Analysis of genome-scale count data in Bioconductor

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DGEList object> d!An object of class "DGEList"!$samples!files groupdescription lib.size!GSM272105 GSM272105.txt DCLK transgenic (Dclk1) mouse hippocampus 2582749!GSM272106 GSM272106.txt WT wild-type mouse hippocampus 3342705!GSM272318 GSM272318.txt DCLK transgenic (Dclk1) mouse hippocampus 3207895!GSM272319 GSM272319.txt WT wild-type mouse hippocampus 3273243!GSM272320 GSM272320.txt DCLK transgenic (Dclk1) mouse hippocampus 2428553!GSM272321 GSM272321.txt WT wild-type mouse hippocampus 358649!GSM272322 GSM272322.txt DCLK transgenic (Dclk1) mouse hippocampus 714498!GSM272323 GSM272323.txt WT wild-type mouse hippocampus 2833329!$counts!GSM272105 GSM272106 GSM272318 GSM272319 GSM272320 GSM272321!TTTTTCTTCTTTCTTTT 3 1 2 6 3 0!CAGGGACCATCTGTAGA 5 19 2 16 2 0!GTGCGTGCAGCTGAGGG 7 4 6 5 7 1!ATACACACTGTAAAGAG 2 0 6 4 6 0!AATTATAGTGCAATTGA 5 3 3 3 2 0!GSM272322 GSM272323!TTTTTCTTCTTTCTTTT 1 2!CAGGGACCATCTGTAGA 2 13!GTGCGTGCAGCTGAGGG 2 3!ATACACACTGTAAAGAG 2 8!AATTATAGTGCAATTGA 0 4!76546 more rows ...!
Multidimensionalscaling plot• Used to assesssimilarity btwlibraries - identifyoutliers andproblematic samples• Commondispersion used asthe ‘distance metric’• Libraries quitesimilar here, apartfrom GSM272322> plotMDS.dge(d)
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 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: Reading in data• Read the data in
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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