Reconstructing tumor-wise protein expression in tissue microarray studies using a Bayesian cell mixture model

Bioinformatics

Volumn 24, Issue 24, 2008, Pages 2880-2886

  Shen, Ronglai ^a   Taylor, Jeremy M G ^b   Ghosh, Debashis ^c  

^a MEMORIAL SLOAN KETTERING CANCER CENTER   (United States)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

^c UNIVERSITY OF MICHIGAN SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MARKER; TUMOR MARKER;

ARTICLE; BAYES THEOREM; CANCER CELL; CANCER SURVIVAL; CONTROLLED STUDY; GENE EXPRESSION REGULATION; HUMAN; HUMAN TISSUE; MAJOR CLINICAL STUDY; MALE; MATHEMATICAL COMPUTING; MONTE CARLO METHOD; NEEDLE BIOPSY; PRIORITY JOURNAL; PROBABILITY; PROSTATE CANCER; PROTEIN EXPRESSION; SIMULATION; STAINING; TISSUE MICROARRAY;

EID: 57249083982     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/btn536     Document Type: Article

References (14)

1. Demichelis,F. et al. (2006) A hierarchical naive Bayes model for handling sample heterogeneity in classification problems: An application to tissue microarrays. BMC Bioinformatics, 24, 514-521.
2. Divito,K.A. et al. (2004) Automated quantitative analysis of tissue microarrays reveals an association between high bcl-2 expression and improved outcome in melanoma. Cancer Res., 64, 8773-8777.
3. Gelfand,A.E. and Smith,A.F.M. (1990) Sampling-based approaches to calculating marginal densities. J. Am. Stat. Assoc., 85, 398-409.
4. Geman,S. and Geman,D. (1984) Stochastic relaxation, Gibbs distributions, and the bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell., 6, 721-741.
5. Ghosh,M. and Rao,J. (1994) Small area estimation: An appraisal. Stat. Sci., 9, 55-93.
6. Ghosh,M. et al. (1998) Generalized linear models for small-area estimation. J. Am. Stat. Assoc., 93, 273-332.
7. Kononen,J. et al. (1998) Tissue microarrays for high-throughput molecular profiling of tumor specimens. Nat. Med., 4, 844-847.
8. Liu,X. et al. (2004) Statistical methods for analyzing tissue microarray data. J. Biopharm. Stat., 14, 671-685.
9. Pfreffermann,D. (2002) Small area estimation-new developments and directions. Int. Stat. Rev., 70, 125-143.
10. Rao,J. (1999) Some recent advances in model based small area estimation. Surv. Methodol., 25, 175-186.
11. Rubin,M.A. et al. (2005) Decreased α-Methylacyl CoA racemase expression in localized prostate cancer is associated with an increased rate of biochemical recurrence and cancer-specific death. Cancer Epidemiol. Biomarkers Prev., 14, 1424-1431.
12. Seligson,D.B. et al. (2005) Global histone modification patterns predict risk of prostate cancer recurrence. Nature 435, 1262-1266.
13. Shen,R. (2007) Statistical Methods in Cancer Genomics. PhD dissertation, University of Michigan.
14. Shen,R. et al. (2008) Modeling intra-tumor protein expression heterogeneity in tissue microarray experiments. Stat. Med., 27 1944-1959.