Analysis of genome-scale count data in Bioconductor

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Analysis in R• R/Bioconductor: open-source statisticalsoftware• Four packages currently available for DEanalysis of count data in R• DEGSeq (Poisson), edgeR, baySeq andDESeq (NB)• For NB, variations in the implementation ofinformation sharing and statistical testing• We work on edgeR, so this is our favourite
Reading in data• Read the data into R session using a‘targets’ file• The function readDGE() creates a ‘DGEList’object which stores our data in R> library(edgeR)!> targets
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Analysis of genome-scalecount data
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Sequencing experiments used for:Seq
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Example:RNA-seq(or similar)for gene
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Summarization
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What does genome-scale count• e.g
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Count data• Count data (e.g. RNA-
Page 13 and 14: Poisson arises naturally frommultin
Page 15 and 16: Technical replicationDNApopulationL
Page 17 and 18: Statistical models• For count dat
Page 19 and 20: Poisson describes technicalvariance
Page 21 and 22: Overdispersion: extra-Poissonvariat
Page 23 and 24: Sources of variation: technical and
Page 25 and 26: Coefficient of variation• Dispers
Page 27 and 28: Common dispersion model• One appr
Page 29 and 30: High read number is relevant for RN
Page 31 and 32: “Composition” ofsampled DNA can
Page 33 and 34: Outline1. Applications2. Summarizat
Page 35 and 36: Extending the common dispersionmode
Page 37 and 38: Weighted Likelihood• WL is the in
Page 39 and 40: Statistical testing for countdata
Page 41 and 42: Significance testing• Simple hypo
Page 43 and 44: Further considerations• RNA-seq e
Page 45 and 46: Binomial exact testingobserved• P
Page 47 and 48: Effect of dispersion> d.tuch$counts
Page 49 and 50: Limitations of exact testing• E.g
Page 51 and 52: More complicated experiments• We
Page 53 and 54: Example: Cancer dataset• RNA-seq
Page 55 and 56: GLM results> glm.res.com[o1[1:10],]
Page 57 and 58: Mean-dispersion relationship• The
Page 59 and 60: Also seems true for more datasets
Page 61 and 62: Concluding remarks• Must understa
Page 63: R Practical
Page 67 and 68: Multidimensionalscaling plot• Use
Page 69 and 70: Exact test in edgeR: common disp> d
Page 71: Exact test in edgeR: tagwise disp>
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Analysis of genome-scale count data in Bioconductor

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