outline Pre-process of data Model Fitting for Identifying Differential Expression Network Inference Microarray data analysis using BioConductor Guiyuan Lei Centre for Integrated Systems Biology of Ageing and Nutrition (CISBAN) School of Mathematics & Statistics Newcastle University http://www.mas.ncl.ac.uk/ ∼ ngl9/ 6 June, 2008 Guiyuan Lei Microarray data analysis using BioConductor
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Microarray data analysis using BioConductorngl9/topics/inotes/MicroarrayAnalysis4CRI.pdf · A microarray analysis for differential gene expression in the soybean genome using Bioconductor
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outlinePre-process of data
Model Fitting for Identifying Differential ExpressionNetwork Inference
Microarray data analysis using BioConductor
Guiyuan Lei
Centre for Integrated Systems Biology of Ageing and Nutrition (CISBAN)School of Mathematics & Statistics
Model Fitting for Identifying Differential ExpressionNetwork Inference
Entering data into BioconductorExtraction of Cerevisiae probesetsExploratory data analysisNormalisationPrincipal Component Analysis
Pre-process of data
Entering data into BioconductorExtraction of Cerevisiae probesetsExploratory data analysisNormalising Microarray dataProbeset level expression to gene level expressionPrincipal Component Analysis
Guiyuan Lei Microarray data analysis using BioConductor
outlinePre-process of data
Model Fitting for Identifying Differential ExpressionNetwork Inference
Entering data into BioconductorExtraction of Cerevisiae probesetsExploratory data analysisNormalisationPrincipal Component Analysis
Guiyuan Lei Microarray data analysis using BioConductor
outlinePre-process of data
Model Fitting for Identifying Differential ExpressionNetwork Inference
Entering data into BioconductorExtraction of Cerevisiae probesetsExploratory data analysisNormalisationPrincipal Component Analysis
Principal Component Analysis [5]
−20 −10 0 10 20 30 40
−10
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1st PC
2nd
PC
Clustering Samples
Principal Component Projection
yeast01.cel
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yeast09.celyeast10.cel
yeast11.cel
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yeast16.cel
yeast17.celyeast18.cel
yeast19.celyeast20.cel
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yeast25.cel
yeast26.celyeast27.cel
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Guiyuan Lei Microarray data analysis using BioConductor
outlinePre-process of data
Model Fitting for Identifying Differential ExpressionNetwork Inference
LimmaPlot time course for top differential expressionHeatmap
Model Fitting for Identifying Differential Expression
Limma model [4]Construct design matrixConstruct constrasts
Up-regulated and down-regulated listPlot time course for top differential expressionVolcano Plot for viewing p-value and fold-changeHeatmap
Guiyuan Lei Microarray data analysis using BioConductor
outlinePre-process of data
Model Fitting for Identifying Differential ExpressionNetwork Inference
LimmaPlot time course for top differential expressionHeatmap
Up and down regulated listFor identifying differential expression, combine the contrasts bycomparing mutant type and wild type at time point 1,2,3 and 4.
Guiyuan Lei Microarray data analysis using BioConductor
outlinePre-process of data
Model Fitting for Identifying Differential ExpressionNetwork Inference
Network Inference
GeneNet [2]: simple, quick, but not robustMetropolis Hastings for decomposable graphs (MH-d) [3].Computationaly intensive but more stableReversiable Jump MCMC for time course data
Yeast Network
Gene 41
Gene 87
Gene 64
Gene 99Gene 57
Gene 81
Gene 94 Gene 91
Gene 98
Gene 96
Gene 58
Gene 68
Gene 88
Gene 92
Gene 80
Gene 95
Gene 69
Gene 97
Gene 65
Gene 63
Gene 66Gene 74
Gene 49
Gene 67 Gene 89
Gene 70
Gene 82Gene 75
Gene 100
Gene 90
Gene 22
Gene 53
Gene 44
Gene 93
Gene 60
Gene 56
Gene 59Gene 55Gene 76
Gene 54
Gene 34
Gene 10
Gene 35
Gene 71
Gene 28
Gene 50
Gene 84
Gene 79
Gene 73
Gene 16
Gene 86
Gene 42
Gene 20
Gene 31
Gene 27
Gene 40Gene 39
Gene 77
Gene 18
Gene 38
Gene 24
Gene 48
Gene 33
Gene 15
Gene 32
Gene 14
Gene 83
Gene 11
Gene 23
Gene 25
Gene 36 Gene 4
Gene 52
Figure: Inferred network by GeneNet package for top 100differentially expressed Yeast genes
Guiyuan Lei Microarray data analysis using BioConductor
outlinePre-process of data
Model Fitting for Identifying Differential ExpressionNetwork Inference
References
W.G. Alvord, J.A. Roayaei, O.A. Quinones, and K.T. Schneider.A microarray analysis for differential gene expression in the soybean genomeusing Bioconductor and R.Briefings in Bioinformatics, September 29, 2007.
Schafer J. and K. Strimmer.An empirical bayes approach to inferring large-scale gene association networks.Bioinformatics, 21:754–764, 2005.
Beatrix Jones, Carlos Carvalho, Adrian Dobra, Chris Hans, Chris Carter, andMike West.Experiments in stochastic computation for high-dimensional graphical models.Statistical Science, 20(4):388–400, 2005.
G.K. Smyth.Linear models and empirical Bayes methods for assessing differential expressionin microarray experiments.Statistical Applications in Genetics and Molecular Biology, 3, 2004.
E. Wit and J. Mcclure.Statistics for Microarrays: Design, Analysis and Inference.Wiley, 2004.
Guiyuan Lei Microarray data analysis using BioConductor