CCAST: A Model-Based Gating Strategy to Isolate Homogeneous Subpopulations in a Heterogeneous Population of Single Cells

PLoS Computational Biology

Volumn 10, Issue 7, 2014, Pages

  Anchang, Benedict ^a   Do, Mary T ^a   Zhao, Xi ^a   Plevritis, Sylvia K ^a  

^a STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BONE; CELL CULTURE; CELL SIGNALING; CLUSTERING ALGORITHMS; COMPUTERIZED TOMOGRAPHY; DATA MINING; DISEASES; POPULATION STATISTICS; SORTING; T-CELLS;

BONE MARROW; BREAST CANCER CELLS; CANCER CELL LINES; CELL DATA; CELL SORTING; CLUSTERING CLASSIFICATIONS; HETEROGENEOUS POPULATIONS; MODEL-BASED OPC; SINGLE CELLS; TREE REPRESENTATION;

DECISION TREES;

CD11B ANTIGEN; CD123 ANTIGEN; CD19 ANTIGEN; CD20 ANTIGEN; CD24 ANTIGEN; CD3 ANTIGEN; CD33 ANTIGEN; CD34 ANTIGEN; CD38 ANTIGEN; CD4 ANTIGEN; CD45 ANTIGEN; CD45RA ANTIGEN; CD8 ANTIGEN; EPITHELIAL CELL ADHESION MOLECULE; THY 1 ANTIGEN; BIOLOGICAL MARKER;

ARTICLE; AUTOMATION; B LYMPHOCYTE; BONE MARROW CELL; BREAST CANCER CELL LINE; CELL FREE SYSTEM; CELL HETEROGENEITY; CELL SELECTION; CELL SUBPOPULATION; CLUSTER ANALYSIS; CLUSTERING CLASSIFICATION AND SORTING TREE; CONTROLLED STUDY; CYTOMETRY; DECISION TREE; HUMAN; HUMAN CELL; INTRACELLULAR SIGNALING; PERIPHERAL BLOOD MONONUCLEAR CELL; PROCESS OPTIMIZATION; PROTEIN EXPRESSION; REPRODUCIBILITY; SINGLE CELL ANALYSIS; T LYMPHOCYTE SUBPOPULATION; ALGORITHM; BIOLOGY; CELL SEPARATION; CHEMISTRY; COMPUTER SIMULATION; PROCEDURES; TUMOR CELL LINE;

ALGORITHMS; BIOLOGICAL MARKERS; CELL LINE, TUMOR; CELL SEPARATION; CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; HUMANS;

EID: 84905483333     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1003664     Document Type: Article

References (28)

