Analysis of genome-scale count data in Bioconductor

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Robinson and Oshlack Genome Biology 2010, 11:R25http://genomebiology.com/2010/11/3/R25Poisson replication induces avuvuzela-shaped “MA”-plotRobinson and Oshlack Genome Biology 2http://genomebiology.com/2010/11/3/R2The trimmed mean of M-values normalization methodThe total RNA production, S k ,cannotbeestimateddirectly, since we do not know the expression levels andAnd the theorytrue lengths of every gene. The However, trimmed mean the relative of M-values RNAnThe validates total RNA that production, thisproduction of two samples, f k =S k /S k’ , essentially a globalfold change, can more easily be determined. we do not We know pro-thS kdirectly, behaviour sinceshouldtrue exist: lengths M ispose an empirical strategy that equatesof everythegene.overallHowproduction essentially of a two logrelative-riskfold change, can more easilysamples, f k =expression levels of genes between samples under thebalassumption that the majority of them are not DE. Onepose an empirical strategy thsimple yet robust way to estimateexpressionthelevelsratioofofgenesRNA productionuses a weightedassumption trimmed that mean theof majority the log ofPower (to detect betwexpression ratios (trimmedchanges)simple meanofMvalues(TMM)).is higher atyet robust way to estimatFor sequencing data, we definehigherduction thecountsuses gene-wise a weighted log-foldchangesas:trimImplications forexpression ratios (trimmed meaFor downstream sequencing data, we defineYgk N k changes as:M g / analysis.log 2Ygk / N k Ygk N kM g /log 2and absolute expression levels:Y gk / N k and absolute expression levels:Ag 1 log Ygk Nk YgkNk Yg22’ for 0Ag 1 log Ygk Nk YgkN22To robustly summarize the observed M values, wetrim both the M values and the A values before taking
Statistical models• For count data, variance increaseswith mean• Starting point: Poisson model• Poisson has simplest meanvariancerelationship
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: Technical replicationDNApopulationL
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 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>
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Analysis of genome-scale count data in Bioconductor

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