Automated identification of stratifying signatures in cellular subpopulations

Proceedings of the National Academy of Sciences of the United States of America

Volumn 111, Issue 26, 2014, Pages

  Bruggner, Robert V ^a,b   Bodenmiller, Bernd ^b   Dill, David L ^c   Tibshirani, Robert J ^d   Nolan, Garry P ^b  

^a STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b UNIVERSITY OF ZURICH   (Switzerland)

^c Stanford University   (United States)

^d STANFORD UNIVERSITY   (United States)

Author keywords

Biomarker discovery; Informatics

Indexed keywords

B LYMPHOCYTE RECEPTOR; BIOLOGICAL MARKER; FC RECEPTOR;

ARTICLE; CELL SUBPOPULATION; CITRUS METHOD; CLUSTER ANALYSIS; CROSS LINKING; CYTOMETRY; MACHINE LEARNING; PERIPHERAL BLOOD MONONUCLEAR CELL; PRIORITY JOURNAL;

BIOMARKER DISCOVERY; INFORMATICS;

ALGORITHMS; BLOOD CELLS; CELLS; COMPUTATIONAL BIOLOGY; FLOW CYTOMETRY; HUMANS; SOFTWARE; T-LYMPHOCYTE SUBSETS;

EID: 84903703398     PISSN: 00278424     EISSN: 10916490     Source Type: Journal    
DOI: 10.1073/pnas.1408792111     Document Type: Article

References (30)

1. Kotecha N, et al. (2008) Single-cell profiling identifies aberrant STAT5 activation in myeloid malignancies with specific clinical and biologic correlates. Cancer Cell 14(4):335-343.
2. Basso G, et al. (2009) Risk of relapse of childhood acute lymphoblastic leukemia is predicted by flow cytometric measurement of residual disease on day 15 bone marrow. J Clin Oncol 27(31):5168-5174.
3. Irish JM, et al. (2010) B-cell signaling networks reveal a negative prognostic human lymphoma cell subset that emerges during tumor progression. Proc Natl Acad Sci USA 107(29):12747-12754.
4. Ganesan A, et al.; Infectious Disease Clinical Research Program HIV Working Group (2010) Immunologic and virologic events in early HIV infection predict subsequent rate of progression. J Infect Dis 201(2):272-284.
5. Freeman SD, et al. (2013) Prognostic relevance of treatment response measured by flow cytometric residual disease detection in older patients with acute myeloid leukemia. J Clin Oncol 31(32):4123-4131.
6. Bendall SC, et al. (2011) Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332(6030):687-696.
7. Bodenmiller B, et al. (2012) Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators. Nat Biotechnol 30(9):858-867.
8. Maecker HT, et al. (2005) Standardization of cytokine flow cytometry assays. BMC Immunol 6:13.
9. Murphy RF (1985) Automated identification of subpopulations in flow cytometric list mode data using cluster analysis. Cytometry 6(4):302-309.
10. Aghaeepour N, Nikolic R, Hoos HH, Brinkman RR (2011) Rapid cell population identification in flow cytometry data. Cytometry A 79(1):6-13.
11. Zare H, Shooshtari P, Gupta A, Brinkman RR (2010) Data reduction for spectral clustering to analyze high throughput flow cytometry data. BMC Bioinformatics 11:403.
12. Lo K, Brinkman RR, Gottardo R (2008) Automated gating of flow cytometry data via robust model-based clustering. Cytometry A 73(4):321-332.
13. Pyne S, et al. (2009) Automated high-dimensional flow cytometric data analysis. Proc Natl Acad Sci USA 106(21):8519-8524.
14. Finak G, Bashashati A, Brinkman R, Gottardo R (2009) Merging mixture components for cell population identification in flow cytometry. Adv Bioinforma 2009:247646.
15. Walther G, et al. (2009) Automatic clustering of flow cytometry data with densitybased merging. Adv Bioinforma 2009:686759.
16. Sugár IP, Sealfon SC (2010) Misty Mountain clustering: Application to fast unsupervised flow cytometry gating. BMC Bioinformatics 11:502.
17. Qian Y, et al. (2010) 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. Cytometry B Clin Cytom 78(Suppl 1):S69-S82.
18. Ge Y, Sealfon SC (2012) flowPeaks: A fast unsupervised clustering for flow cytometry data via K-means and density peak finding. Bioinformatics 28(15):2052-2058.
19. Aghaeepour N, et al.; FlowCAP Consortium; DREAM Consortium (2013) Critical assessment of automated flow cytometry data analysis techniques. NatMethods 10(3):228-238.
20. Aghaeepour N, 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.
21. Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc B 58(1):267-288.
22. Tibshirani R, Hastie T, Narasimhan B, Chu G (2002) Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA 99(10):6567-6572.
23. Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M (2012) KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 40(Database issue): D109-D114.
24. Van Rijsbergen CJ (1979) Information retrieval (Butterworths, London) .
25. Weintrob AC, et al. (2008) Increasing age at HIV seroconversion from 18 to 40 years is associated with favorable virologic and immunologic responses to HAART. J Acquired Immune Deficiency Syndromes 49(1):40-47.
26. Simon RM, Subramanian J, Li MC, Menezes S (2011) Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data. Brief Bioinform 12(3):203-214.
27. Heagerty PJ, Lumley T, Pepe MS (2000) Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 56(2):337-344.
28. Appay V, van Lier RAW, Sallusto F, Roederer M (2008) Phenotype and function of human T lymphocyte subsets: Consensus and issues. Cytometry A 73(11):975-983.
29. Roederer M, et al. (1995) CD8 naive T cell counts decrease progressively in HIVinfected adults. J Clin Invest 95(5):2061-2066.
30. Yuan M, Lin Y (2006) Model selection and estimation in regression with grouped variables. J R Stat Soc B 68(1):49-67.