A Computational Framework to Emulate the Human Perspective in Flow Cytometric Data Analysis
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{"title"=>"A computational framework to emulate the human perspective in flow cytometric data analysis", "type"=>"journal", "authors"=>[{"first_name"=>"Surajit", "last_name"=>"Ray", "scopus_author_id"=>"18635010600"}, {"first_name"=>"Saumyadipta", "last_name"=>"Pyne", "scopus_author_id"=>"8979961900"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84860476264", "sgr"=>"84860476264", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0035693", "pmid"=>"22563466", "pui"=>"364721331"}, "id"=>"8e24ccb6-bada-37c4-93a5-46a17301f939", "abstract"=>"BACKGROUND:In recent years, intense research efforts have focused on developing methods for automated flow cytometric data analysis. However, while designing such applications, little or no attention has been paid to the human perspective that is absolutely central to the manual gating process of identifying and characterizing cell populations. In particular, the assumption of many common techniques that cell populations could be modeled reliably with pre-specified distributions may not hold true in real-life samples, which can have populations of arbitrary shapes and considerable inter-sample variation.\\n\\nRESULTS:To address this, we developed a new framework flowScape for emulating certain key aspects of the human perspective in analyzing flow data, which we implemented in multiple steps. First, flowScape begins with creating a mathematically rigorous map of the high-dimensional flow data landscape based on dense and sparse regions defined by relative concentrations of events around modes. In the second step, these modal clusters are connected with a global hierarchical structure. This representation allows flowScape to perform ridgeline analysis for both traversing the landscape and isolating cell populations at different levels of resolution. Finally, we extended manual gating with a new capacity for constructing templates that can identify target populations in terms of their relative parameters, as opposed to the more commonly used absolute or physical parameters. This allows flowScape to apply such templates in batch mode for detecting the corresponding populations in a flexible, sample-specific manner. We also demonstrated different applications of our framework to flow data analysis and show its superiority over other analytical methods.\\n\\nCONCLUSIONS:The human perspective, built on top of intuition and experience, is a very important component of flow cytometric data analysis. By emulating some of its approaches and extending these with automation and rigor, flowScape provides a flexible and robust framework for computational cytomics.", "link"=>"http://www.mendeley.com/research/computational-framework-emulate-human-perspective-flow-cytometric-data-analysis", "reader_count"=>18, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Researcher"=>8, "Student > Ph. D. Student"=>4, "Other"=>1, "Student > Master"=>1, "Student > Bachelor"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Researcher"=>8, "Student > Ph. D. Student"=>4, "Other"=>1, "Student > Master"=>1, "Student > Bachelor"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Engineering"=>1, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>1, "Agricultural and Biological Sciences"=>8, "Medicine and Dentistry"=>3, "Computer Science"=>2}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>8}, "Computer Science"=>{"Computer Science"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Unspecified"=>{"Unspecified"=>2}, "Environmental Science"=>{"Environmental Science"=>1}}, "reader_count_by_country"=>{"United States"=>1}, "group_count"=>0}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/332148", "https://ndownloader.figshare.com/files/332179", "https://ndownloader.figshare.com/files/332209"], "description"=>"<div><h3>Background</h3><p>In recent years, intense research efforts have focused on developing methods for automated flow cytometric data analysis. However, while designing such applications, little or no attention has been paid to the human perspective that is absolutely central to the manual gating process of identifying and characterizing cell populations. In particular, the assumption of many common techniques that cell populations could be modeled reliably with pre-specified distributions may not hold true in real-life samples, which can have populations of arbitrary shapes and considerable inter-sample variation.</p> <h3>Results</h3><p>To address this, we developed a new framework flowScape for emulating certain key aspects of the human perspective in analyzing flow data, which we implemented in multiple steps. First, flowScape begins with creating a mathematically rigorous map of the high-dimensional flow data landscape based on dense and sparse regions defined by relative concentrations of events around modes. In the second step, these modal clusters are connected with a global hierarchical structure. This representation allows flowScape to perform ridgeline analysis for both traversing the landscape and isolating cell populations at different levels of resolution. Finally, we extended manual gating with a new capacity for constructing templates that can identify target populations in terms of their relative parameters, as opposed to the more commonly used absolute or physical parameters. This allows flowScape to apply such templates in batch mode for detecting the corresponding populations in a flexible, sample-specific manner. We also demonstrated different applications of our framework to flow data analysis and show its superiority over other analytical methods.</p> <h3>Conclusions</h3><p>The human perspective, built on top of intuition and experience, is a very important component of flow cytometric data analysis. By emulating some of its approaches and extending these with automation and rigor, flowScape provides a flexible and robust framework for computational cytomics.</p> </div>", "links"=>[], "tags"=>["computational", "emulate", "cytometric"], "article_id"=>125585, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Surajit Ray", "Saumyadipta Pyne"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0035693.s001", "https://dx.doi.org/10.1371/journal.pone.0035693.s002", "https://dx.doi.org/10.1371/journal.pone.0035693.s003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/A_Computational_Framework_to_Emulate_the_Human_Perspective_in_Flow_Cytometric_Data_Analysis/125585", "title"=>"A Computational Framework to Emulate the Human Perspective in Flow Cytometric Data Analysis", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-05-01 01:33:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/643718"], "description"=>"<p>We show the distribution of Treg events after applying logicle transformation based on marker-specific optimal parameters computed with flowScape. The optimal arguments are shown in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035693#pone-0035693-t001\" target=\"_blank\">Table 1</a>. Notably, for every marker, the distinct negative cluster has disappeared, and the existing clusters display approximately bell shaped distributions. All 0-clusters satisfy normality criteria for kurtosis, skewness and unimodality. The yellow box highlights the optimal structure consisting of fairly well-rounded populations.