Assessing Significance in High-Throughput Experiments by Sequential Goodness of Fit and q-Value Estimation
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{"title"=>"Assessing significance in High-Throughput experiments by sequential goodness of fit and Q-Value estimation", "type"=>"journal", "authors"=>[{"first_name"=>"Antonio", "last_name"=>"Carvajal-Rodriguez", "scopus_author_id"=>"8850713200"}, {"first_name"=>"Jacobo", "last_name"=>"de Uña-Alvarez", "scopus_author_id"=>"6603553828"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-80052595934", "sgr"=>"80052595934", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0024700", "pmid"=>"21931819", "isbn"=>"1932-6203", "pui"=>"362523756"}, "id"=>"9b090bf8-b7f1-385d-bf54-754645841fb5", "abstract"=>"We developed a new multiple hypothesis testing adjustment called SGoF+ implemented as a sequential goodness of fit metatest which is a modification of a previous algorithm, SGoF, taking advantage of the information of the distribution of p-values in order to fix the rejection region. The new method uses a discriminant rule based on the maximum distance between the uniform distribution of p-values and the observed one, to set the null for a binomial test. This new approach shows a better power/pFDR ratio than SGoF. In fact SGoF+ automatically sets the threshold leading to the maximum power and the minimum false non-discovery rate inside the SGoF' family of algorithms. Additionally, we suggest combining the information provided by SGoF+ with the estimate of the FDR that has been committed when rejecting a given set of nulls. We study different positive false discovery rate, pFDR, estimation methods to combine q-value estimates jointly with the information provided by the SGoF+ method. Simulations suggest that the combination of SGoF+ metatest with the q-value information is an interesting strategy to deal with multiple testing issues. These techniques are provided in the latest version of the SGoF+ software freely available at http://webs.uvigo.es/acraaj/SGoF.htm.", "link"=>"http://www.mendeley.com/research/assessing-significance-highthroughput-experiments-sequential-goodness-fit-qvalue-estimation", "reader_count"=>28, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>1, "Student > Doctoral Student"=>2, "Researcher"=>5, "Student > Ph. D. Student"=>10, "Student > Master"=>6, "Other"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>1, "Student > Doctoral Student"=>2, "Researcher"=>5, "Student > Ph. D. Student"=>10, "Student > Master"=>6, "Other"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Environmental Science"=>2, "Biochemistry, Genetics and Molecular Biology"=>3, "Medicine and Dentistry"=>1, "Agricultural and Biological Sciences"=>17, "Social Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Social Sciences"=>{"Social Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>17}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}, "Unspecified"=>{"Unspecified"=>4}, "Environmental Science"=>{"Environmental Science"=>2}}, "reader_count_by_country"=>{"Republic of Singapore"=>1, "India"=>1}, "group_count"=>0}

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Figshare

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  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"12"}
  • {"unique-ip"=>"10", "full-text"=>"11", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"3"}
  • {"unique-ip"=>"5", "full-text"=>"6", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"1"}
  • {"unique-ip"=>"14", "full-text"=>"14", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"5"}
  • {"unique-ip"=>"4", "full-text"=>"4", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"4"}
  • {"unique-ip"=>"7", "full-text"=>"8", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"6"}
  • {"unique-ip"=>"7", "full-text"=>"4", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2018", "month"=>"7"}
  • {"unique-ip"=>"10", "full-text"=>"10", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"8"}
  • {"unique-ip"=>"7", "full-text"=>"6", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
  • {"unique-ip"=>"11", "full-text"=>"11", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"7", "full-text"=>"11", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"4", "full-text"=>"5", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
  • {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"7", "full-text"=>"6", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"11", "full-text"=>"8", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}

Relative Metric

{"start_date"=>"2011-01-01T00:00:00Z", "end_date"=>"2011-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[304, 568, 702, 818, 927, 1027, 1118, 1206, 1285, 1357, 1427, 1500, 1564, 1636, 1705, 1773, 1840, 1909, 1974, 2039, 2106, 2170, 2234, 2296, 2358, 2423, 2484, 2546, 2606, 2673, 2734, 2795, 2857, 2921, 2984, 3046, 3100]}, {"subject_area"=>"/Biology and life sciences/Computational biology", "average_usage"=>[348, 636, 778, 900, 1011, 1120, 1220, 1303, 1392, 1471, 1554, 1628, 1704, 1776, 1855, 1934, 2016, 2084, 2149, 2232, 2303, 2379, 2465, 2539, 2593, 2657, 2722, 2786, 2856, 2918, 2989, 3057, 3124, 3185, 3234, 3300, 3343]}, {"subject_area"=>"/Biology and life sciences/Neuroscience", "average_usage"=>[306, 540, 664, 768, 864, 959, 1036, 1121, 1199, 1277, 1341, 1411, 1477, 1543, 1609, 1675, 1739, 1802, 1868, 1933, 1995, 2050, 2107, 2164, 2220, 2277, 2327, 2374, 2436, 2488, 2542, 2594, 2650, 2705, 2769, 2817, 2859]}, {"subject_area"=>"/Biology and life sciences/Organisms", "average_usage"=>[316, 571, 699, 812, 915, 1008, 1095, 1180, 1255, 1319, 1393, 1461, 1528, 1595, 1662, 1729, 1802, 1868, 1937, 1997, 2062, 2130, 2192, 2254, 2324, 2386, 2450, 2510, 2579, 2642, 2716, 2778, 2842, 2908, 2979, 3042, 3095]}, {"subject_area"=>"/Computer and information sciences", "average_usage"=>[355, 609, 735, 840, 944, 1029, 1109, 1193, 1255, 1320, 1391, 1469, 1540, 1608, 1683, 1753, 1824, 1905, 1967, 2042, 2117, 2189, 2240, 2300, 2392, 2452, 2509, 2569, 2635, 2710, 2772, 2846, 2882, 2956, 3014, 3086, 3139]}, {"subject_area"=>"/Computer and information sciences/Computer software", "average_usage"=>[341, 663, 811, 965, 1077, 1200, 1341, 1450, 1568, 1649, 1737, 1812, 1890, 1974, 2038, 2175, 2297, 2390, 2468, 2545, 2621, 2743, 2885, 2982, 3052, 3124, 3198, 3263, 3343, 3419, 3471, 3533, 3589, 3662, 3748, 3825, 3909, 3976, 4029]}, {"subject_area"=>"/Social sciences", "average_usage"=>[340, 584, 708, 809, 915, 1005, 1089, 1174, 1254, 1334, 1408, 1478, 1563, 1648, 1716, 1791, 1847, 1915, 1979, 2044, 2105, 2163, 2218, 2297, 2382, 2436, 2493, 2553, 2618, 2682, 2741, 2786, 2847, 2937, 2989, 3039, 3102]}]}
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Net::HTTPTooManyRequests

Source
Scopus
Time
2019-07-20 08:58:05 UTC
Target URL
https://api.elsevier.com/content/search/index:SCOPUS?query=DOI(10.1371%2Fjournal.pone.0024700)
Trace

/app/models/concerns/networkable.rb:21:in `get_result'
/app/models/source.rb:165:in `get_data'
/app/models/retrieval_status.rb:47:in `perform_get_data'
/app/jobs/source_job.rb:52:in `block (2 levels) in perform'
/app/jobs/source_job.rb:51:in `block in perform'
/app/jobs/source_job.rb:35:in `each'
/app/jobs/source_job.rb:35:in `perform'