GenePattern flow cytometry suite

Source Code for Biology and Medicine

Ryan R Brinkman¹  Rafick-Pierre Sekaly²  Richard H Scheuermann³  Peter Wilkinson²  Jill P Mesirov⁵  Michael Reich⁵  Ted Liefeld⁵  Yu Qian³  Barbara Allen Hill⁵  Marc-Danie Nazaire⁵  Peter Carr⁵  Karin Breuer⁴  Aaron Barsky⁶  Josef Spidlen⁶ 

[1] Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada;J. Craig Venter Institute, San Diego, CA, USA;Vaccine and Gene Therapy Institute of Florida, Port Saint Lucie, FL, USA;Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, British Columbia, Canada;Computational Biology and Bioinformatics, Broad Institute of MIT and Harvard, Cambridge, MA, USA;Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC, Canada

关键词: Clustering;    Normalization;    Quality assessment;    Data preprocessing;    FCS;    GenePattern;    Data analysis;    Flow cytometry;   

Others  :  805272
DOI  :  10.1186/1751-0473-8-14

 received in 2013-01-10, accepted in 2013-06-21,  发布年份 2013

【 摘 要 】

Background

Traditional 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.

Results

In 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.

Conclusions

GenePattern 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.

【 授权许可】

   
2013 Spidlen et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

【 参考文献 】

• [1]Krutzik PO, Crane JM, Clutter MR, Nolan GP: High-content single-cell drug screening with phosphospecific flow cytometry. Nat Chem Biol 2008, 4(2):132-142.
• [2]Darzynkiewicz Z, Crissman H, Jacobberger JW: Cytometry of the cell cycle: cycling through history. Cytom Part A 2004, 58A:21-32.
• [3]De Rosa SC, Brenchley JM, Roederer M: Beyond six colors: a new era in flow cytometry. Nat Med 2003, 9:112-117.
• [4]Mahnke YD, Roederer M: Optimizing a Multicolor Immunophenotyping Assay. Clin Lab Med 2007, 27(3):469-485.
• [5]DVS Sciences: CyTOF®; Instrument. [ http://www.dvssciences.com/cytof-instrument.php webcite]
• [6]Lugli E, Roederer M, Cossarizza A: Data analysis in flow cytometry: The future just started. Cytom Part A 2010, 77A:705-713.
• [7]Bashashati A, Brinkman RR: A survey of flow cytometry data analysis methods. Adv Bioinformatics 2009, 2009:1-19. Article ID 584603, http://dx.doi.org/10.1155/2009/584603 webcite, http://www.hindawi.com/journals/abi/2009/584603/ webcite
• [8]Gentleman R, Carey V, Bates D, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini A, Sawitzki G, Smith C, Smyth G, Tierney L, Yang J, Zhang J: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 2004, 5(10):R80. BioMed Central Full Text
• [9]Reich M, Liefeld T, Gould J, Lerner J, Tamayo P, Mesirov JP: GenePattern 2.0. Nat Genet 2006, 38(5):500-501.
• [10]Mesirov JP: Accessible reproducible research. Science 2010, 327(5964):415-416.
• [11]Hahne F, LeMeur N, Brinkman R, Ellis B, Haaland P, Sarkar D, Spidlen J, Strain E, Gentleman R: flowCore: a Bioconductor package for high throughput flow cytometry. BMC Bioinformatics 2009, 106. [ http://www.biomedcentral.com/1471-2105/10/106 webcite]
• [12]Strain E, Hahne F, Brinkman RR, Haaland P: Analysis of high-throughput flow cytometry data using plateCore. Adv Bioinformatics 2009, 2009:10. Article ID 356141. http://dx.doi.org/10.1155/2009/356141 webcite., http://www.hindawi.com/journals/abi/2009/356141/ webcite
• [13]Le Meur N, Rossini A, Gasparetto M, Smith C, Brinkman RR, Gentleman R: Data quality assessment of ungated flow cytometry data in high throughput experiments. Cytom Part A 2007, 76(6):393-403.
• [14]Hahne F, Khodabakhshi AH, Bashashati A, Wong CJ, Gascoyne RD, Weng AP, Seifert-Margolis V, Bourcier K, Asare A, Lumley T, Gentleman R, Brinkman RR: Per-channel basis normalization methods for flow cytometry data. Cytom Part A 2009, 77(2):121-131.
• [15]Lo K, Hahne F, Brinkman R, Gottardo R: flowClust: a Bioconductor package for automated gating of flow cytometry data. BMC Bioinformatics 2009, 10:145. BioMed Central Full Text
• [16]Finak G, Bashashati A, Brinkman RR, Gottardo RR: Merging mixture components for cell population identification in flow cytometry. Adv Bioinformatics 2009, 2009:1-12. Article ID 247646. http://dx.doi.org/10.1155/2009/247646 webcite, http://www.hindawi.com/journals/abi/2009/247646/ webcite
• [17]Zare H, Shooshtari P, Gupta A, Brinkman R: Data reduction for spectral clustering to analyze high throughput flow cytometry data. BMC Bioinformatics 2010, 403. [ http://www.biomedcentral.com/1471-2105/11/403 webcite]
• [18]Aghaeepour N, Nikolic R, Hoos H, Brinkman R: Rapid cell population identification in flow cytometry data. Cytom Part A 2011, 79:6-13.
