## ----setup, eval = TRUE------------------------------------------------------- suppressPackageStartupMessages(library(Biobase)) suppressPackageStartupMessages(library(limma)) suppressPackageStartupMessages(library(gCrisprTools)) data("es", package = "gCrisprTools") data("ann", package = "gCrisprTools") data("aln", package = "gCrisprTools") knitr::opts_chunk$set(message = FALSE, fig.width = 8, fig.height = 8, warning = FALSE) ## ----eval = TRUE-------------------------------------------------------------- sk <- relevel(as.factor(pData(es)$TREATMENT_NAME), "ControlReference") names(sk) <- row.names(pData(es)) ## ----eval = TRUE-------------------------------------------------------------- design <- model.matrix(~ 0 + REPLICATE_POOL + TREATMENT_NAME, pData(es)) colnames(design) <- gsub('TREATMENT_NAME', '', colnames(design)) contrasts <-makeContrasts(DeathExpansion - ControlExpansion, levels = design) ## ----eval = TRUE, fig.width = 8, fig.height = 10------------------------------ es <- ct.filterReads(es, trim = 1000, sampleKey = sk) ## ----eval = TRUE, fig.width = 6, fig.height = 12------------------------------ es <- ct.normalizeGuides(es, method = "scale", plot.it = TRUE) #See man page for other options vm <- voom(exprs(es), design) fit <- lmFit(vm, design) fit <- contrasts.fit(fit, contrasts) fit <- eBayes(fit) ## ----eval = TRUE-------------------------------------------------------------- ann <- ct.prepareAnnotation(ann, fit, controls = "NoTarget") ## ----eval = TRUE-------------------------------------------------------------- resultsDF <- ct.generateResults( fit, annotation = ann, RRAalphaCutoff = 0.1, permutations = 1000, scoring = "combined", permutation.seed = 2 ) ## ----eval = TRUE-------------------------------------------------------------- # Create random alternative target associations altann <- sapply(ann$ID, function(x){ out <- as.character(ann$geneSymbol)[ann$ID %in% x] if(runif(1) < 0.01){out <- c(out, sample(as.character(ann$geneSymbol), size = 1))} return(out) }, simplify = FALSE) resultsDF <- ct.generateResults( fit, annotation = ann, RRAalphaCutoff = 0.1, permutations = 1000, scoring = "combined", alt.annotation = altann, permutation.seed = 2 ) ## ----eval = TRUE-------------------------------------------------------------- data("fit", package = "gCrisprTools") data("resultsDF", package = "gCrisprTools") fit <- fit[(row.names(fit) %in% row.names(ann)),] resultsDF <- resultsDF[(row.names(resultsDF) %in% row.names(ann)),] targetResultDF <- ct.simpleResult(resultsDF) #For a simpler target-level result object ## ----eval = TRUE-------------------------------------------------------------- ct.alignmentChart(aln, sk) ct.rawCountDensities(es, sk) ## ----eval = TRUE-------------------------------------------------------------- ct.gRNARankByReplicate(es, sk) ct.gRNARankByReplicate(es, sk, annotation = ann, geneSymb = "NoTarget") #Show locations of NTC gRNAs ## ----eval = TRUE-------------------------------------------------------------- ct.viewControls(es, ann, sk, normalize = FALSE) ct.viewControls(es, ann, sk, normalize = TRUE) ## ----eval = TRUE, fig.width = 8, fig.height = 12------------------------------ ct.GCbias(es, ann, sk) ct.GCbias(fit, ann, sk) ## ----eval = TRUE-------------------------------------------------------------- ct.stackGuides(es, sk, plotType = "gRNA", annotation = ann, nguides = 40) ## ----eval = TRUE-------------------------------------------------------------- ct.stackGuides(es, sk, plotType = "Target", annotation = ann) ## ----eval = TRUE-------------------------------------------------------------- ct.stackGuides(es, sk, plotType = "Target", annotation = ann, subset = names(sk)[grep('Expansion', sk)]) ## ----eval = TRUE-------------------------------------------------------------- ct.guideCDF(es, sk, plotType = "gRNA") ct.guideCDF(es, sk, plotType = "Target", annotation = ann) ## ----eval = TRUE-------------------------------------------------------------- ct.contrastBarchart(resultsDF) ## ----eval = TRUE-------------------------------------------------------------- ct.topTargets(fit, resultsDF, ann, targets = 10, enrich = TRUE) ct.topTargets(fit, resultsDF, ann, targets = 10, enrich = FALSE) ## ----eval = TRUE, fig.width = 8, fig.height = 10------------------------------ ct.viewGuides("Target1633", fit, ann) ct.gRNARankByReplicate(es, sk, annotation = ann, geneSymb = "Target1633") ## ----eval = TRUE-------------------------------------------------------------- ct.signalSummary(resultsDF, targets = list('TargetSetA' = c(sample(unique(resultsDF$geneSymbol), 3)), 'TargetSetB' = c(sample(unique(resultsDF$geneSymbol), 2)))) ## ----eval = TRUE-------------------------------------------------------------- data("essential.genes", package = "gCrisprTools") ct.targetSetEnrichment(resultsDF, essential.genes) ## ----rocprc, eval = TRUE------------------------------------------------------ ROC <- ct.ROC(resultsDF, essential.genes, direction = "deplete") PRC <- ct.PRC(resultsDF, essential.genes, direction = "deplete") show(ROC) # show(PRC) is equivalent for the PRC analysis ## ----eval = TRUE, warning=FALSE, message = FALSE------------------------------ #Create a geneset database using one of the many helper functions genesetdb <- sparrow::getMSigGeneSetDb(collection = 'h', species = 'human', id.type = 'entrez') ct.seas(resultsDF, gdb = genesetdb) # If you have a library that targets elements unevenly (e.g., variable numbers of # elements/promoters per genes), you can conform it via `sparrow::convertIdentifiers()` genesetdb.GREAT <- sparrow::convertIdentifiers(genesetdb, from = 'geneID', to = 'geneSymbol', xref = ann) ct.seas(resultsDF, gdb = genesetdb.GREAT) ## ----eval = FALSE------------------------------------------------------------- # path2report <- #Make a report of the whole experiment # ct.makeReport(fit = fit, # eset = es, # sampleKey = sk, # annotation = ann, # results = resultsDF, # aln = aln, # outdir = ".") # # path2QC <- #Or one focusing only on experiment QC # ct.makeQCReport(es, # trim = 1000, # log2.ratio = 0.05, # sampleKey = sk, # annotation = ann, # aln = aln, # identifier = 'Crispr_QC_Report', # lib.size = NULL # ) # # path2Contrast <- #Or Contrast-specific one # ct.makeContrastReport(eset = es, # fit = fit, # sampleKey = sk, # results = resultsDF, # annotation = ann, # comparison.id = NULL, # identifier = 'Crispr_Contrast_Report') ## ----------------------------------------------------------------------------- sessionInfo()