SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis
Publication Date
November 24, 2015
Journal
PLOS Computational Biology
Authors
Minzhe Guo, Hui Wang, S. Steven Potter, Jeffrey A. Whitsett, et al
Volume
11
Issue
11
Pages
e1004575
DOI
https://dx.plos.org/10.1371/journal.pcbi.1004575
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004575
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/26600239
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4658017
Europe PMC
http://europepmc.org/abstract/MED/26600239
Web of Science
000365801600034
Scopus
84949293695
Mendeley
http://www.mendeley.com/research/sincera-pipeline-singlecell-rnaseq-profiling-analysis
Events
Loading … Spinner

Mendeley | Further Information

{"title"=>"SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis", "type"=>"journal", "authors"=>[{"first_name"=>"Minzhe", "last_name"=>"Guo", "scopus_author_id"=>"35113278100"}, {"first_name"=>"Hui", "last_name"=>"Wang", "scopus_author_id"=>"56590779600"}, {"first_name"=>"S. Steven", "last_name"=>"Potter", "scopus_author_id"=>"7102952269"}, {"first_name"=>"Jeffrey A.", "last_name"=>"Whitsett", "scopus_author_id"=>"55871120724"}, {"first_name"=>"Yan", "last_name"=>"Xu", "scopus_author_id"=>"57192964311"}], "year"=>2015, "source"=>"PLoS Computational Biology", "identifiers"=>{"issn"=>"15537358", "doi"=>"10.1371/journal.pcbi.1004575", "sgr"=>"84949293695", "scopus"=>"2-s2.0-84949293695", "isbn"=>"1097-4172 (Electronic) 0092-8674 (Linking)", "pmid"=>"26600239", "pui"=>"607184055"}, "id"=>"11549dc3-788b-3392-9481-2334f30aaa7b", "abstract"=>"A major challenge in developmental biology is to understand the genetic and cellular processes/programs driving organ formation and differentiation of the diverse cell types that comprise the embryo. While recent studies using single cell transcriptome analysis illustrate the power to measure and understand cellular heterogeneity in complex biological systems, processing large amounts of RNA-seq data from heterogeneous cell populations creates the need for readily accessible tools for the analysis of single-cell RNA-seq (scRNA-seq) profiles. The present study presents a generally applicable analytic pipeline (SINCERA: a computational pipeline for SINgle CEll RNA-seq profiling Analysis) for processing scRNA-seq data from a whole organ or sorted cells. The pipeline supports the analysis for: 1) the distinction and identification of major cell types; 2) the identification of cell type specific gene signatures; and 3) the determination of driving forces of given cell types. We applied this pipeline to the RNA-seq analysis of single cells isolated from embryonic mouse lung at E16.5. Through the pipeline analysis, we distinguished major cell types of fetal mouse lung, including epithelial, endothelial, smooth muscle, pericyte, and fibroblast-like cell types, and identified cell type specific gene signatures, bioprocesses, and key regulators. SINCERA is implemented in R, licensed under the GNU General Public License v3, and freely available from CCHMC PBGE website, https://research.cchmc.org/pbge/sincera.html.", "link"=>"http://www.mendeley.com/research/sincera-pipeline-singlecell-rnaseq-profiling-analysis", "reader_count"=>235, "reader_count_by_academic_status"=>{"Unspecified"=>8, "Professor > Associate Professor"=>8, "Researcher"=>62, "Student > Doctoral Student"=>8, "Student > Ph. D. Student"=>74, "Student > Postgraduate"=>10, "Student > Master"=>21, "Other"=>15, "Student > Bachelor"=>18, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>2, "Professor"=>6}, "reader_count_by_user_role"=>{"Unspecified"=>8, "Professor > Associate Professor"=>8, "Researcher"=>62, "Student > Doctoral Student"=>8, "Student > Ph. D. Student"=>74, "Student > Postgraduate"=>10, "Student > Master"=>21, "Other"=>15, "Student > Bachelor"=>18, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>2, "Professor"=>6}, "reader_count_by_subject_area"=>{"Unspecified"=>10, "Agricultural and Biological Sciences"=>98, "Chemistry"=>3, "Computer Science"=>15, "Engineering"=>6, "Biochemistry, Genetics and Molecular Biology"=>62, "Mathematics"=>11, "Medicine and Dentistry"=>13, "Neuroscience"=>6, "Pharmacology, Toxicology and Pharmaceutical Science"=>2, "Physics and Astronomy"=>1, "Psychology"=>1, "Immunology and Microbiology"=>7}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>13}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>1}, "Mathematics"=>{"Mathematics"=>11}, "Unspecified"=>{"Unspecified"=>10}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>2}, "Engineering"=>{"Engineering"=>6}, "Chemistry"=>{"Chemistry"=>3}, "Neuroscience"=>{"Neuroscience"=>6}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>7}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>98}, "Computer Science"=>{"Computer Science"=>15}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>62}}, "reader_count_by_country"=>{"Sweden"=>3, "United States"=>6, "China"=>1, "Japan"=>2, "Denmark"=>2, "United Kingdom"=>3, "France"=>1, "Australia"=>1, "Switzerland"=>1}, "group_count"=>11}

