OpenCyto: An Open Source Infrastructure for Scalable, Robust, Reproducible, and Automated, End-to-End Flow Cytometry Data Analysis
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{"title"=>"OpenCyto: An Open Source Infrastructure for Scalable, Robust, Reproducible, and Automated, End-to-End Flow Cytometry Data Analysis", "type"=>"journal", "authors"=>[{"first_name"=>"Greg", "last_name"=>"Finak", "scopus_author_id"=>"15072838600"}, {"first_name"=>"Jacob", "last_name"=>"Frelinger", "scopus_author_id"=>"24469897800"}, {"first_name"=>"Wenxin", "last_name"=>"Jiang", "scopus_author_id"=>"7403697739"}, {"first_name"=>"Evan W.", "last_name"=>"Newell", "scopus_author_id"=>"7004596930"}, {"first_name"=>"John", "last_name"=>"Ramey", "scopus_author_id"=>"57076123800"}, {"first_name"=>"Mark M.", "last_name"=>"Davis", "scopus_author_id"=>"7404850298"}, {"first_name"=>"Spyros A.", "last_name"=>"Kalams", "scopus_author_id"=>"7004215696"}, {"first_name"=>"Stephen C.", "last_name"=>"De Rosa", "scopus_author_id"=>"7006639747"}, {"first_name"=>"Raphael", "last_name"=>"Gottardo", "scopus_author_id"=>"12799324000"}], "year"=>2014, "source"=>"PLoS Computational Biology", "identifiers"=>{"issn"=>"15537358", "scopus"=>"2-s2.0-84961288133", "sgr"=>"84961288133", "pui"=>"607875775", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "pmid"=>"25167361", "doi"=>"10.1371/journal.pcbi.1003806"}, "id"=>"3cad3090-0cb5-3c6b-ba56-12a22ddd2433", "abstract"=>"Flow cytometry is used increasingly in clinical research for cancer, immunology and vaccines. Technological advances in cytometry instrumentation are increasing the size and dimensionality of data sets, posing a challenge for traditional data management and analysis. Automated analysis methods, despite a general consensus of their importance to the future of the field, have been slow to gain widespread adoption. Here we present OpenCyto, a new BioConductor infrastructure and data analysis framework designed to lower the barrier of entry to automated flow data analysis algorithms by addressing key areas that we believe have held back wider adoption of automated approaches. OpenCyto supports end-to-end data analysis that is robust and reproducible while generating results that are easy to interpret. We have improved the existing, widely used core BioConductor flow cytometry infrastructure by allowing analysis to scale in a memory efficient manner to the large flow data sets that arise in clinical trials, and integrating domain-specific knowledge as part of the pipeline through the hierarchical relationships among cell populations. Pipelines are defined through a text-based csv file, limiting the need to write data-specific code, and are data agnostic to simplify repetitive analysis for core facilities. We demonstrate how to analyze two large cytometry data sets: an intracellular cytokine staining (ICS) data set from a published HIV vaccine trial focused on detecting rare, antigen-specific T-cell populations, where we identify a new subset of CD8 T-cells with a vaccine-regimen specific response that could not be identified through manual analysis, and a CyTOF T-cell phenotyping data set where a large staining panel and many cell populations are a challenge for traditional analysis. The substantial improvements to the core BioConductor flow cytometry packages give OpenCyto the potential for wide adoption. It can rapidly leverage new developments in computational cytometry and facilitate reproducible analysis in a unified environment.", "link"=>"http://www.mendeley.com/research/opencyto-open-source-infrastructure-scalable-robust-reproducible-automated-endtoend-flow-cytometry-d", "reader_count"=>141, "reader_count_by_academic_status"=>{"Unspecified"=>4, "Professor > Associate Professor"=>9, "Researcher"=>48, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>35, "Student > Postgraduate"=>1, "Other"=>7, "Student > Master"=>18, "Student > Bachelor"=>10, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>4, "Professor > Associate Professor"=>9, "Researcher"=>48, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>35, "Student > Postgraduate"=>1, "Other"=>7, "Student > Master"=>18, "Student > Bachelor"=>10, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_subject_area"=>{"Unspecified"=>7, "Engineering"=>3, "Environmental Science"=>3, "Biochemistry, Genetics and Molecular Biology"=>15, "Mathematics"=>5, "Agricultural and Biological Sciences"=>59, "Medicine and Dentistry"=>16, "Arts and Humanities"=>1, "Chemistry"=>3, "Computer Science"=>10, "Immunology and Microbiology"=>19}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>16}, "Chemistry"=>{"Chemistry"=>3}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>19}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>59}, "Computer Science"=>{"Computer Science"=>10}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>15}, "Mathematics"=>{"Mathematics"=>5}, "Unspecified"=>{"Unspecified"=>7}, "Environmental Science"=>{"Environmental Science"=>3}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"Canada"=>1, "Sweden"=>1, "Czech Republic"=>1, "Netherlands"=>2, "United States"=>4, "Uruguay"=>1, "Italy"=>1, "United Kingdom"=>1, "Israel"=>1, "France"=>1, "Switzerland"=>1, "Spain"=>1}, "group_count"=>5}

