A Quantitative System for Discriminating Induced Pluripotent Stem Cells, Embryonic Stem Cells and Somatic Cells
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{"title"=>"A Quantitative System for Discriminating Induced Pluripotent Stem Cells, Embryonic Stem Cells and Somatic Cells", "type"=>"journal", "authors"=>[{"first_name"=>"Anyou", "last_name"=>"Wang", "scopus_author_id"=>"7404619690"}, {"first_name"=>"Ying", "last_name"=>"Du", "scopus_author_id"=>"57198409389"}, {"first_name"=>"Qianchuan", "last_name"=>"He", "scopus_author_id"=>"55574220807"}, {"first_name"=>"Chunxiao", "last_name"=>"Zhou", "scopus_author_id"=>"12244663600"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"368342567", "sgr"=>"84873925301", "pmid"=>"23418520", "scopus"=>"2-s2.0-84873925301", "doi"=>"10.1371/journal.pone.0056095", "issn"=>"19326203"}, "id"=>"b4a21e76-94c8-359e-a191-659526227dc5", "abstract"=>"Induced pluripotent stem cells (iPSCs) derived from somatic cells (SCs) and embryonic stem cells (ESCs) provide promising resources for regenerative medicine and medical research, leading to a daily identification of new cell lines. However, an efficient system to discriminate the different types of cell lines is lacking. Here, we develop a quantitative system to discriminate the three cell types, iPSCs, ESCs, and SCs. The system consists of DNA-methylation biomarkers and mathematical models, including an artificial neural network and support vector machines. All biomarkers were unbiasedly selected by calculating an eigengene score derived from analysis of genome-wide DNA methylations. With 30 biomarkers, or even with as few as 3 top biomarkers, this system can discriminate SCs from pluripotent cells (PCs, including ESCs and iPSCs) with almost 100% accuracy. With approximately 100 biomarkers, the system can distinguish ESCs from iPSCs with an accuracy of 95%. This robust system performs precisely with raw data without normalization as well as with converted data in which the continuous methylation levels are accounted. Strikingly, this system can even accurately predict new samples generated from different microarray platforms and the next-generation sequencing. The subtypes of cells, such as female and male iPSCs and fetal and adult SCs, can also be discriminated with this method. Thus, this novel quantitative system works as an accurate framework for discriminating the three cell types, iPSCs, ESCs, and SCs. This strategy also supports the notion that DNA-methylation generally varies among the three cell types.", "link"=>"http://www.mendeley.com/research/quantitative-system-discriminating-induced-pluripotent-stem-cells-embryonic-stem-cells-somatic-cells", "reader_count"=>30, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Researcher"=>7, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>6, "Student > Master"=>7, "Student > Bachelor"=>4, "Professor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Researcher"=>7, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>6, "Student > Master"=>7, "Student > Bachelor"=>4, "Professor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>3, "Materials Science"=>2, "Agricultural and Biological Sciences"=>18, "Medicine and Dentistry"=>5, "Economics, Econometrics and Finance"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Materials Science"=>{"Materials Science"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>5}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>18}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}}, "reader_count_by_country"=>{"United States"=>1, "Japan"=>1, "Portugal"=>1}, "group_count"=>2}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/485173", "https://ndownloader.figshare.com/files/485174", "https://ndownloader.figshare.com/files/485176", "https://ndownloader.figshare.com/files/485177", "https://ndownloader.figshare.com/files/485178", "https://ndownloader.figshare.com/files/485179", "https://ndownloader.figshare.com/files/485180"], "description"=>"<div><p>Induced pluripotent stem cells (iPSCs) derived from somatic cells (SCs) and embryonic stem cells (ESCs) provide promising resources for regenerative medicine and medical research, leading to a daily identification of new cell lines. However, an efficient system to discriminate the different types of cell lines is lacking. Here, we develop a quantitative system to discriminate the three cell types, iPSCs, ESCs, and SCs. The system consists of DNA-methylation biomarkers and mathematical models, including an artificial neural network and support vector machines. All biomarkers were unbiasedly selected by calculating an eigengene score derived from analysis of genome-wide DNA methylations. With 30 biomarkers, or even with as few as 3 top biomarkers, this system can discriminate SCs from pluripotent cells (PCs, including ESCs