CrossRef

Scopus | Further Information

{"@_fa"=>"true", "link"=>[{"@_fa"=>"true", "@ref"=>"self", "@href"=>"https://api.elsevier.com/content/abstract/scopus_id/84949293695"}, {"@_fa"=>"true", "@ref"=>"author-affiliation", "@href"=>"https://api.elsevier.com/content/abstract/scopus_id/84949293695?field=author,affiliation"}, {"@_fa"=>"true", "@ref"=>"scopus", "@href"=>"https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84949293695&origin=inward"}, {"@_fa"=>"true", "@ref"=>"scopus-citedby", "@href"=>"https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=84949293695&origin=inward"}], "prism:url"=>"https://api.elsevier.com/content/abstract/scopus_id/84949293695", "dc:identifier"=>"SCOPUS_ID:84949293695", "eid"=>"2-s2.0-84949293695", "dc:title"=>"SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis", "dc:creator"=>"Guo M.", "prism:publicationName"=>"PLoS Computational Biology", "prism:issn"=>"1553734X", "prism:eIssn"=>"15537358", "prism:volume"=>"11", "prism:issueIdentifier"=>"11", "prism:pageRange"=>nil, "prism:coverDate"=>"2015-11-01", "prism:coverDisplayDate"=>"November 2015", "prism:doi"=>"10.1371/journal.pcbi.1004575", "citedby-count"=>"112", "affiliation"=>[{"@_fa"=>"true", "affilname"=>"Cincinnati Children's Hospital Medical Center", "affiliation-city"=>"Cincinnati", "affiliation-country"=>"United States"}, {"@_fa"=>"true", "affilname"=>"University of Cincinnati", "affiliation-city"=>"Cincinnati", "affiliation-country"=>"United States"}], "pubmed-id"=>"26600239", "prism:aggregationType"=>"Journal", "subtype"=>"ar", "subtypeDescription"=>"Article", "article-number"=>"e1004575", "source-id"=>"4000151810", "openaccess"=>"1", "openaccessFlag"=>true}

Article Coverage Curated

Facebook

  • {"url"=>"http%3A%2F%2Fjournals.plos.org%2Fploscompbiol%2Farticle%3Fid%3D10.1371%252Fjournal.pcbi.1004575", "share_count"=>5, "like_count"=>0, "comment_count"=>0, "click_count"=>0, "total_count"=>5}