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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1653338"], "description"=>"<p>OpenCyto can reproduce the FlowJo manual gates from a 16-workspace data set in 21 minutes with a peak memory usage of 1.8 GB. Once gated, the data occupies only 4.6 MB of RAM and is efficiently stored on disk in the HDF5/NetCDF format. Automated gating of the same data set using on OpenCyto GatingTemplate to generate data-driven gates for each of the 470 samples takes 1.74 hours on a single-processor. This can be parallelized across multiple cores for greater efficiency. The 420×2<sup>4</sup> Boolean subsets of 4-cytokine producing cells can be generated and extracted efficiently, taking only 17 minutes for 7520 different subsets. Analogous results are shown for the CyTOF data, which has higher dimensionality. Calculating the Boolean subsets of 9 cytokine gates for the four maturation subsets in the data was extremely quick. In contrast, the 4×2<sup>9</sup> Boolean subsets took 104 minutes to compute in FlowJo.</p><p>Performance metric of OpenCyto on the flow cytometry and CyTOF data sets, on a single-processor machine with 8 GB of RAM.</p>", "links"=>[], "tags"=>["cell populations", "Open Source Infrastructure", "data analysis framework", "flow data analysis algorithms", "intracellular cytokine staining", "OpenCyto", "core BioConductor flow cytometry packages", "HIV vaccine trial", "core BioConductor flow cytometry infrastructure", "flow data sets", "Automated analysis methods", "ics", "cytometry data sets"], "article_id"=>1154221, "categories"=>["Biological Sciences"], "users"=>["Greg Finak", "Jacob Frelinger", "Wenxin Jiang", "Evan W. Newell", "John Ramey", "Mark M. Davis", "Spyros A. Kalams", "Stephen C. De Rosa", "Raphael Gottardo"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003806.t001", "stats"=>{"downloads"=>3, "page_views"=>48, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_metric_of_OpenCyto_on_the_flow_cytometry_and_CyTOF_data_sets_on_a_single_processor_machine_with_8_GB_of_RAM_/1154221", "title"=>"Performance metric of OpenCyto on the flow cytometry and CyTOF data sets, on a single-processor machine with 8 GB of RAM.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-08-28 03:03:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1653337"], "description"=>"<p>The majority of naïve CD8 T cells (TN) do not express any cytokines (degree of functionality 0) or are mono-functional, while effector memory cells (TEM) are the most polyfunctional of the subsets (peaking at degree 5). Short-lived effector (TEF) cells have lower polyfunctionality (peaking at degree 4), and central memory (TCM) populations tend to have a constant level of polyfunctionality from degree1 through degree 7. The area under the curve for each cell subset integrates to one. The y-axis is transformed by a hyperbolic-arcsine to facilitate visualization of differences between subsets at higher degrees of polyfunctionality.</p>", "links"=>[], "tags"=>["cell populations", "Open Source Infrastructure", "data analysis framework", "flow data analysis algorithms", "intracellular cytokine staining", "OpenCyto", "core BioConductor flow cytometry packages", "HIV vaccine trial", "core BioConductor flow cytometry infrastructure", "flow data sets", "Automated analysis methods", "ics", "cytometry data sets"], "article_id"=>1154220, "categories"=>["Biological Sciences"], "users"=>["Greg Finak", "Jacob Frelinger", "Wenxin Jiang", "Evan W. Newell", "John Ramey", "Mark M. Davis", "Spyros A. Kalams", "Stephen C. De Rosa", "Raphael Gottardo"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003806.g006", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_distribution_of_cells_of_each_maturational_state_and_their_degree_of_functionality_/1154220", "title"=>"The distribution of cells of each maturational state and their degree of functionality.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-28 03:03:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1653333"], "description"=>"<p>A) Box-plots of the paired differences (post-vaccination – baseline) in proportions of cytokine-producing cells from significant cell subsets identified by the linear model (see Supplementary Methods) for each stimulation condition, gating method, and vaccine regimen. Differences between baseline and post-vaccination are background-corrected (stimulated – non-stimulated). There were no significant differences between the observed distributions for manual or OpenCyto gating (paired Wilcoxon test). B) Scatter plots comparing manual gating vs. OpenCyto gating. The per-subject, background-corrected difference between vaccine and baseline is plotted for OpenCyto and manual gating, with concordance correlation coefficients shown for all stimulations.