and iPSCs) with almost 100% accuracy. With approximately 100 biomarkers, the system can distinguish ESCs from iPSCs with an accuracy of 95%. This robust system performs precisely with raw data without normalization as well as with converted data in which the continuous methylation levels are accounted. Strikingly, this system can even accurately predict new samples generated from different microarray platforms and the next-generation sequencing. The subtypes of cells, such as female and male iPSCs and fetal and adult SCs, can also be discriminated with this method. Thus, this novel quantitative system works as an accurate framework for discriminating the three cell types, iPSCs, ESCs, and SCs. This strategy also supports the notion that DNA-methylation generally varies among the three cell types.</p> </div>", "links"=>[], "tags"=>["quantitative", "discriminating", "induced", "pluripotent", "embryonic", "cells", "somatic", "cells"], "article_id"=>156772, "categories"=>["Molecular Biology", "Biological Sciences", "Cell Biology", "Genetics", "Developmental Biology"], "users"=>["Anyou Wang", "Ying Du", "Qianchuan He", "Chunxiao Zhou"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0056095.s001", "https://dx.doi.org/10.1371/journal.pone.0056095.s002", "https://dx.doi.org/10.1371/journal.pone.0056095.s003", "https://dx.doi.org/10.1371/journal.pone.0056095.s004", "https://dx.doi.org/10.1371/journal.pone.0056095.s005", "https://dx.doi.org/10.1371/journal.pone.0056095.s006", "https://dx.doi.org/10.1371/journal.pone.0056095.s007"], "stats"=>{"downloads"=>10, "page_views"=>42, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/A_Quantitative_System_for_Discriminating_Induced_Pluripotent_Stem_Cells_Embryonic_Stem_Cells_and_Somatic_Cells__/156772", "title"=>"A Quantitative System for Discriminating Induced Pluripotent Stem Cells, Embryonic Stem Cells and Somatic Cells", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-02-13 01:52:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/490380"], "description"=>"<p>A, a high correction relationship exists between methylation percentage measured from sequencing and the beta value measured from Illumina microarray. B, Our system discriminates the three cell types with high accuracy with converted data. For visualization purposes, only percentage of NNET was shown here due to its similarity with SVM and the high correlation between accuracy percentage and kappa. This practice was also applied to following figures in this study.</p>", "links"=>[], "tags"=>["discriminating", "performs", "converted"], "article_id"=>160903, "categories"=>["Molecular Biology", "Biological Sciences", "Cell Biology", "Genetics", "Developmental Biology"], "users"=>["Anyou Wang", "Ying Du", "Qianchuan He", "Chunxiao Zhou"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056095.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_discriminating_system_performs_precisely_on_converted_data_/160903", "title"=>"The discriminating system performs precisely on converted data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-13 00:15:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/490737"], "description"=>"<p>Left panel, top 10 biomarkers for discriminating SCs from PCs. Right panel, top 10 biomarkers for discriminating iPSCs from ESCs. Please see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056095#pone.0056095.s002\" target=\"_blank\">Table S2</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056095#pone.0056095.s003\" target=\"_blank\">S3</a>, and S4 for complete list used in this study.</p>", "links"=>[], "tags"=>["biomarker"], "article_id"=>161261, "categories"=>["Molecular Biology", "Biological Sciences", "Cell Biology", "Genetics", "Developmental Biology"], "users"=>["Anyou Wang", "Ying Du", "Qianchuan He", "Chunxiao Zhou"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056095.t001", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Top_biomarker_list_/161261", "title"=>"Top biomarker list.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-13 00:21:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/490709"], "description"=>"<p>Prediction profiling of our system.</p>", "links"=>[], "tags"=>["profiling"], "article_id"=>161234, "categories"=>["Molecular Biology", "Biological Sciences", "Cell Biology", "Genetics", "Developmental Biology"], "users"=>["Anyou Wang", "Ying Du", "Qianchuan He", "Chunxiao Zhou"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056095.t002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prediction_profiling_of_our_system_/161234", "title"=>"Prediction profiling of our system.