation setting II: areas under ROC curves.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-09 03:44:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/1308448"], "description"=>"<p>Boxplots of AUC calculated based on 30 pseudo-datasets are shown for each combination of dispersion estimation method and test for differential expression.</p>", "links"=>[], "tags"=>["areas", "roc"], "article_id"=>872420, "categories"=>["Biological Sciences"], "users"=>["William Michael Landau", "Peng Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0081415.g008", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulation_setting_III_areas_under_ROC_curves_/872420", "title"=>"Simulation setting III: areas under ROC curves.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-09 03:44:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/1308449"], "description"=>"<p>Boxplots of AUC calculated based on 30 pseudo-datasets are shown for each combination of dispersion estimation method and test for differential expression.</p>", "links"=>[], "tags"=>["areas", "roc"], "article_id"=>872421, "categories"=>["Biological Sciences"], "users"=>["William Michael Landau", "Peng Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0081415.g009", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulation_setting_IV_areas_under_ROC_curves_/872421", "title"=>"Simulation setting IV: areas under ROC curves.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-09 03:44:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/1308450"], "description"=>"<p>Boxplots of AUC calculated based on 30 pseudo-datasets are shown for each combination of dispersion estimation method and test for differential expression.</p>", "links"=>[], "tags"=>["areas", "roc"], "article_id"=>872422, "categories"=>["Biological Sciences"], "users"=>["William Michael Landau", "Peng Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0081415.g010", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulation_setting_V_areas_under_ROC_curves_/872422", "title"=>"Simulation setting V: areas under ROC curves.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-09 03:44:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/1308452"], "description"=>"<p>Boxplots of AUC calculated based on 30 pseudo-datasets are shown for each combination of dispersion estimation method and test for differential expression.</p>", "links"=>[], "tags"=>["areas", "roc"], "article_id"=>872424, "categories"=>["Biological Sciences"], "users"=>["William Michael Landau", "Peng Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0081415.g011", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulation_setting_VI_areas_under_ROC_curves_/872424", "title"=>"Simulation setting VI: areas under ROC curves.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-09 03:44:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/1308453"], "description"=>"<p>See the end of the Methods section for details.</p>", "links"=>[], "tags"=>[], "article_id"=>872425, "categories"=>["Biological Sciences"], "users"=>["William Michael Landau", "Peng Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0081415.t002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulation_Settings_/872425", "title"=>"Simulation Settings.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-12-09 03:44:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/1308454"], "description"=>"<p>For the full implementation, please see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081415#pone.0081415.s001\" target=\"_blank\">Simulation Code S1</a>.</p>", "links"=>[], "tags"=>["packages", "simulation"], "article_id"=>872426, "categories"=>["Biological Sciences"], "users"=>["William Michael Landau", "Peng Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0081415.t001", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_R_Packages_Required_for_the_Simulation_Study_8217_s_Implementation_/872426", "title"=>"R Packages Required for the Simulation Study’s Implementation.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-12-09 03:44:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/1308455"], "description"=>"<div><p>A central goal of RNA sequencing (RNA-seq) experiments is to detect differentially expressed genes. In the ubiquitous negative binomial model for RNA-seq data, each gene is given a dispersion parameter, and correctly estimating these dispersion parameters is vital to detecting differential expression. Since the dispersions control the variances of the gene counts, underestimation may lead to false discovery, while overestimation may lower the rate of true detection. After briefly reviewing several popular dispersion estimation methods, this article describes a simulation study that compares them in terms of point estimation and the effect on the performance of tests for differential expression. The methods that maximize the test performance are the ones that use a moderate degree of dispersion shrinkage: the DSS, Tagwise wqCML, and Tagwise APL. In practical RNA-seq data analysis, we recommend using one of these moderate-shrinkage methods with the QLShrink test in QuasiSeq R package.