• [19]Proposed API for Reading and Writing FCS files [ http://murphylab.web.cmu.edu/FCSAPI/FCSAPIv0108.html webcite]
• [20]CFCS - Java library for Reading and Writing FCS files [ http://sf.net/projects/flowcyt/files/FCS/FCS%20Utilities/CFCS.091019.zip/download webcite]
• [21]Spidlen J, Moore W, Parks D, Goldberg M, Bray C, Bierre P, Gorombey P, Hyun B, Hubbard M, Lange S, Lefebvre R, Leif RR, Novo D, Ostruszka L, Treister A, Wood J, Murphy RF, Roederer M, Sudar D, Zigon R, Brinkman RR: Data file standard for flow cytometry, version FCS 3.1. Cytom Part A 2010, 77:97-100.
• [22]Parks DR, Roederer M, Moore WA: A new “Logicle” display method avoids deceptive effects of logarithmic scaling for low signals and compensated data. Cytom Part A 2006, 69(6):541-551.
• [23]Moore WA, Parks DR: Update for the logicle data scale including operational code implementations. Cytom Part A 2012, 81(4):273-277.
• [24]Spidlen J, Leif RC, Moore W, Roederer M, Brinkman RR: Gating-ML: XML-based gating descriptions in flow cytometry. Cytom Part A 2008, 73(12):1151-1157.
• [25]Bagwell CB: Hyperlog – a flexible log-like transform for negative, zero, and positive valued data. Cytom Part A 2005, 64:34-42.
• [26]Battye FL: A mathematically simple alternative to the logarithmic transform for flow cytometric fluorescence data displays. [ http://tinyurl.com/BattyeSplitscale webcite]
• [27]Wang K, Ng SK, McLachlan GJ: Multivariate skew t mixture models: applications to fluorescence-activated cell sorting data. Digit Image Comput: Tech Appl 2009, 0:526-531.
• [28]Rogers WT, Holyst HA: FlowFP: A bioconductor package for fingerprinting flow cytometric data. Adv Bioinformatics 2009, 1-11. Article ID 193947. http://dx.doi.org/10.1155/2009/193947 webcite, http://www.hindawi.com/journals/abi/2009/193947/ webcite
• [29]Naumann U, Luta G, Wand M: The curvHDR method for gating flow cytometry samples. BMC Bioinformatics 2010, 11:44. BioMed Central Full Text
• [30]Achuthanandam R, Quinn J, Capocasale R, Bugelski P, Hrebien L, Kam M: Sequential univariate gating approach to study the effects of erythropoietin in murine bone marrow. Cytom Part A 2008, 73(8):702-714.
• [31]Boedigheimer MJ, Ferbas J: Mixture modeling approach to flow cytometry data. Cytom Part A 2008, 73(5):421-429.
• [32]Roederer M, Hardy RR: Frequency difference gating: A multivariate method for identifying subsets that differ between samples. Cytom Part A 2001, 45:56-64.
• [33]Pyne S, Hu X, Wang K, Rossin E, Lin T, Maier L, Baecher-Allan C, McLachlan G, Tamayo P, Hafler D, De Jager P, Mesirov J: Automated high-dimensional flow cytometric data analysis. Proc Natl Acad Sci 2009, 106:8519-8524.
• [34]Scheuermann RH, Qian Y, Wei C, Sanz I: ImmPort FLOCK: Automated cell population identification in high dimensional flow cytometry data. J Immunol 2009, 182:42.17.
• [35]Qian Y, Wei C, Eun-Hyung Lee F, Campbell J, Halliley J, Lee JA, Cai J, Kong YM, Thomson E, Dunn P, Seegmiller AC, Karandikar NJ, Tipton CM, Mosmann T, Sanz I, Scheuermann RH, Sadat E: 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. Cytom Part B: Clin Cytom 2010, 78B:S69-S82.
• [36]Bakker ST, De Grooth B, Greve J: Cluster analysis of flow cytometric list mode data on a personal computer. Cytom Part A 1993, 14(6):649-659.
• [37]Latorre I, De Souza-Galvao M, Ruiz-Manzano J, Lacoma A, Prat C, Fuenzalida L, Altet N, Ausina V, Dominguez J: Quantitative evaluation of T-cell response after specific antigen stimulation in active and latent tuberculosis infection in adults and children. Diagn Microbiol Infect Dis 2009, 65(3):236-246.
• [38]Darrah PA, Patel DT, De Luca PM, Davey DF, Flynn BJ, Hoff ST, Andersen P, Reed SG, Morris SL, Roederer M, Seder RA, Lindsay RW: Multifunctional TH1 cells define a correlate of vaccine-mediated protection against Leishmania major. Nat Med 2007, 13(7):843-850.
• [39]Dykstra B, Kent D, Bowie M, McCaffrey L, Hamilton M, Lyons K, Lee SJ, Brinkman R, Eaves C: Long-term propagation of distinct hematopoietic differentiation programs In Vivo. Cell Stem Cell 2007, 1(2):218-229. http://dx.doi.org/10.1016/j.stem.2007.05.015 webcite
• [40]Kotecha N, Krutzik PO, Irish JM: Web-based Analysis and Publication of Flow Cytometry Experiments. John Wiley and Sons, Inc: 111 River Street, Hoboken, NJ 07030-5774, USA. Current Protocols in Cytometry 2010 chap. Chapter 10, Unit 10.17
• [41]Qiu P, Simonds EF, Bendall SC, Gibbs KD, Bruggner RV, Linderman MD, Sachs K, Nolan GP, Plevritis SK: Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat Biotechnol 2011, 29:886-891.