## ----setup-------------------------------------------------------------------- library(Biobase) library(gCrisprTools) knitr::opts_chunk$set(message = FALSE, fig.width = 8, fig.height = 8) data(resultsDF) head(resultsDF) ## ----------------------------------------------------------------------------- res <- ct.simpleResult(resultsDF, collapse = 'geneSymbol') head(res) ## ----------------------------------------------------------------------------- # Make "another" result res2 <- res res2$best.p <- res2$best.p * runif(nrow(res2)) res2$direction[sample(1:nrow(res), 500)] <- res2$direction[sample(1:nrow(res), 500)] regularized <- ct.regularizeContrasts(dflist = list('Experiment1' = res[1:1500,], 'Experiment2' = res2[1:1900,]), collapse = 'geneSymbol') str(regularized) ## ----------------------------------------------------------------------------- comparison <- ct.compareContrasts(dflist = regularized, statistics = c('best.q', 'best.p'), cutoffs = c(0.5,0.05), same.dir = rep(TRUE, length(regularized))) head(comparison, 30) ## ----------------------------------------------------------------------------- ct.compareContrasts(dflist = regularized, statistics = c('best.q', 'best.p'), cutoffs = c(0.5,0.05), same.dir = rep(TRUE, length(regularized)), return.stats = TRUE) ## ----contrastbarchart, fig.width=6, fig.height = 5---------------------------- ct.contrastBarchart(regularized, background = FALSE, statistic = 'best.p') ## ----fig.width = 5, fig.height = 5-------------------------------------------- scat <- ct.scatter(regularized, targets = 'geneSymbol', statistic = 'best.p', cutoff = 0.05) ## ----------------------------------------------------------------------------- head(scat) ## ----contrast interaction, message = FALSE, warning=FALSE, fig.width = 6, fig.height = 12---- library(Biobase) library(limma) library(gCrisprTools) #Create a complex model design; removing the replicate term for clarity data("es", package = "gCrisprTools") data("ann", package = "gCrisprTools") design <- model.matrix(~ 0 + TREATMENT_NAME, pData(es)) colnames(design) <- gsub('TREATMENT_NAME', '', colnames(design)) contrasts <-makeContrasts(ControlTime = ControlExpansion - ControlReference, DeathOverTime = DeathExpansion - ControlReference, Interaction = DeathExpansion - ControlExpansion, levels = design) es <- ct.normalizeGuides(es, method = "scale") #See man page for other options vm <- voom(exprs(es), design) fit <- lmFit(vm, design) fit <- contrasts.fit(fit, contrasts) fit <- eBayes(fit) allResults <- sapply(colnames(fit$coefficients), function(x){ ct.generateResults(fit, annotation = ann, RRAalphaCutoff = 0.1, permutations = 1000, scoring = "combined", permutation.seed = 2, contrast.term = x) }, simplify = FALSE) allSimple <- ct.regularizeContrasts(allResults) ## ----------------------------------------------------------------------------- time.effect <- ct.compareContrasts(list("con" = allSimple$ControlTime, "tx" = allSimple$DeathOverTime)) summary(time.effect$replicated) ## ----------------------------------------------------------------------------- mod.control <- ct.compareContrasts(list("con" = allSimple$ControlTime, "Interaction" = allSimple$Interaction), same.dir = c(TRUE, FALSE)) summary(mod.control$replicated) ## ----fig.height= 6, fig.width = 8--------------------------------------------- upset <- ct.upSet(allSimple) ## ----------------------------------------------------------------------------- show(upset) ## ----fig.width=6, fig.height=6, warnings= FALSE------------------------------- genesetdb <- sparrow::getMSigGeneSetDb(collection = 'h', species = 'human', id.type = 'entrez') sparrowList <- ct.seas(allSimple, gdb = genesetdb) show(sparrowList) #Can use returned matrices to facilitate downstream comparisons: plot(-log10(sparrow::results(sparrowList$DeathOverTime, 'fgsea')$padj), -log10(sparrow::results(sparrowList$Interaction, 'fgsea')$padj), pch = 19, col = rgb(0,0,0.8,0.5), ylab = "Pathway -log10(P), Treatment Over Time", xlab = "Pathway -log10(P), Marginal Time Effect, Treatment Arm", main = 'Evidence for Pathway Enrichment') abline(0,1,lty = 2) ## ----------------------------------------------------------------------------- sessionInfo()