Analysis of genome-scale count data in Bioconductor

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Assessing DE: a statistical problem• Two group setting*: for each gene, estimateλ 1 and λ 2 (mean level for each group) and thedispersionTag ID! A1! A2! A3! A4! B1! B2! B3!ENSG00000215443! 14! 12! 5! 13! 6! 16! 14!ENSG00000222008! 97! 113! 90! 101! 10! 13! 10!ENSG00000101444! 46! 63! 58! 71! 54! 53! 1001!ENSG00000101333! 256! 793! 4156! 5463! 1705! 976! 1320!…"… tens of thousands more tags …"• Conduct a hypothesis test for λ 1 and λ 2• Obtain a p-value for the significance of DEfor each gene*Generalises to n groups
Significance testing• Simple hypothesis testH 0 : λ 1 = λ 2vsH A : λ 1 != λ 2• Easy to state, but requires somesophisticated statistics to testappropriately
Page 1 and 2: Analysis of genome-scalecount data
Page 3 and 4: Sequencing experiments used for:Seq
Page 5 and 6: Example:RNA-seq(or similar)for gene
Page 7 and 8: Summarization
Page 9 and 10: What does genome-scale count• e.g
Page 11 and 12: 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: Statistical testing for countdata
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 and 64: R Practical
Page 65 and 66: Reading in data• Read the data in
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>

Analysis of genome-scale count data in Bioconductor

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