</p>", "links"=>[], "tags"=>["Computational biology", "cell biology", "computer science", "mathematics"], "article_id"=>314205, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Surajit Ray", "Saumyadipta Pyne"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0035693.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Optimal_Transformation_with_flowScape_/314205", "title"=>"Optimal Transformation with flowScape.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-05-01 01:10:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/643810"], "description"=>"<p>The distributions of CompControl events after Arcsinh transformation based on different values of the cofactor are shown. The cofactor values that satisfied our tests were 2500 and 1000. For these values, we see that there is no spurious splitting of the 0-cluster, which produces distinctive negative clusters for cofactors less than 1000. On the other hand, for cofactors greater than 2500, the 0-clusters are clearly spiky. In contrast, the 0-cluster for the cofactor values optimized according to flowScape normality criterion is neither too peaked nor too flat. Thus flowScape addressed both problems of over- and under-transformation of data.</p>", "links"=>[], "tags"=>["optimal", "cofactor"], "article_id"=>314312, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Surajit Ray", "Saumyadipta Pyne"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0035693.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Selecting_the_optimal_value_of_cofactor_using_flowScape_/314312", "title"=>"Selecting the optimal value of cofactor using flowScape.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-05-01 01:11:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/643956"], "description"=>"<p>We demonstrate live gating on a representative LCL sample using flowScape. (a) The sample is shown as a scatterplot in terms of forward and side scatters. (b) Using flowScape, we map the data landscape and determine the ridgeline (red curve) for the sample, as shown in 3-D. The ridgeline connects every modal cluster in the multi-dimensional data by traversing the terrain from peak to peak across slopes and valleys in terms of data density, thus providing a systematic hierarchical description of the sample using the landscape map. (c) The ridgeline (here shown as blue/yellow curve for dense/sparse regions) can therefore be used for objective extraction of relatively denser concentrations of events. A dip in the ridgeline (red asterisks) can guide the demarcation of cell subpopulations that are otherwise hard to isolate with automated clustering. Thus flowScape can offer the unique advantages of human intuition without paying the cost of associated subjectivity. (d) The final live gating results of flowScape are shown as 2 major populations in blue (live cells) and red (dead), after removing points at the extremity (around bin 1000). Clearly these clusters have non-elliptical shapes that could not be captured by many of the common clustering methods.</p>", "links"=>[], "tags"=>["clustering", "ridgeline"], "article_id"=>314452, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Surajit Ray", "Saumyadipta Pyne"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0035693.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Modal_clustering_via_landscape_mapping_and_ridgeline_analysis_/314452", "title"=>"Modal clustering via landscape mapping and ridgeline analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-05-01 01:14:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/644098"], "description"=>"<p>In the left panel we show the scatterplots of two LCL samples in terms of forward- and side-scatters. Owing to the inter-connected nature of the distributions, extraction of the live cell population is difficult via automation. Using modal clustering and ridgeline analysis, flowScape provides algorithmic means to separate and extract the populations based on locations where the altitude of the ridgeline dips while moving from one peak to another, as marked with red asterisks. The ridgeline is colored according to its altitude at each coordinate.</p>", "links"=>[], "tags"=>["Computational biology", "cell biology", "computer science", "mathematics"], "article_id"=>314596, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Surajit Ray", "Saumyadipta Pyne"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0035693.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Objective_isolation_of_cell_populations_/314596", "title"=>"Objective isolation of cell populations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-05-01 01:16:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/644236"], "description"=>"<p>We compared the results of lymphocyte gating for two representative samples (s6a06, s6a07 – the last two time points for Patient 6 in GvHD data). For both samples, we ran 2 well-known methods, flowCore and SamSpectral, and flowScape to automatically identify the lymphocyte populations (as defined in Ellis et al. <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035693#pone.0035693-Ellis1\" target=\"_blank\">[43]</a>). While flowCore gate (red ellipse) was unable to detect the target population automatically in either sample, SamSpectral gated it (black outline) it correctly in only the sample to the left. On the other hand, due to the sparseness of the corresponding population in the sample to the right, SamSpectral failed to isolate it. In contrast, flowScape’s dynamic, sample-specific templates captured the lymphocyte populations accurately in both samples in spite of their inter-sample variation in locations, densities and shapes (green outline).</p>", "links"=>[], "tags"=>["automated", "gating"], "article_id"=>314734, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Surajit Ray", "Saumyadipta Pyne"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0035693.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparative_analysis_of_automated_gating_performance_by_different_methods_/314734", "title"=>"Comparative analysis of automated gating performance by different methods.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-05-01 01:18:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/644321"], "description"=>"<p>Optimal values of logicle cofactor for all markers in Treg Data</p>", "links"=>[], "tags"=>["logicle", "cofactor", "markers", "treg"], "article_id"=>314815, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Surajit Ray", "Saumyadipta Pyne"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0035693.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Optimal_values_of_logicle_cofactor_for_all_markers_in_Treg_Data_/314815", "title"=>"Optimal values of logicle cofactor for all markers in Treg Data", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-05-01 01:20:15"}

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Relative Metric

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