</p>", "links"=>[], "tags"=>["cell populations", "Open Source Infrastructure", "data analysis framework", "flow data analysis algorithms", "intracellular cytokine staining", "OpenCyto", "core BioConductor flow cytometry packages", "HIV vaccine trial", "core BioConductor flow cytometry infrastructure", "flow data sets", "Automated analysis methods", "ics", "cytometry data sets"], "article_id"=>1154216, "categories"=>["Biological Sciences"], "users"=>["Greg Finak", "Jacob Frelinger", "Wenxin Jiang", "Evan W. Newell", "John Ramey", "Mark M. Davis", "Spyros A. Kalams", "Stephen C. De Rosa", "Raphael Gottardo"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003806.g003", "stats"=>{"downloads"=>1, "page_views"=>50, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_OpenCyto_automated_gating_and_manual_gating_performed_with_FlowJo_and_imported_and_reproduced_in_R_using_OpenCyto_for_HVTN_080_/1154216", "title"=>"Comparison of OpenCyto automated gating and manual gating (performed with FlowJo and imported and reproduced in R using OpenCyto) for HVTN 080.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-28 03:03:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1653328"], "description"=>"<p>The automated gates are data-driven. Each panel shows a corresponding manual and automated gate side-by-side. The left panel is the manual gate; the right panel is the OpenCyto data-driven gate. Parent population names differ between manual and automated gates for singlets and lymphocytes because the automated gating hierarchy differs from the manual gating by including boundary and boundary debris gates, respectively, before these populations. Starting at the top left and proceeding along the rows, the gates shown are singlets, live cells, lymphocytes, CD3<sup>+</sup> T-cells, CD4<sup>+</sup> and CD8<sup>+</sup> T-cells, IFN-γ<sup>+</sup> and IL2<sup>+</sup> expressing CD4<sup>+</sup> and CD8<sup>+</sup> T-cells, and Granzyme B<sup>+</sup> and CD57<sup>+</sup> expressing CD8<sup>+</sup> T-cells. The manual and automated gates are very comparable.</p>", "links"=>[], "tags"=>["cell populations", "Open Source Infrastructure", "data analysis framework", "flow data analysis algorithms", "intracellular cytokine staining", "OpenCyto", "core BioConductor flow cytometry packages", "HIV vaccine trial", "core BioConductor flow cytometry infrastructure", "flow data sets", "Automated analysis methods", "ics", "cytometry data sets"], "article_id"=>1154211, "categories"=>["Biological Sciences"], "users"=>["Greg Finak", "Jacob Frelinger", "Wenxin Jiang", "Evan W. Newell", "John Ramey", "Mark M. Davis", "Spyros A. Kalams", "Stephen C. De Rosa", "Raphael Gottardo"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003806.g002", "stats"=>{"downloads"=>1, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_a_subset_of_manual_gates_and_OpenCyto_automated_gates_for_a_representative_sample_from_the_HVTN080_ICS_data_set_/1154211", "title"=>"Comparison of a subset of manual gates and OpenCyto automated gates for a representative sample from the HVTN080 ICS data set.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-28 03:03:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1653322"], "description"=>"<p>When reproducing manual gating, raw FCS files and FlowJo workspace XML files are read into the R environment using <i>parseWorkspace</i>, creating a <i>GatingSet</i> object that represents the compensated, transformed and gated data stored in an <i>ncdfFlowSet</i> on disk. Cell populations annotated with gates can be visualized using <i>plotGate</i>, from the <i>flowViz</i> package Gating schemes can be visualized using <i>plot</i>. To perform automated gating, the user defines a <i>csv</i> representation of a gating tree, which is read by the <i>OpenCyto</i> package to generate a <i>gatingTemplate object</i>. This template can be applied to a <i>GatingSet</i> containing data, but no gates, provided the data uses the markers defined in the template. OpenCyto utilizes built-in automated gating methods, or external methods registered via a plug-in framework, to gate different cell subsets and populate the <i>GatingSet</i> with data-driven gate definitions for each sample. Manual and automated gating may be readily compared within a single framework. Cell populations and features can be extracted for further statistical analysis with other R and BioConductor software packages. Data (red boxes), software packages (blue boxes), framework functionality (gray boxes), and data flow/data structures (arrows/labeled arrows) are represented. <i>flowCore</i>, <i>flowStats</i>, and <i>flowViz</i>, are the <i>core</i> Bioconductor flow packages that benefit from the substantial infrastructure changes we have made to improve scalability and data visualization.