1. Polyak K, (2011) Heterogeneity in breast cancer. J Clin Invest 121: 3786-3788.
2. Longo LT, (2012) Tumor Heterogeneity and Personalized Medicine. N Engl J Med 366: 956-957.
3. Gupta PB, Fillmore CM, Jiang G, Shapira SD, Tao K, et al. (2011) Stochastic State Transitions Give Rise to Phenotypic Equilibrium in Populations of Cancer Cells. Cell 146: 633-644.
4. Zapperi S, La Porta CAM, (2012) Do cancer cells undergo phenotypic switching? The case for imperfect cancer stem cell markers. Scientific Reports 2: 441.
5. Turnpenny P, Ellard S (2007) Chapter 8. In: Emery's Elements of Medical Genetics. 13th edition. Atlanta: Elsevier Limited.
6. Slade I, Murray A, Hanks S, Kumer A, Walker L, et al. (2011) Heterogeneity of familial medulloblastoma and contribution of germline PTCH1 and SUFU mutations to sporadic medulloblastoma. Fam Cancer 10(2): 337-42.
7. Heng HH, Bremer SW, Stevens JB, Ye KJ, Liu G, et al. (2009) Genetic and epigenetic heterogeneity in cancer: a genome-centric perspective. J Cell Physiol 220(3): 538-47.
8. Qiu P, Simonds EF, Bendall SC, Gibbs KD Jr, Bruggner RV, et al. (2011) Extracting a Cellular Hierarchy from High-dimensional Cytometry Data with SPADE. Nature Biotechnology 29(10): 886-891.
9. Bendall SC, Simonds EF, Qiu P, Amir ED, Krutzik PO, et al. (2011) Single Cell Mass Cytometry of Differential Immune and Drug Responses Across the Human Hematopoietic Continuum. Science 332(6030): 687-696.
10. Ellis B, Haaland P, Hahne F, Le Meur N, Gopalakrishnan N (2013) Flowcore: Basic structures for flow cytometry data. R package version 1.24.2.
11. Herzenberg LA, James T, Moore WA, Herzenberg LA, Parks DR, (2006) Interpreting flow cytometry data: a guide for the perplexed. Nature Immunology 7: 681-685.
12. Aghaeepour N, Finak G; FlowCAP Consortium; DREAM Consortium, Hoos H, et al. (2013) Critical assessment of automated flow cytometry data analysis techniques. Nature methods 10(3): 228-238.
13. Boedigheimer MJ, Ferbas J, (2008) Mixture modeling approach to flow cytometry data. Cytometry A 73(5): 421-9.
14. Pyne S, Hu X, Wang K, Rossin E, Lin T, Maier L, et al. (2009) Automated high-dimensional flow cytometric data analysis. PNAS 106(21): 8519-24.
15. Finak G, Bashashati A, Brinkman R, Gottardo R, (2009) Merging mixture components for cell population identification in flow cytometry. Advances in Bioinformatics 2009: 247646.
16. Lo K, Hahne F, Brinkman R, Gottardo R, (2009) flowClust: a bioconductor package for automated gating of flow cytometry data. BMC Bioinformatics 14(10): 145.
17. Ray S, Pyne S, (2012) A Computational Framework to Emulate the Human Perspective in Flow Cytometric Data Analysis. PLoS ONE 7(5): e35693.
18. Bashashati A, Brinkman RR, (2009) A Survey of Flow Cytometry Data Analysis Methods. Advances in Bioinformatics 2009: 584603.
19. Benaglia T, Chauveau D, Hunter DR, (2009) An EM-like algorithm for semi- and non-parametric estimation in multivariate mixtures. Journal of Computational and Graphical Statistics 18: 505-526.
20. Mullner D, (2013) fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python. Journal of Statistical Software 53(9): 118 Available: http://www.jstatsoft.org/v53/i09/. Accessed 8 July 2014.
21. Lee G, Stoolman L, Scott C, (2011) Transfer Learning for Automatic Gating of Flow Cytometry Data. JMLR Workshop and Conference Proceedings 27: 155-166.
22. Aghaeepour N, Chattopadhyay PK, Ganesan A, O'Neill K, Zare H, et al. (2012) Early immunologic correlates of HIV protection can be identified from computational analysis of complex multivariate T-cell flow cytometry assays. Bioinformatics 28(7): 1009-1016.
23. Aghaeepour N, Jalali A, O'Neill K, Chattopadhyay PK, Roederer M, et al. (2012) RchyOptimyx: Cellular Hierarchy Optimization for Flow Cytometry. Cytometry Part A 81(12): 1022-1030.
24. Del Giudice I, Matutes E, Morilla R, Morilla A, Owusu-Ankomah K, et al. (2004) The diagnostic value of CD123 in B-cell disorders with hairy or villous lymphocytes. haematologica 89(3): 303-308.
25. Katz G, Shabtai A, Rokach L, Ofek N, et al. (2012) ConfDTree: Improving Decision Trees Using Confidence Intervals. ICDM, IEEE Computer Society 339-348.
26. Rousseeuw PJ, (1987) Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics 20: 53-65.
27. Hothorn T, Hornik K, Zeileis A, (2006) Unbiased recursive partitioning: A conditional inference framework. Journal of Computational and Graphical Statistics 15: 651-674.
28. Strasser H, Weber C, (1999) On the Asymptotic Theory of Permutation Statistics. Mathematical Methods of Statistics 8: 220-250.