GenePattern flow cytometry suite

BackgroundTraditional flow cytometry data analysis is largely based on interactive and time consuming analysis of series two dimensional representations of up to 20 dimensional data. Recent technological advances have increased the amount of data generated by the technology and outpaced the development of data analysis approaches. While there are advanced tools available, including many R/BioConductor packages, these are only accessible programmatically and therefore out of reach for most experimentalists. GenePattern is a powerful genomic analysis platform with over 200 tools for analysis of gene expression, proteomics, and other data. A web-based interface provides easy access to these tools and allows the creation of automated analysis pipelines enabling reproducible research.ResultsIn order to bring advanced flow cytometry data analysis tools to experimentalists without programmatic skills, we developed the GenePattern Flow Cytometry Suite. It contains 34 open source GenePattern flow cytometry modules covering methods from basic processing of flow cytometry standard (i.e., FCS) files to advanced algorithms for automated identification of cell populations, normalization and quality assessment. Internally, these modules leverage from functionality developed in R/BioConductor. Using the GenePattern web-based interface, they can be connected to build analytical pipelines.ConclusionsGenePattern Flow Cytometry Suite brings advanced flow cytometry data analysis capabilities to users with minimal computer skills. Functionality previously available only to skilled bioinformaticians is now easily accessible from a web browser.

[1]  M. Roederer,et al.  Optimizing a multicolor immunophenotyping assay. , 2007, Clinics in laboratory medicine.

[2]  Jean YH Yang,et al.  Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.

[3]  Wayne A Moore,et al.  Update for the logicle data scale including operational code implementations , 2012, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[4]  A. Lacoma,et al.  Quantitative evaluation of T-cell response after specific antigen stimulation in active and latent tuberculosis infection in adults and children. , 2009, Diagnostic microbiology and infectious disease.

[5]  J. Mesirov,et al.  GenePattern 2.0 , 2006, Nature Genetics.

[6]  J. Mesirov,et al.  Automated high-dimensional flow cytometric data analysis , 2009, Proceedings of the National Academy of Sciences.

[7]  T C Bakker Schut,et al.  Cluster analysis of flow cytometric list mode data on a personal computer. , 1993, Cytometry.

[8]  Jamie L. Harden,et al.  Central Role of Tumor-Associated CD8+ T Effector/Memory Cells in Restoring Systemic Antitumor Immunity1 , 2009, The Journal of Immunology.

[9]  Mario Roederer,et al.  Gating‐ML: XML‐based gating descriptions in flow cytometry , 2008, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[10]  Ryan R Brinkman,et al.  Rapid cell population identification in flow cytometry data , 2011, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[11]  Wade T. Rogers,et al.  FlowFP: A Bioconductor Package for Fingerprinting Flow Cytometric Data , 2009, Adv. Bioinformatics.

[12]  Nikesh Kotecha,et al.  Web‐Based Analysis and Publication of Flow Cytometry Experiments , 2010, Current protocols in cytometry.

[13]  Mario Roederer,et al.  Dear Reader, , 2003, Nature Medicine.

[14]  Matthew R Clutter,et al.  High-content single-cell drug screening with phosphospecific flow cytometry. , 2008, Nature chemical biology.

[15]  Ryan R Brinkman,et al.  Per‐channel basis normalization methods for flow cytometry data , 2009, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[16]  George Luta,et al.  The curvHDR method for gating flow cytometry samples , 2010, BMC Bioinformatics.

[17]  Yu Qian,et al.  ImmPort FLOCK: Automated cell population identification in high dimensional flow cytometry data (42.17) , 2009, The Journal of Immunology.

[18]  Mario Roederer,et al.  A new “Logicle” display method avoids deceptive effects of logarithmic scaling for low signals and compensated data , 2006, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[19]  Leonid Hrebien,et al.  Sequential univariate gating approach to study the effects of erythropoietin in murine bone marrow , 2008, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[20]  C Bruce Bagwell,et al.  Hyperlog—A flexible log‐like transform for negative, zero, and positive valued data , 2005, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[21]  Raphael Gottardo,et al.  flowClust: a Bioconductor package for automated gating of flow cytometry data , 2009, BMC Bioinformatics.

[22]  Mario Roederer,et al.  Data File Standard for Flow Cytometry, version FCS 3.1 , 2009, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[23]  R. Scheuermann,et al.  Elucidation of seventeen human peripheral blood B‐cell subsets and quantification of the tetanus response using a density‐based method for the automated identification of cell populations in multidimensional flow cytometry data , 2010, Cytometry. Part B, Clinical cytometry.

[24]  Maura Gasparetto,et al.  Data quality assessment of ungated flow cytometry data in high throughput experiments , 2007, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[25]  Ryan Brinkman,et al.  Long-term propagation of distinct hematopoietic differentiation programs in vivo. , 2007, Cell stem cell.

[26]  Zbigniew Darzynkiewicz,et al.  Cytometry of the cell cycle: Cycling through history , 2004, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[27]  B. Thiers,et al.  Multifunctional TH1 cells define a correlate of vaccine-mediated protection against Leishmania major , 2008 .

[28]  Ryan Remy Brinkman,et al.  Analysis of High-Throughput Flow Cytometry Data Using plateCore , 2009, Adv. Bioinformatics.

[29]  Sean C. Bendall,et al.  Extracting a Cellular Hierarchy from High-dimensional Cytometry Data with SPADE , 2011, Nature Biotechnology.

[30]  Mario Roederer,et al.  Multifunctional TH1 cells define a correlate of vaccine-mediated protection against Leishmania major , 2007, Nature Medicine.

[31]  Jill P Mesirov,et al.  Accessible Reproducible Research , 2010, Science.

[32]  M Roederer,et al.  Frequency difference gating: a multivariate method for identifying subsets that differ between samples. , 2001, Cytometry.

[33]  M. Roederer,et al.  Data analysis in flow cytometry: The future just started , 2010, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[34]  Robert Gentleman,et al.  flowCore: a Bioconductor package for high throughput flow cytometry , 2009, BMC Bioinformatics.

[35]  Greg Finak,et al.  Merging Mixture Components for Cell Population Identification in Flow Cytometry , 2009, Adv. Bioinformatics.

[36]  Arvind Gupta,et al.  Data reduction for spectral clustering to analyze high throughput flow cytometry data , 2010, BMC Bioinformatics.

[37]  Kui Wang,et al.  Multivariate Skew t Mixture Models: Applications to Fluorescence-Activated Cell Sorting Data , 2009, 2009 Digital Image Computing: Techniques and Applications.

[38]  John Ferbas,et al.  Mixture modeling approach to flow cytometry data , 2008, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[39]  Ali Bashashati,et al.  A Survey of Flow Cytometry Data Analysis Methods , 2009, Adv. Bioinformatics.