</p>", "links"=>[], "tags"=>["cell populations", "Open Source Infrastructure", "data analysis framework", "flow data analysis algorithms", "intracellular cytokine staining", "OpenCyto", "core BioConductor flow cytometry packages", "HIV vaccine trial", "core BioConductor flow cytometry infrastructure", "flow data sets", "Automated analysis methods", "ics", "cytometry data sets"], "article_id"=>1154205, "categories"=>["Biological Sciences"], "users"=>["Greg Finak", "Jacob Frelinger", "Wenxin Jiang", "Evan W. Newell", "John Ramey", "Mark M. Davis", "Spyros A. Kalams", "Stephen C. De Rosa", "Raphael Gottardo"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003806.g001", "stats"=>{"downloads"=>2, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_An_overview_of_the_OpenCyto_infrastructure_/1154205", "title"=>"An overview of the OpenCyto infrastructure.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-28 03:03:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1653336"], "description"=>"<p>Samples were stimulated with PMA-Ionomycin for 3 hours. Rows represent different maturational cell subsets (TN: naïve, TCM: central memory, TEF: effector, TEM: effector memory) and are clustered by Euclidean distance similarity. Columns represent different cytokine-producing cell subsets. The bottom legend defines the cell subset in a column. The legend is colored by degree of functionality of the cell subsets (light blue: degree 1, dark blue: degree 2, light green: degree 3, dark green: degree 4, salmon: degree 5, red: degree 6, orange: degree 7). The shading of individual blocks of the heatmap represents the average proportion of cells in the subset across the two samples, normalized to the total number of CD8 T-cells. Naïve cells have low polyfunctionality compared to effector, effector memory, and central memory cells.</p>", "links"=>[], "tags"=>["cell populations", "Open Source Infrastructure", "data analysis framework", "flow data analysis algorithms", "intracellular cytokine staining", "OpenCyto", "core BioConductor flow cytometry packages", "HIV vaccine trial", "core BioConductor flow cytometry infrastructure", "flow data sets", "Automated analysis methods", "ics", "cytometry data sets"], "article_id"=>1154219, "categories"=>["Biological Sciences"], "users"=>["Greg Finak", "Jacob Frelinger", "Wenxin Jiang", "Evan W. Newell", "John Ramey", "Mark M. Davis", "Spyros A. Kalams", "Stephen C. De Rosa", "Raphael Gottardo"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003806.g005", "stats"=>{"downloads"=>2, "page_views"=>45, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_average_frequency_of_expression_across_two_CyTOF_samples_for_cytokine_producing_cell_subsets_from_four_T_cell_maturational_states_/1154219", "title"=>"The average frequency of expression across two CyTOF samples for cytokine-producing cell subsets from four T-cell maturational states.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-28 03:03:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1653335"], "description"=>"<p>The perforin marker exhibits staining variability as evidenced by the varying width and position of the negative peak and was not gated by the manual template-gating approach. Despite this variability, OpenCyto data-driven automated gating is able to identify a reasonable threshold for perforin positive cells.</p>", "links"=>[], "tags"=>["cell populations", "Open Source Infrastructure", "data analysis framework", "flow data analysis algorithms", "intracellular cytokine staining", "OpenCyto", "core BioConductor flow cytometry packages", "HIV vaccine trial", "core BioConductor flow cytometry infrastructure", "flow data sets", "Automated analysis methods", "ics", "cytometry data sets"], "article_id"=>1154218, "categories"=>["Biological Sciences"], "users"=>["Greg Finak", "Jacob Frelinger", "Wenxin Jiang", "Evan W. Newell", "John Ramey", "Mark M. Davis", "Spyros A. Kalams", "Stephen C. De Rosa", "Raphael Gottardo"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003806.g004", "stats"=>{"downloads"=>6, "page_views"=>81, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_of_OpenCyto_automated_gates_on_the_perforin_channel_for_CD8_T_cells_for_six_randomly_selected_samples_from_the_HVTN_080_ICS_data_set_/1154218", "title"=>"Example of OpenCyto automated gates on the perforin channel for CD8<sup>+</sup> T-cells for six randomly selected samples from the HVTN 080 ICS data set.