Modeling of inter-sample variation in flow cytometric data with the joint clustering and matching procedure

Cytometry Part A

Volumn 89, Issue 1, 2016, Pages 30-43

  Lee, Sharon X ^a   McLachlan, Geoffrey J ^a   Pyne, Saumyadipta ^b,c  

^a UNIVERSITY OF QUEENSLAND   (Australia)

^b PUBLIC HEALTH FOUNDATION OF INDIA   (India)

^c UNIVERSITY OF HYDERABAD   (India)

Author keywords

Class template; Classification; Clustering; EM algorithm; Flow cytometry; Inter sample variation; JCM; Matching; Skew mixture models

Indexed keywords

BIOLOGICAL MARKER; MEMBRANE PROTEIN;

ALGORITHM; AUTOMATED PATTERN RECOGNITION; BIOLOGY; BLOOD; CLUSTER ANALYSIS; FLOW CYTOMETRY; HUMAN; INFORMATION PROCESSING; LEUKEMIA, MYELOID, ACUTE; LYMPHOMA, FOLLICULAR; PROCEDURES; STATISTICAL ANALYSIS; THEORETICAL MODEL; WEST NILE FEVER;

ALGORITHMS; AUTOMATIC DATA PROCESSING; BIOMARKERS; CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; DATA INTERPRETATION, STATISTICAL; FLOW CYTOMETRY; HUMANS; LEUKEMIA, MYELOID, ACUTE; LYMPHOMA, FOLLICULAR; MEMBRANE PROTEINS; MODELS, THEORETICAL; PATTERN RECOGNITION, AUTOMATED; WEST NILE FEVER;

EID: 84983143258     PISSN: 15524922     EISSN: 15524930     Source Type: Journal    
DOI: 10.1002/cyto.a.22789     Document Type: Article

References (29)

