## ---- eval = FALSE------------------------------------------------------- # library(Biobase) # library(limma) # library(gCrisprTools) # # data("es", package = "gCrisprTools") # data("ann", package = "gCrisprTools") # data("aln", package = "gCrisprTools") ## ---- eval = FALSE------------------------------------------------------- # sk <- relevel(as.factor(pData(es)$TREATMENT_NAME), "ControlReference") # names(sk) <- row.names(pData(es)) ## ---- eval = FALSE------------------------------------------------------- # design <- model.matrix(~ 0 + REPLICATE_POOL + TREATMENT_NAME, pData(es)) # colnames(design) <- gsub('TREATMENT_NAME', '', colnames(design)) # contrasts <-makeContrasts(DeathExpansion - ControlExpansion, levels = design) ## ---- eval = FALSE------------------------------------------------------- # es <- ct.filterReads(es, trim = 1000, sampleKey = sk) ## ---- eval = FALSE------------------------------------------------------- # 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 = FALSE------------------------------------------------------- # ann <- ct.prepareAnnotation(ann, fit, controls = "NoTarget") ## ---- eval = FALSE------------------------------------------------------- # resultsDF <- # ct.generateResults( # fit, # annotation = ann, # RRAalphaCutoff = 0.1, # permutations = 1000, # scoring = "combined", # permutation.seed = 2 # ) ## ---- eval = FALSE------------------------------------------------------- # 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)),] ## ---- eval = FALSE------------------------------------------------------- # ct.alignmentChart(aln, sk) # ct.rawCountDensities(es, sk) ## ---- eval = FALSE------------------------------------------------------- # ct.gRNARankByReplicate(es, sk) # ct.gRNARankByReplicate(es, sk, annotation = ann, geneSymb = "NoTarget") #Show locations of NTC gRNAs ## ---- eval = FALSE------------------------------------------------------- # ct.viewControls(es, ann, sk, normalize = FALSE) # ct.viewControls(es, ann, sk, normalize = TRUE) ## ---- eval = FALSE------------------------------------------------------- # ct.GCbias(es, ann, sk) # ct.GCbias(fit, ann, sk) ## ---- eval = FALSE------------------------------------------------------- # ct.stackGuides(es, # sk, # plotType = "gRNA", # annotation = ann, # nguides = 40) ## ---- eval = FALSE------------------------------------------------------- # ct.stackGuides(es, # sk, # plotType = "Target", # annotation = ann) ## ---- eval = FALSE------------------------------------------------------- # ct.stackGuides(es, # sk, # plotType = "Target", # annotation = ann, # subset = names(sk)[grep('Expansion', sk)]) ## ---- eval = FALSE------------------------------------------------------- # ct.guideCDF(es, sk, plotType = "gRNA") # ct.guideCDF(es, sk, plotType = "Target", annotation = ann) ## ---- eval = FALSE------------------------------------------------------- # ct.topTargets(fit, # resultsDF, # ann, # targets = 10, # enrich = TRUE) # ct.topTargets(fit, # resultsDF, # ann, # targets = 10, # enrich = FALSE) ## ---- eval = FALSE------------------------------------------------------- # ct.viewGuides("Target1633", fit, ann) # ct.gRNARankByReplicate(es, sk, annotation = ann, geneSymb = "Target1633") ## ---- eval = FALSE------------------------------------------------------- # enrichmentResults <- # ct.PantherPathwayEnrichment( # resultsDF, # pvalue.cutoff = 0.01, # enrich = TRUE, # organism = 'mouse' # ) ## ---- eval = FALSE------------------------------------------------------- # data("essential.genes", package = "gCrisprTools") # ROCs <- ct.ROC(resultsDF, essential.genes, stat = "deplete.p") # PRCs <- ct.PRC(resultsDF, essential.genes, stat = "deplete.p") ## ---- 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')