Twitter

Counter

  • {"month"=>"11", "year"=>"2015", "pdf_views"=>"195", "xml_views"=>"2", "html_views"=>"1258"}
  • {"month"=>"12", "year"=>"2015", "pdf_views"=>"282", "xml_views"=>"2", "html_views"=>"1670"}
  • {"month"=>"1", "year"=>"2016", "pdf_views"=>"136", "xml_views"=>"0", "html_views"=>"572"}
  • {"month"=>"2", "year"=>"2016", "pdf_views"=>"115", "xml_views"=>"0", "html_views"=>"460"}
  • {"month"=>"3", "year"=>"2016", "pdf_views"=>"87", "xml_views"=>"0", "html_views"=>"403"}
  • {"month"=>"4", "year"=>"2016", "pdf_views"=>"114", "xml_views"=>"0", "html_views"=>"406"}
  • {"month"=>"5", "year"=>"2016", "pdf_views"=>"80", "xml_views"=>"0", "html_views"=>"271"}
  • {"month"=>"6", "year"=>"2016", "pdf_views"=>"94", "xml_views"=>"0", "html_views"=>"339"}
  • {"month"=>"7", "year"=>"2016", "pdf_views"=>"66", "xml_views"=>"0", "html_views"=>"263"}
  • {"month"=>"8", "year"=>"2016", "pdf_views"=>"84", "xml_views"=>"0", "html_views"=>"284"}
  • {"month"=>"9", "year"=>"2016", "pdf_views"=>"89", "xml_views"=>"0", "html_views"=>"311"}
  • {"month"=>"10", "year"=>"2016", "pdf_views"=>"65", "xml_views"=>"0", "html_views"=>"282"}
  • {"month"=>"11", "year"=>"2016", "pdf_views"=>"29", "xml_views"=>"0", "html_views"=>"267"}
  • {"month"=>"12", "year"=>"2016", "pdf_views"=>"81", "xml_views"=>"0", "html_views"=>"456"}
  • {"month"=>"1", "year"=>"2017", "pdf_views"=>"112", "xml_views"=>"1", "html_views"=>"387"}
  • {"month"=>"2", "year"=>"2017", "pdf_views"=>"92", "xml_views"=>"1", "html_views"=>"363"}
  • {"month"=>"3", "year"=>"2017", "pdf_views"=>"80", "xml_views"=>"0", "html_views"=>"399"}
  • {"month"=>"4", "year"=>"2017", "pdf_views"=>"81", "xml_views"=>"0", "html_views"=>"367"}
  • {"month"=>"5", "year"=>"2017", "pdf_views"=>"84", "xml_views"=>"2", "html_views"=>"363"}
  • {"month"=>"6", "year"=>"2017", "pdf_views"=>"63", "xml_views"=>"1", "html_views"=>"282"}
  • {"month"=>"7", "year"=>"2017", "pdf_views"=>"85", "xml_views"=>"0", "html_views"=>"291"}
  • {"month"=>"8", "year"=>"2017", "pdf_views"=>"88", "xml_views"=>"0", "html_views"=>"310"}
  • {"month"=>"9", "year"=>"2017", "pdf_views"=>"116", "xml_views"=>"3", "html_views"=>"411"}
  • {"month"=>"10", "year"=>"2017", "pdf_views"=>"108", "xml_views"=>"2", "html_views"=>"444"}
  • {"month"=>"11", "year"=>"2017", "pdf_views"=>"122", "xml_views"=>"0", "html_views"=>"409"}
  • {"month"=>"12", "year"=>"2017", "pdf_views"=>"91", "xml_views"=>"2", "html_views"=>"381"}
  • {"month"=>"1", "year"=>"2018", "pdf_views"=>"104", "xml_views"=>"0", "html_views"=>"325"}
  • {"month"=>"2", "year"=>"2018", "pdf_views"=>"83", "xml_views"=>"1", "html_views"=>"170"}
  • {"month"=>"3", "year"=>"2018", "pdf_views"=>"75", "xml_views"=>"0", "html_views"=>"167"}
  • {"month"=>"4", "year"=>"2018", "pdf_views"=>"122", "xml_views"=>"0", "html_views"=>"185"}
  • {"month"=>"5", "year"=>"2018", "pdf_views"=>"107", "xml_views"=>"0", "html_views"=>"198"}
  • {"month"=>"6", "year"=>"2018", "pdf_views"=>"79", "xml_views"=>"2", "html_views"=>"184"}
  • {"month"=>"7", "year"=>"2018", "pdf_views"=>"107", "xml_views"=>"3", "html_views"=>"192"}
  • {"month"=>"8", "year"=>"2018", "pdf_views"=>"76", "xml_views"=>"1", "html_views"=>"188"}
  • {"month"=>"9", "year"=>"2018", "pdf_views"=>"51", "xml_views"=>"0", "html_views"=>"163"}
  • {"month"=>"10", "year"=>"2018", "pdf_views"=>"49", "xml_views"=>"2", "html_views"=>"106"}
  • {"month"=>"11", "year"=>"2018", "pdf_views"=>"41", "xml_views"=>"0", "html_views"=>"97"}
  • {"month"=>"12", "year"=>"2018", "pdf_views"=>"58", "xml_views"=>"0", "html_views"=>"105"}
  • {"month"=>"1", "year"=>"2019", "pdf_views"=>"72", "xml_views"=>"1", "html_views"=>"131"}
  • {"month"=>"2", "year"=>"2019", "pdf_views"=>"86", "xml_views"=>"0", "html_views"=>"154"}
  • {"month"=>"3", "year"=>"2019", "pdf_views"=>"101", "xml_views"=>"3", "html_views"=>"163"}
  • {"month"=>"4", "year"=>"2019", "pdf_views"=>"56", "xml_views"=>"0", "html_views"=>"137"}
  • {"month"=>"5", "year"=>"2019", "pdf_views"=>"95", "xml_views"=>"1", "html_views"=>"200"}
  • {"month"=>"6", "year"=>"2019", "pdf_views"=>"86", "xml_views"=>"2", "html_views"=>"118"}
  • {"month"=>"7", "year"=>"2019", "pdf_views"=>"91", "xml_views"=>"0", "html_views"=>"150"}
  • {"month"=>"8", "year"=>"2019", "pdf_views"=>"97", "xml_views"=>"1", "html_views"=>"127"}
  • {"month"=>"9", "year"=>"2019", "pdf_views"=>"87", "xml_views"=>"0", "html_views"=>"131"}
  • {"month"=>"10", "year"=>"2019", "pdf_views"=>"73", "xml_views"=>"1", "html_views"=>"147"}
  • {"month"=>"11", "year"=>"2019", "pdf_views"=>"71", "xml_views"=>"0", "html_views"=>"132"}
  • {"month"=>"12", "year"=>"2019", "pdf_views"=>"93", "xml_views"=>"0", "html_views"=>"178"}
  • {"month"=>"1", "year"=>"2020", "pdf_views"=>"69", "xml_views"=>"2", "html_views"=>"94"}
  • {"month"=>"2", "year"=>"2020", "pdf_views"=>"43", "xml_views"=>"1", "html_views"=>"78"}
  • {"month"=>"3", "year"=>"2020", "pdf_views"=>"77", "xml_views"=>"1", "html_views"=>"84"}
  • {"month"=>"4", "year"=>"2020", "pdf_views"=>"84", "xml_views"=>"3", "html_views"=>"93"}
  • {"month"=>"5", "year"=>"2020", "pdf_views"=>"142", "xml_views"=>"0", "html_views"=>"90"}
  • {"month"=>"6", "year"=>"2020", "pdf_views"=>"110", "xml_views"=>"0", "html_views"=>"109"}
  • {"month"=>"7", "year"=>"2020", "pdf_views"=>"84", "xml_views"=>"2", "html_views"=>"101"}
  • {"month"=>"8", "year"=>"2020", "pdf_views"=>"32", "xml_views"=>"0", "html_views"=>"80"}
  • {"month"=>"9", "year"=>"2020", "pdf_views"=>"42", "xml_views"=>"0", "html_views"=>"66"}
  • {"month"=>"10", "year"=>"2020", "pdf_views"=>"48", "xml_views"=>"0", "html_views"=>"59"}

Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2546286", "https://ndownloader.figshare.com/files/2546287", "https://ndownloader.figshare.com/files/2546288", "https://ndownloader.figshare.com/files/2546289", "https://ndownloader.figshare.com/files/2546290", "https://ndownloader.figshare.com/files/2546292", "https://ndownloader.figshare.com/files/2546293", "https://ndownloader.figshare.com/files/2546294", "https://ndownloader.figshare.com/files/2546295", "https://ndownloader.figshare.com/files/2546296", "https://ndownloader.figshare.com/files/2546297", "https://ndownloader.figshare.com/files/2546298", "https://ndownloader.figshare.com/files/2546299", "https://ndownloader.figshare.com/files/2546301", "https://ndownloader.figshare.com/files/2546302", "https://ndownloader.figshare.com/files/2546303", "https://ndownloader.figshare.com/files/2546304", "https://ndownloader.figshare.com/files/2546305", "https://ndownloader.figshare.com/files/2546306", "https://ndownloader.figshare.com/files/2546307", "https://ndownloader.figshare.com/files/2546308", "https://ndownloader.figshare.com/files/2546310", "https://ndownloader.figshare.com/files/2546311", "https://ndownloader.figshare.com/files/2546312", "https://ndownloader.figshare.com/files/2546313", "https://ndownloader.figshare.com/files/2546314", "https://ndownloader.figshare.com/files/2546315", "https://ndownloader.figshare.com/files/2546316", "https://ndownloader.figshare.com/files/2546318", "https://ndownloader.figshare.com/files/2546319", "https://ndownloader.figshare.com/files/2546320"], "description"=>"<div><p>A major challenge in developmental biology is to understand the genetic and cellular processes/programs driving organ formation and differentiation of the diverse cell types that comprise the embryo. While recent studies using single cell transcriptome analysis illustrate the power to measure and understand cellular heterogeneity in complex biological systems, processing large amounts of RNA-seq data from heterogeneous cell populations creates the need for readily accessible tools for the analysis of single-cell RNA-seq (scRNA-seq) profiles. The present study presents a generally applicable analytic pipeline (SINCERA: a computational pipeline for <u>SIN</u>gle <u>CE</u>ll <u>R</u>NA-seq profiling <u>A</u>nalysis) for processing scRNA-seq data from a whole organ or sorted cells. The pipeline supports the analysis for: 1) the distinction and identification of major cell types; 2) the identification of cell type specific gene signatures; and 3) the determination of driving forces of given cell types. We applied this pipeline to the RNA-seq analysis of single cells isolated from embryonic mouse lung at E16.5. Through the pipeline analysis, we distinguished major cell types of fetal mouse lung, including epithelial, endothelial, smooth muscle, pericyte, and fibroblast-like cell types, and identified cell type specific gene signatures, bioprocesses, and key regulators. SINCERA is implemented in R, licensed under the GNU General Public License v3, and freely available from CCHMC PBGE website, <a href=\"https://research.cchmc.org/pbge/sincera.html\" target=\"_blank\">https://research.cchmc.org/pbge/sincera.html</a>.</p></div>", "links"=>[], "tags"=>["mouse lung", "gnu", "cell type", "gene signatures", "cell types", "cell transcriptome analysis", "CCHMC PBGE website", "pipeline", "SINCERA"], "article_id"=>1611968, "categories"=>["Biological Sciences"], "users"=>["Minzhe Guo", "Hui Wang", "S. Steven Potter", "Jeffrey A. Whitsett", "Yan Xu"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1004575.s001", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s002", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s003", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s004", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s005", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s006", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s007", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s008", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s009", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s010", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s011", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s012", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s013", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s014", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s015", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s016", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s017", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s018", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s019", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s020", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s021", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s022", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s023", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s024", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s025", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s026", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s027", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s028", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s029", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s030", "https://dx.doi.org/10.1371/journal.pcbi.1004575.s031"], "stats"=>{"downloads"=>32, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_SINCERA_A_Pipeline_for_Single_Cell_RNA_Seq_Profiling_Analysis_/1611968", "title"=>"SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-11-24 04:14:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/2546112"], "description"=>"<p>The analytic pipeline consists of three main components: pre-processing, cell type identification, and cell type specific gene signature and driving force identification.