Analysis of genome-scale count data in Bioconductor

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Exact test for NB distribution• Sum of NB RVs is a NB RV, if library sizes(means) are equal, under the null hypothesisof no difference• Conditioning gives ‘overdispersedmultinomial’ from which we can computeexact p-values as per binomial test• Statistical sophistication: quantile-adjustmentto equalise library sizes and enable exact testfor NB model• Size of dispersion has big effect onsignificance of DE
Effect of dispersion> d.tuch$counts[hicom.lotgw,order(d.tuch$samples$group)]!N8 N33 N51 T8 T33 T51!FABP4 62 62 387 0 37 2022!MMP1 68 74 11190 1883 1998 24955!TTTY15 241 1 0 46 0 0!> de.tuch.com$table[hicom.lotgw,]!logConc logFC p.value!FABP4 -15.59 2.016 0.005006!MMP1 -11.59 1.865 0.008713!TTTY15 -17.90 -2.281 0.002998!> de.tuch.tgw$table[hicom.lotgw,]!logConc logFC p.value!FABP4 -15.60 2.018 0.05040!MMP1 -11.59 1.866 0.05771!TTTY15 -17.87 -2.238 0.07857!> d.tuch$common.dispersion![1] 0.3325!> d.tuch$tagwise.dispersion[hicom.lotgw]![1] 0.6694 0.6207 0.9417!
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 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: Binomial exact testingobserved• P
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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