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-28 03:03:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1653383", "https://ndownloader.figshare.com/files/1653384", "https://ndownloader.figshare.com/files/1653385", "https://ndownloader.figshare.com/files/1653386", "https://ndownloader.figshare.com/files/1653387", "https://ndownloader.figshare.com/files/1653388", "https://ndownloader.figshare.com/files/1653389", "https://ndownloader.figshare.com/files/1653391", "https://ndownloader.figshare.com/files/1653392", "https://ndownloader.figshare.com/files/1653393", "https://ndownloader.figshare.com/files/1653394", "https://ndownloader.figshare.com/files/1653395", "https://ndownloader.figshare.com/files/1653396"], "description"=>"<div><p>Flow cytometry is used increasingly in clinical research for cancer, immunology and vaccines. Technological advances in cytometry instrumentation are increasing the size and dimensionality of data sets, posing a challenge for traditional data management and analysis. Automated analysis methods, despite a general consensus of their importance to the future of the field, have been slow to gain widespread adoption. Here we present OpenCyto, a new BioConductor infrastructure and data analysis framework designed to lower the barrier of entry to automated flow data analysis algorithms by addressing key areas that we believe have held back wider adoption of automated approaches. OpenCyto supports <i>end-to-end</i> data analysis that is <i>robust and reproducible</i> while generating results that are <i>easy to interpret</i>. We have improved the existing, widely used <i>core</i> BioConductor flow cytometry infrastructure by allowing analysis to <i>scale</i> in a memory efficient manner to the large flow data sets that arise in clinical trials, and integrating <i>domain-specific knowledge</i> as part of the pipeline through the <i>hierarchical relationships among cell populations</i>. Pipelines are defined through a text-based <i>csv</i> file, limiting the need to write data-specific code, and are <i>data agnostic</i> to simplify <i>repetitive analysis</i> for core facilities. We demonstrate how to analyze two large cytometry data sets: an intracellular cytokine staining (ICS) data set from a published HIV vaccine trial focused on detecting rare, antigen-specific T-cell populations, where we identify a new subset of CD8 T-cells with a vaccine-regimen specific response that could not be identified through manual analysis, and a CyTOF T-cell phenotyping data set where a large staining panel and many cell populations are a challenge for traditional analysis. The substantial improvements to the <i>core</i> BioConductor flow cytometry packages give OpenCyto the potential for wide adoption. It can rapidly leverage new developments in computational cytometry and facilitate reproducible analysis in a unified environment.</p></div>", "links"=>[], "tags"=>["cell populations", "Open Source Infrastructure", "data analysis framework", "flow data analysis algorithms", "intracellular cytokine staining", "OpenCyto", "core BioConductor flow cytometry packages", "HIV vaccine trial", "core BioConductor flow cytometry infrastructure", "flow data sets", "Automated analysis methods", "ics", "cytometry data sets"], "article_id"=>1154241, "categories"=>["Biological Sciences"], "users"=>["Greg Finak", "Jacob Frelinger", "Wenxin Jiang", "Evan W. Newell", "John Ramey", "Mark M. Davis", "Spyros A. Kalams", "Stephen C. De Rosa", "Raphael Gottardo"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003806.s001", "https://dx.doi.org/10.1371/journal.pcbi.1003806.s002", "https://dx.doi.org/10.1371/journal.pcbi.1003806.s003", "https://dx.doi.org/10.1371/journal.pcbi.1003806.s004", "https://dx.doi.org/10.1371/journal.pcbi.1003806.s005", "https://dx.doi.org/10.1371/journal.pcbi.1003806.s006", "https://dx.doi.org/10.1371/journal.pcbi.1003806.s007", "https://dx.doi.org/10.1371/journal.pcbi.1003806.s008", "https://dx.doi.org/10.1371/journal.pcbi.1003806.s009", "https://dx.doi.org/10.1371/journal.pcbi.1003806.s010", "https://dx.doi.org/10.1371/journal.pcbi.1003806.s011", "https://dx.doi.org/10.1371/journal.pcbi.1003806.s012", "https://dx.doi.org/10.1371/journal.pcbi.1003806.s013"], "stats"=>{"downloads"=>6, "page_views"=>24, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_OpenCyto_An_Open_Source_Infrastructure_for_Scalable_Robust_Reproducible_and_Automated_End_to_End_Flow_Cytometry_Data_Analysis_/1154241", "title"=>"OpenCyto: An Open Source Infrastructure for Scalable, Robust, Reproducible, and Automated, End-to-End Flow Cytometry Data Analysis", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-08-28 03:03:34"}

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Relative Metric

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