</p>", "links"=>[], "tags"=>["mouse lung", "gnu", "cell type", "gene signatures", "cell types", "cell transcriptome analysis", "CCHMC PBGE website", "pipeline", "SINCERA"], "article_id"=>1611949, "categories"=>["Biological Sciences"], "users"=>["Minzhe Guo", "Hui Wang", "S. Steven Potter", "Jeffrey A. Whitsett", "Yan Xu"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004575.g001", "stats"=>{"downloads"=>8, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_Workflow_/1611949", "title"=>"Schematic Workflow.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-11-24 04:14:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/2546115"], "description"=>"<p>Cells (n = 148) from two sample preparations from fetal mouse lung at E16.5 were assigned into 9 clusters via hierarchical clustering using average linkage and centered Pearson’s correlation. Each color represents a distinct cell cluster, labeled as C1-C9. The rectangles represent single lung cells from the first preparation and the ellipses consist of single cells from a second independent preparation. Connection lines indicate the z-score correlation between the two cells > = 0.05. The blue lines connect cells within the same preparation, while the red lines connect cells across preparations.</p>", "links"=>[], "tags"=>["mouse lung", "gnu", "cell type", "gene signatures", "cell types", "cell transcriptome analysis", "CCHMC PBGE website", "pipeline", "SINCERA"], "article_id"=>1611950, "categories"=>["Biological Sciences"], "users"=>["Minzhe Guo", "Hui Wang", "S. Steven Potter", "Jeffrey A. Whitsett", "Yan Xu"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004575.g002", "stats"=>{"downloads"=>4, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Identification_of_Major_Lung_Cell_Types_/1611950", "title"=>"Identification of Major Lung Cell Types.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-11-24 04:14:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/2546117"], "description"=>"<p>(A) Expression patterns of representative known cell type markers were used to validate the correct assignment of major lung cell types at E16.5. Expression levels were normalized by per-sample z-score transformation. (B) ROC curves of the rank-aggregation-based validation showed a high consistency (AUC>0.8) between the cell type assignments and the expression patterns of known cell type specific markers (<b><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004575#pcbi.1004575.s023\" target=\"_blank\">S2 Table</a></b>).</p>", "links"=>[], "tags"=>["mouse lung", "gnu", "cell type", "gene signatures", "cell types", "cell transcriptome analysis", "CCHMC PBGE website", "pipeline", "SINCERA"], "article_id"=>1611951, "categories"=>["Biological Sciences"], "users"=>["Minzhe Guo", "Hui Wang", "S. Steven Potter", "Jeffrey A. Whitsett", "Yan Xu"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004575.g003", "stats"=>{"downloads"=>2, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Validation_of_Cell_Type_Assignments_using_Known_Biomarkers_/1611951", "title"=>"Validation of Cell Type Assignments using Known Biomarkers.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-11-24 04:14:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/2546121"], "description"=>"<p>Information on gene expression in certain cell types were downloaded from EBI Expression Atlas (<a href=\"http://www.ebi.ac.uk/gxa\" target=\"_blank\">http://www.ebi.ac.uk/gxa</a>). Results were obtained using differentially expressed genes as the input gene lists. The lengths of the bars represent transformed p-value (−log<sub>10</sub> (<i>p</i>)) of highly enriched cell types for each cell cluster, where <i>p</i> is the p-value calculated by one-tailed Fisher’s exact test and represents the degree of a cell type enrichment in a given cell cluster.</p>", "links"=>[], "tags"=>["mouse lung", "gnu", "cell type", "gene signatures", "cell types", "cell transcriptome analysis", "CCHMC PBGE website", "pipeline", "SINCERA"], "article_id"=>1611952, "categories"=>["Biological Sciences"], "users"=>["Minzhe Guo", "Hui Wang", "S. Steven Potter", "Jeffrey A. Whitsett", "Yan Xu"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004575.g004", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prediction_of_Cell_Types_for_Each_Cluster_using_Cell_Type_Enrichment_Analysis_/1611952", "title"=>"Prediction of Cell Types for Each Cluster using Cell Type Enrichment Analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-11-24 04:14:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/2546126"], "description"=>"<p>(A) Heatmap shows that the predicted cell type specific signature genes are selectively expressed in defined cell types. Gene expression was per sample z-score normalized. (B) The top 20 signature genes based on the ranking scores for each lung cell type are listed. Genes in red are the known markers that were used to train the signature prediction models.