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A Quantitative Overview to Gene Expression Profiling in Animal Genetics Armidale Animal Breeding Summer Course, UNE, Feb. 2006 Analysis of (cDNA) Microarray.

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Presentation on theme: "A Quantitative Overview to Gene Expression Profiling in Animal Genetics Armidale Animal Breeding Summer Course, UNE, Feb. 2006 Analysis of (cDNA) Microarray."— Presentation transcript:

1 A Quantitative Overview to Gene Expression Profiling in Animal Genetics Armidale Animal Breeding Summer Course, UNE, Feb. 2006 Analysis of (cDNA) Microarray Data: Part VI. Bayesmix A software program for Bayesian analysis of mixture models with an application to model-based clustering of microarray gene expression data

2 A Quantitative Overview to Gene Expression Profiling in Animal Genetics Armidale Animal Breeding Summer Course, UNE, Feb. 2006 Bayesmix

3 A Quantitative Overview to Gene Expression Profiling in Animal Genetics Armidale Animal Breeding Summer Course, UNE, Feb. 2006 Introduction: Concept & Analysis possibilities Challenges for microarray Technical Concerns: Software development Software comparison Final remarks Contents Bayesmix

4 A Quantitative Overview to Gene Expression Profiling in Animal Genetics Armidale Animal Breeding Summer Course, UNE, Feb. 2006 Concept & Analysis Possibilities Bayesmix

5 A Quantitative Overview to Gene Expression Profiling in Animal Genetics Armidale Animal Breeding Summer Course, UNE, Feb. 2006 Challenges for Microarrays 1.Class Comparison 2.Class Discovery Detecting Outliers Bayesmix

6 A Quantitative Overview to Gene Expression Profiling in Animal Genetics Armidale Animal Breeding Summer Course, UNE, Feb. 2006 BAYESMIX: Software development FORTRAN 90 Up to 5 Components Gibbs Sampling Chain Length 12,000 Bayesmix

7 A Quantitative Overview to Gene Expression Profiling in Animal Genetics Armidale Animal Breeding Summer Course, UNE, Feb. 2006 BAYESMIX: Software development Bayesmix

8 A Quantitative Overview to Gene Expression Profiling in Animal Genetics Armidale Animal Breeding Summer Course, UNE, Feb. 2006 BAYESMIX: Software comparison (EMMIX) Bayesmix

9 A Quantitative Overview to Gene Expression Profiling in Animal Genetics Armidale Animal Breeding Summer Course, UNE, Feb. 2006 BAYESMIX: Software test Bayesmix N = 1,000 N = 2,000N = 4,747

10 A Quantitative Overview to Gene Expression Profiling in Animal Genetics Armidale Animal Breeding Summer Course, UNE, Feb. 2006 DataNProcedureLogLParameter Cluster%MeanVar. 11,000True-150.0-10.010.0 250.010.0 ML-3,233.50150.0-9.979.46 250.010.029.47 BAYESMIX-3,240.42150.0-9.619.59 250.09.669.61 22,000True-110.0-10.010.0 280.00.05.0 310.0 ML-5,679.40110.5-9.529.93 279.00.014.55 310.59.9810.34 BAYESMIX-5,692.53110.3-8.808.93 279.60.044.62 310.19.209.50 BAYESMIX: Software test (results) Bayesmix

11 A Quantitative Overview to Gene Expression Profiling in Animal Genetics Armidale Animal Breeding Summer Course, UNE, Feb. 2006 DATA 3EMMIX: (logL = -11,864) 0.044  N(-0.87, 67.46) + 0.590  N(-2.30, 10.42) + 0.366  N(-2.41, 2.32) BAYESMIX: (logL = -11,944) 0.008  N( -1.02, 208.79) + 0.981  N( -2.26, 7.61) + 0.011  N(-11.18, 3.63) BAYESMIX: Software test (results) Bayesmix

12 A Quantitative Overview to Gene Expression Profiling in Animal Genetics Armidale Animal Breeding Summer Course, UNE, Feb. 2006 BAYESMIX: Software test (results) Bayesmix

13 A Quantitative Overview to Gene Expression Profiling in Animal Genetics Armidale Animal Breeding Summer Course, UNE, Feb. 2006 CPU Time (sec.) DATA 11,000 74 314 DATA 22,000154 2,265 DATA 34,74736055,359 (15.4hr!) BAYESMIX EMMIX NB: EMMIX can be modified to make it faster BAYESMIX: Software comparison (EMMIX) Bayesmix

14 A Quantitative Overview to Gene Expression Profiling in Animal Genetics Armidale Animal Breeding Summer Course, UNE, Feb. 2006 Conclusions BAYESMIX works Some features require further development: Flexibility in Chain Length (CODA) Unknown Number of Components Multivariate EMMIX is a far more complete software Mixtures have other applications (eg. Selective Genotyping) Both softwares are available ……and will be used in this course Bayesmix

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