1. Tanner SD, Bandura DR, Ornatsky O, Baranov VI, Nitz M, Winnik MA. Flow cytometer with mass spectrometer detection for massively multiplexed single-cell biomarker assay. Pure Appl Chem 2008;80:2627-2641.
2. Bendall SC, Simonds EF, Qiu P, Amir EaD Krutzik PO, Finck R, Bruggner RV, Melamed R, Trejo A, Ornatsky OI, Balderas RS, Plevritis SK, Sachs K, Pe'er D, Tanner SD Nolan GP. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 2011;332:687-696.
3. Aghaeepour N, Finak G, Hoos H, Mosmann T, Gottardo R, Brinkman RR, Scheuermann RH, The FLOWCAP Consortium, the DREAM Consortium. Critical assessment of automated flow cytometry analysis techniques. Nature Methods 2013;10:228-238.
4. Pyne S, Hu X, Wang K, Rossin E, Lin TI, Maier LM, Baecher-Allan C, McLachlan GJ, Tamayo P, Hafler DA, et al. Automated high-dimensional flow cytometric data analysis. Proc Natl Acad Sci USA 2009;106:8519-8524.
5. Frühwirth-Schnatter S, Pyne S. Bayesian inference for finite mixtures of univariate and multivariate skew-normal and skew-t distributions. Biostatistics 2010;11:317-336.
6. Cron A, Gouttefangeas C, Frelinger J, Lin L, Singh SK, Britten CM, Welters MJ, van der Burg SH, West M, Chan C. Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples. PLoS Comput Biol 2013;9.
7. Pyne S, Lee SX, Wang K, Irish J, Tamayo P, Nazaire MD, Duong T, Ng SK, Hafler D, Levy R, et al. Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data. Plos One 2014;9:e100334
8. Dundar M, Akova F, Yerebakan HZ, Rajwa B. A non-parametric bayesian model for joint cell clustering and cluster matching: Identification of anomalous sample phenotypes with random effects. BMC Bioinformatics 2014;15:314
9. Rossin E, Lin TI, Ho HJ, Mentzer S, Pyne S. A framework for analytical characterization of monoclonal antibodies based on reactivity profiles in different tissues. Bioinformatics 2011;27:2746-2753.
10. Ho HJ, Lin TI, Chang HH, Haase HB, Huang S, Pyne S. Parametric modeling of cellular state transitions as measured with flow cytometry different tissues. BMC Bioinformatics 2012;13:S5
11. Ho HJ, Pyne S, Lin TI. Maximum likelihood inference for mixtures of skew student-t-normal distributions through practical EM-type algorithms. Stat Comput 2012;22:287-299.
12. Naim I, Datta S, Sharma G, Cavenaugh JS, Mosmann TR. Swift: Scalable weighted iterative sampling for flow cytometry clustering. In: IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP). 2010, pp 509-512.
13. Hahne F, Khodabakhshi AH, Bashashati A, Wong CJ, Gascoyne RD, Weng AP, Seyfert-Margolis V, Bourcier K, Asare A, et al. Per-channel basis normalization methods for flow cytometry data. Cytometry A 2010;77:121-131.
14. O'Neill K, Aghaeepour N, Parker J, Hogge D, Karsan A, Dalal B, Brinkman RR. Deep profiling of multitube flow cytometry data. Bioinformatics 2015;31:1623-1631.
15. Naim I, Datta S, Rebhahn J, Cavenaugh JS, Mosmann TR, Sharma G. Swift: Scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 1: Algorithm design. Cytometry A 2014;85:402-421.
16. Dundar M, Yerebakan HZ, Rajwa B. Batch discovery of recurring rare classes toward identifying anomalous samples. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. New York, NY: ACM, 2014, pp 223-232.
17. Bruggner RV, Bodenmiller B, Dill DL, Tibshirani RJ, Nolan GP. Automated identification of stratifying signatures in cellular subpopulations. Proc Natl Acad Sci 2014;111:E2770-E2777.
18. Ge Y, Sealfon SC. Flowpeaks: A fast unsupervised clustering for flow cytometry data via k-means and density peak finding. Bioinformatics 2012;28:2052-2058.
19. Irish JM, Myklebust JH, Alizadeh AA, Houot R, Sharman JP, Czerwinski DK, Nolan GP, Levy R. Bcell signaling networks reveal a negative prognostic human lymphoma cell subset that emerges during tumor progression. Proc Natl Acad Sci U S A 2010;107:12747-12754.
20. Irish JM, Kotecha N, Nolan GP. Mapping normal and cancer cell signalling networks: Towards single-cell proteomics. Nat Rev Cancer 2006;6:146-155.
21. Lee SX, McLachlan GJ. On mixtures of skew-normal and skew t-distributions. Adv Data Anal Classification 2013;7:241-266.
22. Lee S, McLachlan GJ. Finite mixtures of multivariate skew t-distributions: Some recent and new results. Stat Comput 2014;24:181-202.
23. Azzalini A, Capitanio A. Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t distribution. J R Stat Soc Ser B 2003;65:367-389.
24. McLachlan GJ, Peel D. Finite Mixture Models. New York: Wiley Series in Probability and Statistics; 2000.
25. Mesaros A, Heittola T, Palomaki K. Analysis of acoustic-semantic relationship for diversely annotated real-world audio data. In: IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP. 2013, pp. 813-817.
26. Hubert L, Arabie P. Comparing partitions. J Classification 1985;2:193-218.
27. Azad A, Rajwa B, Pothen A. Immunophenotypes of acute myeloid leukemia from flow cytometry data using templates. arXiv:1403.6358 [q-bio.QM] 2014.
28. Qiu P. Inferring phenotypic properties from single-cell characteristics. Plos One 2012;7:e37038
29. Pyne S, Lee S, McLachlan G. Nature and man: The goal of bio-security in the course of rapid and inevitable human development. J Ind Soc Agric Stat 2015;69:117-125.