</p>", "links"=>[], "tags"=>["mouse lung", "gnu", "cell type", "gene signatures", "cell types", "cell transcriptome analysis", "CCHMC PBGE website", "pipeline", "SINCERA"], "article_id"=>1611953, "categories"=>["Biological Sciences"], "users"=>["Minzhe Guo", "Hui Wang", "S. Steven Potter", "Jeffrey A. Whitsett", "Yan Xu"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004575.g005", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predicted_Signature_Genes_for_Major_Lung_Cell_Types_/1611953", "title"=>"Predicted Signature Genes for Major Lung Cell Types.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-11-24 04:14:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/2546131"], "description"=>"<p>(A) Rank importance of transcription factors (TFs) in the main connected component of epithelial specific transcriptional regulatory network (TRN). The sizes of the TF nodes are proportional to their average-ranked node importance. The main connected component of epithelial TRN is comprised of 348 nodes and 432 edges. The nodes in red are the TFs and the nodes in grey are differentially expressed genes (p-value<0.01) in epithelial cells and are not TFs. The edges were established using the first-order conditional dependence approach described in the Methods section with a cutoff at 0.05. (B) The <i>Hopx</i> local network (the first hop is shown). <i>Hopx</i> was the top ranked TF identified by driving force analysis (<b><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004575#pcbi.1004575.t001\" target=\"_blank\">Table 1</a></b>).</p>", "links"=>[], "tags"=>["mouse lung", "gnu", "cell type", "gene signatures", "cell types", "cell transcriptome analysis", "CCHMC PBGE website", "pipeline", "SINCERA"], "article_id"=>1611954, "categories"=>["Biological Sciences"], "users"=>["Minzhe Guo", "Hui Wang", "S. Steven Potter", "Jeffrey A. Whitsett", "Yan Xu"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004575.g006", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mouse_Lung_Epithelial_Specific_Transcriptional_Regulatory_Network_/1611954", "title"=>"Mouse Lung Epithelial Specific Transcriptional Regulatory Network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-11-24 04:14:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/2546133"], "description"=>"<p>All ranks are in decreasing order of the TF importance metric values. TFs in bold font are associated with lung-related mouse phenotypes (<a href=\"http://www.informatics.jax.org/mp/annotations/MP:0005388\" target=\"_blank\">http://www.informatics.jax.org/mp/annotations/MP:0005388</a>).</p><p>Top 20 Predicted Key Transcription Factors for Lung Epithelial Cells at E16.5.</p>", "links"=>[], "tags"=>["mouse lung", "gnu", "cell type", "gene signatures", "cell types", "cell transcriptome analysis", "CCHMC PBGE website", "pipeline", "SINCERA"], "article_id"=>1611955, "categories"=>["Biological Sciences"], "users"=>["Minzhe Guo", "Hui Wang", "S. Steven Potter", "Jeffrey A. Whitsett", "Yan Xu"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004575.t001", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Top_20_Predicted_Key_Transcription_Factors_for_Lung_Epithelial_Cells_at_E16_5_/1611955", "title"=>"Top 20 Predicted Key Transcription Factors for Lung Epithelial Cells at E16.5.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-11-24 04:14:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/2546134"], "description"=>"<p>Regulatory targets are ranked in the increasing order of “Rank by CM”. The full set of candidate targets for consensus maximization consisted of genes that are differentially expressed in epithelial cells (p-value<0.01). Targets with bold font are known <i>Nkx2-1</i> targets in lung epithelial cells. “Expression based Prediction (EP)” is based on the first-order conditional dependence inference described in the Methods. “ChIP-seq” is based on the result of previous <i>Nkx2-1</i> ChIP-seq experiment: 1-represents the target has at least one predicted peak region; 0-means no predicted peak. “Literature Evidence 1” and “Literature Evidence 2” encodes the literature support from Ingenuity IPA (<a href=\"http://www.ingenuity.com/products/ipa\" target=\"_blank\">http://www.ingenuity.com/products/ipa</a>) and Genomatix (<a href=\"https://www.genomatix.de/\" target=\"_blank\">https://www.genomatix.de</a>), respectively. “Consensus Maximized Score (CM)” is the output of the consensus maximization. “Rank by EP” is the ranking of targets in the increasing order of the values in “Expression based Prediction (EP)”. “Rank by CM” is the ranking of targets in the decreasing order of the values in “Consensus Maximized Score (CM)”.</p><p>Top 20 Predicted Regulatory Targets of <i>Nkx2-1</i> Identified from a Consensus among Expression based Prediction, ChIP-seq, and Literature Evidence.</p>", "links"=>[], "tags"=>["mouse lung", "gnu", "cell type", "gene signatures", "cell types", "cell transcriptome analysis", "CCHMC PBGE website", "pipeline", "SINCERA"], "article_id"=>1611956, "categories"=>["Biological Sciences"], "users"=>["Minzhe Guo", "Hui Wang", "S. Steven Potter", "Jeffrey A. Whitsett", "Yan Xu"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004575.t002", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Top_20_Predicted_Regulatory_Targets_of_Nkx2_1_Identified_from_a_Consensus_among_Expression_based_Prediction_ChIP_seq_and_Literature_Evidence_/1611956", "title"=>"Top 20 Predicted Regulatory Targets of <i>Nkx2-1</i> Identified from a Consensus among Expression based Prediction, ChIP-seq, and Literature Evidence.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-11-24 04:14:19"}

