Analysis of genome-scale count data in Bioconductor

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The adjustment to data analysis isstraightforward• Assumption: core set of genes that do notchange in expression.• Pick a reference sample, compute trimmedmean of M-values (TMM) to reference• LTM( [Y gk /M k ] / [Y gk’ /M k’ ] ) estimates S k’ /S k• Adjustment to statistical analysis:– Use “effective” library size (edgeR)– Use additional offset (GLM)
Outline1. Applications2. Summarization3. Statistical models for count data4. “Normalization”5. Sharing information over entire dataset6. Statistical testing7. Other considerations – error model and morecomplex designs(Current) Bioconductor tools:baySeq, DEGseq, DESeq, edgeRPreliminaries(~40min)Practical(~20min)Moreadvancedtopics(~30min)Practical(~30min)
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: “Composition” ofsampled DNA can
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>

Analysis of genome-scale count data in Bioconductor

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