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### R code from vignette source 'rnaseqAnalysis.Rnw'
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### code chunk number 1: load data (eval = FALSE)
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## library(ReportingTools)
## data(mockRnaSeqData)
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### code chunk number 2: run_edgeR (eval = FALSE)
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## library(edgeR)
## conditions <- c(rep("case",3), rep("control", 3))
## d <- DGEList(counts = mockRnaSeqData, group = conditions)
## d <- calcNormFactors(d)
## d <- estimateCommonDisp(d)
## ## Get an edgeR object
## edgeR.de <- exactTest(d)
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### code chunk number 3: edgeR_report (eval = FALSE)
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## library(lattice)
## rep.theme <- reporting.theme()
## ## Change symbol colors in plots
## rep.theme$superpose.symbol$col <- c("blue", "red")
## rep.theme$superpose.symbol$fill <- c("blue", "red")
## lattice.options(default.theme = rep.theme)
## ## Publish a report of the top 10 genes with p-values < 0.05 and log-fold change > 2
## ## In this case, the plots contain the counts from mockRnaSeqData, which are not normalized.
## ## The publish function does not normalize counts for the countTable argument to allow for
## ## flexibility in plotting various units (e.g. RPKM instead of counts).
##
## deReport <- HTMLReport(shortName = 'RNAseq_analysis_with_edgeR',
## title = 'RNA-seq analysis of differential expression using edgeR',
## reportDirectory = "./reports")
## publish(edgeR.de, deReport, countTable=mockRnaSeqData,
## conditions=conditions, annotation.db = 'org.Mm.eg',
## pvalueCutoff = .05, lfc = 2, n = 10, name="edgeR")
## finish(deReport)
##
## ## If you would like to plot normalized counts, run the following commands instead:
## ## mockRnaSeqData.norm <- d$pseudo.counts
## ## publish(edgeR.de, deReport, mockRnaSeqData.norm,
## ## conditions, annotation.db = 'org.Mm.eg',
## ## pvalueCutoff = .05, lfc = 2, n = 10)
## ## finish(deReport)
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### code chunk number 4: edgeR_report (eval = FALSE)
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## d <- DGEList(counts = mockRnaSeqData, group = conditions)
## d <- calcNormFactors(d)
## design <- model.matrix(~conditions)
## d <- estimateGLMCommonDisp(d, design)
## d <- estimateGLMTrendedDisp(d, design)
## d <- estimateGLMTagwiseDisp(d, design)
## fit <- glmFit(d,design)
## edgeR.lrt <- glmLRT(fit, coef=2)
##
## deReport2 <- HTMLReport(shortName = 'RNAseq_analysis_with_edgeR_2',
## title = 'RNA-seq analysis of differential expression using edgeR (LRT)',
## reportDirectory = "./reports")
## publish(edgeR.lrt, deReport2, countTable=mockRnaSeqData,
## conditions=conditions, annotation.db = 'org.Mm.eg',
## pvalueCutoff = .05, lfc = 2, n = 10, name="edgeRlrt")
## finish(deReport2)
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### code chunk number 5: run_DESeq (eval = FALSE)
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## library(DESeq)
## cds<-newCountDataSet(mockRnaSeqData, conditions)
## cds<-estimateSizeFactors(cds)
## cds<-estimateDispersions(cds)
## res<-nbinomTest(cds,"control", "case" )
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### code chunk number 6: DESeq_report (eval = FALSE)
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## desReport <- HTMLReport(shortName = 'RNAseq_analysis_with_DESeq',
## title = 'RNA-seq analysis of differential expression using DESeq',
## reportDirectory = "./reports")
## publish(res,desReport,name="df",countTable=mockRnaSeqData, pvalueCutoff=0.05,
## conditions=conditions,annotation.db="org.Mm.eg.db",
## expName="deseq",reportDir="./reports", .modifyDF=makeDESeqDF)
## finish(desReport)
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### code chunk number 7: run_DESeq2 (eval = FALSE)
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## library(DESeq2)
## conditions <- c(rep("case",3), rep("control", 3))
## mockRna.dse <- DESeqDataSetFromMatrix(countData = mockRnaSeqData,
## colData = as.data.frame(conditions), design = ~ conditions)
## colData(mockRna.dse)$conditions <- factor(colData(mockRna.dse)$conditions, levels=c("control", "case"))
## ## Get a DESeqDataSet object
## mockRna.dse <- DESeq(mockRna.dse)
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### code chunk number 8: DESeq2_report (eval = FALSE)
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## des2Report <- HTMLReport(shortName = 'RNAseq_analysis_with_DESeq2',
## title = 'RNA-seq analysis of differential expression using DESeq2',
## reportDirectory = "./reports")
## publish(mockRna.dse,des2Report, pvalueCutoff=0.05,
## annotation.db="org.Mm.eg.db", factor = colData(mockRna.dse)$conditions,
## reportDir="./reports")
## finish(des2Report)
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### code chunk number 9: Do GO analysis (eval = FALSE)
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## library(GOstats)
## library(org.Mm.eg.db)
## tt <- topTags(edgeR.de, n = 1000, adjust.method = 'BH', sort.by = 'p.value')
## selectedIDs <- rownames(tt$table)
## universeIDs <- rownames(mockRnaSeqData)
## goParams <- new("GOHyperGParams",
## geneIds = selectedIDs,
## universeGeneIds = universeIDs,
## annotation ="org.Mm.eg" ,
## ontology = "MF",
## pvalueCutoff = 0.01,
## conditional = TRUE,
## testDirection = "over")
## goResults <- hyperGTest(goParams)
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### code chunk number 10: make the GO report (eval = FALSE)
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## goReport <- HTMLReport(shortName = 'go_analysis_rnaseq',
## title = "GO analysis of mockRnaSeqData",
## reportDirectory = "./reports")
## publish(goResults, goReport, selectedIDs=selectedIDs, annotation.db="org.Mm.eg",
## pvalueCutoff= 0.05)
## finish(goReport)
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### code chunk number 11: Do PFAM analysis (eval = FALSE)
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## library(Category)
## params <- new("PFAMHyperGParams",
## geneIds= selectedIDs,
## universeGeneIds=universeIDs,
## annotation="org.Mm.eg",
## pvalueCutoff= 0.01,
## testDirection="over")
## PFAMResults <- hyperGTest(params)
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### code chunk number 12: make the PFAM report (eval = FALSE)
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## PFAMReport <- HTMLReport(shortName = 'pfam_analysis_rnaseq',
## title = "PFAM analysis of mockRnaSeqData",
## reportDirectory = "./reports")
## publish(PFAMResults, PFAMReport, selectedIDs=selectedIDs, annotation.db="org.Mm.eg",categorySize=5)
## finish(PFAMReport)
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### code chunk number 13: make the index page (eval = FALSE)
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## indexPage <- HTMLReport(shortName = "indexRNASeq",
## title = "Analysis of mockRnaSeqData",
## reportDirectory = "./reports")
## publish(Link(list(deReport,des2Report, goReport, PFAMReport), report = indexPage),
## indexPage)
## finish(indexPage)
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