PMC Usage Stats | Further Information

  • {"unique-ip"=>"136", "full-text"=>"131", "pdf"=>"57", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"51", "supp-data"=>"25", "cited-by"=>"0", "year"=>"2015", "month"=>"12"}
  • {"unique-ip"=>"76", "full-text"=>"58", "pdf"=>"28", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"28", "supp-data"=>"8", "cited-by"=>"0", "year"=>"2016", "month"=>"1"}
  • {"unique-ip"=>"64", "full-text"=>"40", "pdf"=>"7", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"24", "supp-data"=>"14", "cited-by"=>"0", "year"=>"2016", "month"=>"2"}
  • {"unique-ip"=>"64", "full-text"=>"49", "pdf"=>"20", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"28", "supp-data"=>"20", "cited-by"=>"1", "year"=>"2016", "month"=>"3"}
  • {"unique-ip"=>"66", "full-text"=>"62", "pdf"=>"28", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"81", "supp-data"=>"9", "cited-by"=>"0", "year"=>"2016", "month"=>"4"}
  • {"unique-ip"=>"46", "full-text"=>"45", "pdf"=>"18", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"19", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"5"}
  • {"unique-ip"=>"57", "full-text"=>"59", "pdf"=>"21", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"17", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2016", "month"=>"6"}
  • {"unique-ip"=>"38", "full-text"=>"36", "pdf"=>"12", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"7", "supp-data"=>"5", "cited-by"=>"0", "year"=>"2016", "month"=>"7"}
  • {"unique-ip"=>"45", "full-text"=>"43", "pdf"=>"18", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"19", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2016", "month"=>"8"}
  • {"unique-ip"=>"25", "full-text"=>"25", "pdf"=>"12", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"15", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2016", "month"=>"9"}
  • {"unique-ip"=>"29", "full-text"=>"27", "pdf"=>"15", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"8", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"10"}
  • {"unique-ip"=>"48", "full-text"=>"44", "pdf"=>"14", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"44", "supp-data"=>"3", "cited-by"=>"0", "year"=>"2016", "month"=>"11"}
  • {"unique-ip"=>"29", "full-text"=>"30", "pdf"=>"4", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"20", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2016", "month"=>"12"}
  • {"unique-ip"=>"49", "full-text"=>"41", "pdf"=>"13", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"20", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2017", "month"=>"1"}
  • {"unique-ip"=>"44", "full-text"=>"38", "pdf"=>"27", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"9", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2017", "month"=>"2"}
  • {"unique-ip"=>"39", "full-text"=>"40", "pdf"=>"19", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"39", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2017", "month"=>"3"}
  • {"unique-ip"=>"38", "full-text"=>"42", "pdf"=>"4", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"41", "supp-data"=>"9", "cited-by"=>"0", "year"=>"2017", "month"=>"4"}
  • {"unique-ip"=>"42", "full-text"=>"34", "pdf"=>"13", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"28", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"5"}
  • {"unique-ip"=>"35", "full-text"=>"33", "pdf"=>"11", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"15", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2017", "month"=>"6"}
  • {"unique-ip"=>"52", "full-text"=>"43", "pdf"=>"9", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"30", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2017", "month"=>"7"}
  • {"unique-ip"=>"54", "full-text"=>"49", "pdf"=>"15", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"18", "supp-data"=>"1", "cited-by"=>"1", "year"=>"2017", "month"=>"8"}
  • {"unique-ip"=>"68", "full-text"=>"61", "pdf"=>"23", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"35", "supp-data"=>"7", "cited-by"=>"0", "year"=>"2017", "month"=>"9"}
  • {"unique-ip"=>"82", "full-text"=>"76", "pdf"=>"22", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"69", "supp-data"=>"17", "cited-by"=>"0", "year"=>"2017", "month"=>"10"}
  • {"unique-ip"=>"59", "full-text"=>"60", "pdf"=>"12", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"28", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2017", "month"=>"11"}
  • {"unique-ip"=>"54", "full-text"=>"51", "pdf"=>"13", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"23", "supp-data"=>"6", "cited-by"=>"0", "year"=>"2017", "month"=>"12"}
  • {"unique-ip"=>"64", "full-text"=>"66", "pdf"=>"24", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"40", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"1"}