</p></div>", "links"=>[], "tags"=>["estimation", "rna-seq", "simulation-based"], "article_id"=>872427, "categories"=>["Biological Sciences"], "users"=>["William Michael Landau", "Peng Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0081415", "stats"=>{"downloads"=>6, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Dispersion_Estimation_and_Its_Effect_on_Test_Performance_in_RNA_seq_Data_Analysis_A_Simulation_Based_Comparison_of_Methods_/872427", "title"=>"Dispersion Estimation and Its Effect on Test Performance in RNA-seq Data Analysis: A Simulation-Based Comparison of Methods", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-12-09 03:44:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/1308433"], "description"=>"<p>Hammer data and Hammer-generated pseudo-data are in blue, while Pickrell data and Pickrell-generated pseudo-data are shown in black. The top two panels show the gene-wise log geometric mean counts and log dispersion estimates, estimated with the QL method, for the Hammer and Pickrell datasets. The bottom two panels plot the analogous quantities for example simulated pseudo-datasets, except that the log dispersions plotted are the true dispersions used to simulate the pseudo-counts and the gene-wise log geometric mean counts are the ’s (log geometric mean counts from the real data) used in the simulations. The vertical bar at around in the plots of the log dispersions is an artifact of the QL method, which sets extremely low dispersions (i.e., dispersions of non-overdispersed genes) to a common minimum value.</p>", "links"=>[], "tags"=>[], "article_id"=>872405, "categories"=>["Biological Sciences"], "users"=>["William Michael Landau", "Peng Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0081415.g001", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_look_at_the_data_/872405", "title"=>"A look at the data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-09 03:44:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/1308435"], "description"=>"<p>The top two panels show the relationship between the log QL-method-estimated dispersions and the gene-wise log geometric mean counts of the Hammer and Pickrell datasets. The bottom two plot the analogous quantities for example simulated pseudo-datasets, except that the log dispersions plotted are the true log dispersions used to simulate the pseudo-counts (i.e., the ’s) and the gene-wise log geometric mean counts are the ’s used in the simulations. Bins in these two-dimensional histograms are shaded by their log frequency.</p>", "links"=>[], "tags"=>[], "article_id"=>872407, "categories"=>["Biological Sciences"], "users"=>["William Michael Landau", "Peng Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0081415.g002", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Dispersion_mean_relationships_/872407", "title"=>"Dispersion-mean relationships.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-09 03:44:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/1308436"], "description"=>"<p>Mean squared error of the transformed dispersions.</p>", "links"=>[], "tags"=>["squared", "transformed"], "article_id"=>872408, "categories"=>["Biological Sciences"], "users"=>["William Michael Landau", "Peng Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0081415.g003", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_squared_error_of_the_transformed_dispersions_/872408", "title"=>"Mean squared error of the transformed dispersions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-09 03:44:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/1308439"], "description"=>"<p>Dispersions with gene-wise log geometric mean counts below the median (log mean from 2.17 to 1.63) are shown in black, while those above the median (log mean from 1.63 to 10.6) are shown in light blue. Overlapping points are shown in dark blue. Results for simulation settings I and III are similar.</p>", "links"=>[], "tags"=>["dispersions"], "article_id"=>872411, "categories"=>["Biological Sciences"], "users"=>["William Michael Landau", "Peng Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0081415.g004", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulation_setting_II_estimated_vs_true_dispersions_for_an_example_pseudo_dataset_/872411", "title"=>"Simulation setting II: estimated vs true dispersions for an example pseudo-dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-09 03:44:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/1308441"], "description"=>"<p>Dispersions with gene-wise log geometric mean counts below the median (log mean from −2.17 to 4.49) are shown in black, while those above the median (log mean from 4.49 to 12.3) are shown in light blue. Overlapping points are shown in dark blue. Results for simulations IV and VI are similar.</p>", "links"=>[], "tags"=>["dispersions"], "article_id"=>872413, "categories"=>["Biological Sciences"], "users"=>["William Michael Landau", "Peng Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0081415.g005", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulation_setting_V_estimated_vs_true_dispersions_for_an_example_pseudo_dataset_/872413", "title"=>"Simulation setting V: estimated vs true dispersions for an example pseudo-dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-09 03:44:29"}

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  • {"unique-ip"=>"17", "full-text"=>"18", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"13", "full-text"=>"15", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"19", "full-text"=>"19", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}
  • {"unique-ip"=>"13", "full-text"=>"15", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"11", "full-text"=>"12", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
  • {"unique-ip"=>"27", "full-text"=>"28", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}
  • {"unique-ip"=>"19", "full-text"=>"14", "pdf"=>"8", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}

Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[269, 466, 588, 697, 800, 896, 988, 1076, 1165, 1254, 1340, 1417]}, {"subject_area"=>"/Biology and life sciences/Computational biology", "average_usage"=>[295, 511, 651, 775, 882, 992, 1100, 1201, 1304, 1400, 1486, 1570, 1650]}, {"subject_area"=>"/Physical sciences", "average_usage"=>[254, 431, 547, 651, 748, 842, 932, 1017, 1098, 1178, 1259, 1336, 1404]}, {"subject_area"=>"/Physical sciences/Mathematics", "average_usage"=>[259, 431, 541, 639, 727, 816, 898, 980, 1061, 1136, 1214, 1294, 1356]}]}
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