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-13 00:20:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/490235"], "description"=>"<p>Accuracy was measured as kappa value and accuracy percentage, shown on the Y-axis. The top panel A represents SCs discriminating from PCs and the bottom panel B represents ESCs from iPSCs. The X-axis represents marker number, from 1 marker to 50 markers in SCs versus PCs panel (top), and from 1 to 200 markers in ESCs versus iPSCs panel (bottom B). Only data for 50 and 200 markers for these two groups are shown because the system became a static state after that level.</p>", "links"=>[], "tags"=>["dna", "methylation", "biomarker"], "article_id"=>160757, "categories"=>["Molecular Biology", "Biological Sciences", "Cell Biology", "Genetics", "Developmental Biology"], "users"=>["Anyou Wang", "Ying Du", "Qianchuan He", "Chunxiao Zhou"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056095.g002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_DNA_methylation_biomarker_system_/160757", "title"=>"Performance of DNA methylation biomarker system.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-13 00:12:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/490075"], "description"=>"<p>All methylation sites measured by microarray were used to profile the overall methylation patterns of the three cell types, iPSCs, ESCs, and SCs. A, unsupervised clustering analysis revealed that SCs were separated from PCs (iPSCs and ESCs). In the PCs subgroup, most ESCs were separated from iPSCs. B, Correspondence analysis classified three cell types, SCs, iPSCs, and ESCs. SCs and PCs were separated in first component while most of ESCs and iPSCs were separated in second component. C, iPSCs and ESCs were further classified by correspondence analysis in 3D space. For visualization purposes, only one subset of data was shown here.</p>", "links"=>[], "tags"=>["methylation", "profiling"], "article_id"=>160599, "categories"=>["Molecular Biology", "Biological Sciences", "Cell Biology", "Genetics", "Developmental Biology"], "users"=>["Anyou Wang", "Ying Du", "Qianchuan He", "Chunxiao Zhou"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056095.g001", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overall_methylation_profiling_of_three_cell_types_/160599", "title"=>"Overall methylation profiling of three cell types.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-13 00:09:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/490531"], "description"=>"<p>Our system reaches the similar discriminating power as that with normalized data.</p>", "links"=>[], "tags"=>["genetics and genomics", "molecular biology", "Computational biology", "cell biology", "developmental biology"], "article_id"=>161050, "categories"=>["Molecular Biology", "Biological Sciences", "Cell Biology", "Genetics", "Developmental Biology"], "users"=>["Anyou Wang", "Ying Du", "Qianchuan He", "Chunxiao Zhou"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056095.g004", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Our_system_works_accurately_with_raw_data_/161050", "title"=>"Our system works accurately with raw data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-13 00:17:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/490618"], "description"=>"<p>A and B denote correspondence analysis to classify subtypes of cells. A, the subtype of female and male iPSCs. B, the subtype of fetal and adult SCs. C and D show the accuracy of discriminating subtypes of cells. C, the subtype of female and male iPSCs. D, the subtype of fetal and adult SCs.</p>", "links"=>[], "tags"=>["subtype"], "article_id"=>161147, "categories"=>["Molecular Biology", "Biological Sciences", "Cell Biology", "Genetics", "Developmental Biology"], "users"=>["Anyou Wang", "Ying Du", "Qianchuan He", "Chunxiao Zhou"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056095.g005", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cell_subtype_discrimination_/161147", "title"=>"Cell subtype discrimination.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-13 00:19:07"}

PMC Usage Stats | Further Information

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

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/Cell biology", "average_usage"=>[272, 472, 600, 713, 815, 911, 1004, 1094, 1185, 1273, 1358, 1441]}, {"subject_area"=>"/Biology and life sciences/Neuroscience", "average_usage"=>[261, 444, 554, 655, 748, 834, 923, 1004, 1089, 1170, 1244, 1315, 1380]}, {"subject_area"=>"/Computer and information sciences", "average_usage"=>[297, 488, 616, 724, 828, 939, 1038, 1127, 1223, 1311, 1393, 1479, 1556]}, {"subject_area"=>"/Computer and information sciences/Information technology", "average_usage"=>[265, 446, 547, 656, 751, 809, 898, 978, 1077, 1216, 1295, 1357, 1403, 1469]}, {"subject_area"=>"/Medicine and health sciences", "average_usage"=>[264, 460, 584, 692, 794, 887, 978, 1067, 1154, 1241, 1328, 1408, 1474]}]}
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