  • {"unique-ip"=>"49", "full-text"=>"53", "pdf"=>"24", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"44", "supp-data"=>"31", "cited-by"=>"0", "year"=>"2018", "month"=>"3"}
  • {"unique-ip"=>"38", "full-text"=>"45", "pdf"=>"10", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"6", "supp-data"=>"4", "cited-by"=>"0", "year"=>"2019", "month"=>"1"}
  • {"unique-ip"=>"50", "full-text"=>"50", "pdf"=>"15", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"17", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2018", "month"=>"5"}
  • {"unique-ip"=>"49", "full-text"=>"38", "pdf"=>"16", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"12", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"4"}
  • {"unique-ip"=>"54", "full-text"=>"39", "pdf"=>"12", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"29", "supp-data"=>"6", "cited-by"=>"0", "year"=>"2018", "month"=>"6"}
  • {"unique-ip"=>"72", "full-text"=>"71", "pdf"=>"20", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"13", "supp-data"=>"13", "cited-by"=>"0", "year"=>"2018", "month"=>"7"}
  • {"unique-ip"=>"71", "full-text"=>"67", "pdf"=>"28", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"18", "supp-data"=>"3", "cited-by"=>"0", "year"=>"2018", "month"=>"8"}
  • {"unique-ip"=>"59", "full-text"=>"61", "pdf"=>"14", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"22", "supp-data"=>"4", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
  • {"unique-ip"=>"42", "full-text"=>"32", "pdf"=>"8", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"28", "supp-data"=>"9", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
  • {"unique-ip"=>"48", "full-text"=>"48", "pdf"=>"12", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"6", "supp-data"=>"13", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"58", "full-text"=>"59", "pdf"=>"16", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"4", "supp-data"=>"73", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"19", "full-text"=>"19", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"6", "supp-data"=>"2", "cited-by"=>"2", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"46", "full-text"=>"51", "pdf"=>"13", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"15", "supp-data"=>"24", "cited-by"=>"2", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"58", "full-text"=>"50", "pdf"=>"13", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"14", "supp-data"=>"42", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"65", "full-text"=>"77", "pdf"=>"12", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"7", "supp-data"=>"18", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}
  • {"unique-ip"=>"27", "full-text"=>"30", "pdf"=>"9", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"5", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"75", "full-text"=>"99", "pdf"=>"9", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"15", "supp-data"=>"36", "cited-by"=>"1", "year"=>"2019", "month"=>"9"}
  • {"unique-ip"=>"36", "full-text"=>"34", "pdf"=>"12", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"11", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}
  • {"unique-ip"=>"62", "full-text"=>"50", "pdf"=>"32", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"9", "supp-data"=>"1", "cited-by"=>"1", "year"=>"2019", "month"=>"12"}
  • {"unique-ip"=>"50", "full-text"=>"61", "pdf"=>"14", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"4", "supp-data"=>"4", "cited-by"=>"7", "year"=>"2020", "month"=>"2"}
  • {"unique-ip"=>"60", "full-text"=>"56", "pdf"=>"15", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"23", "supp-data"=>"3", "cited-by"=>"0", "year"=>"2020", "month"=>"3"}
  • {"unique-ip"=>"63", "full-text"=>"72", "pdf"=>"23", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"6", "supp-data"=>"3", "cited-by"=>"0", "year"=>"2020", "month"=>"4"}
  • {"unique-ip"=>"63", "full-text"=>"63", "pdf"=>"12", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2020", "month"=>"5"}
  • {"unique-ip"=>"47", "full-text"=>"56", "pdf"=>"13", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"6"}
  • {"unique-ip"=>"31", "full-text"=>"25", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"30", "cited-by"=>"0", "year"=>"2020", "month"=>"7"}
  • {"unique-ip"=>"33", "full-text"=>"25", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"32", "cited-by"=>"0", "year"=>"2020", "month"=>"8"}
  • {"unique-ip"=>"39", "full-text"=>"27", "pdf"=>"10", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"11", "supp-data"=>"1", "cited-by"=>"1", "year"=>"2020", "month"=>"9"}

Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
Loading … Spinner
There are currently no alerts