Nothing
## ---- eval = FALSE------------------------------------------------------------
# if (!requireNamespace("BiocManager", quietly=TRUE))
# install.packages("BiocManager")
# BiocManager::install("gCrisprTools")
## ---- message=FALSE, warning=FALSE--------------------------------------------
library(Biobase)
library(limma)
library(gCrisprTools)
## -----------------------------------------------------------------------------
data("es", package = "gCrisprTools")
es
head(exprs(es))
## -----------------------------------------------------------------------------
data("ann", package = "gCrisprTools")
head(ann)
## -----------------------------------------------------------------------------
sk <- relevel(as.factor(pData(es)$TREATMENT_NAME), "ControlReference")
names(sk) <- row.names(pData(es))
sk
## ---- fig.width = 7, fig.height = 5-------------------------------------------
data("aln", package = "gCrisprTools")
head(aln)
ct.alignmentChart(aln, sk)
## ---- fig.width=6, fig.height = 8---------------------------------------------
es.floor <- ct.filterReads(es, read.floor = 30, sampleKey = sk)
es <- ct.filterReads(es, trim = 1000, log2.ratio = 4, sampleKey = sk)
##Convenience function for conforming the annotation object to exclude the trimmed gRNAs
ann <- ct.prepareAnnotation(ann, es, controls = "NoTarget")
## ---- fig.width=6, fig.height = 8---------------------------------------------
es <- ct.normalizeGuides(es, 'scale', annotation = ann, sampleKey = sk, plot.it = TRUE)
es.norm <- ct.normalizeGuides(es, 'slope', annotation = ann, sampleKey = sk, plot.it = TRUE)
es.norm <- ct.normalizeGuides(es, 'controlScale', annotation = ann, sampleKey = sk, plot.it = TRUE, geneSymb = 'NoTarget')
es.norm <- ct.normalizeGuides(es, 'controlSpline', annotation = ann, sampleKey = sk, plot.it = TRUE, geneSymb = 'NoTarget')
## ---- eval= FALSE-------------------------------------------------------------
# #Not run:
# path2QC <- ct.makeQCReport(es,
# trim = 1000,
# log2.ratio = 0.05,
# sampleKey = sk,
# annotation = ann,
# aln = aln,
# identifier = 'Crispr_QC_Report',
# lib.size = NULL
# )
## ---- fig.width=6, fig.height=6-----------------------------------------------
ct.rawCountDensities(es, sk)
## ---- fig.width=6, fig.height = 6---------------------------------------------
ct.gRNARankByReplicate(es, sk) #Visualization of gRNA abundance distribution
## ---- fig.width=6, fig.height = 6---------------------------------------------
ct.gRNARankByReplicate(es, sk, annotation = ann, geneSymb = "Target1633")
## ---- fig.width=6, fig.height = 6---------------------------------------------
ct.viewControls(es, ann, sk, normalize = FALSE, geneSymb = 'NoTarget')
## ---- fig.width=6, fig.height = 4---------------------------------------------
ct.guideCDF(es, sk, plotType = "gRNA")
## -----------------------------------------------------------------------------
design <- model.matrix(~ 0 + REPLICATE_POOL + TREATMENT_NAME, pData(es))
colnames(design) <- gsub('TREATMENT_NAME', '', colnames(design))
contrasts <-makeContrasts(DeathExpansion - ControlExpansion, levels = design)
vm <- voom(exprs(es), design)
fit <- lmFit(vm, design)
fit <- contrasts.fit(fit, contrasts)
fit <- eBayes(fit)
## ---- message=FALSE, warning=FALSE--------------------------------------------
resultsDF <-
ct.generateResults(
fit,
annotation = ann,
RRAalphaCutoff = 0.1,
permutations = 1000,
scoring = "combined"
)
## ---- fig.width=6, fig.height = 6---------------------------------------------
ct.topTargets(fit,
resultsDF,
ann,
targets = 10,
enrich = TRUE)
## ---- fig.width=6, fig.height = 8---------------------------------------------
ct.stackGuides(
es,
sk,
plotType = "Target",
annotation = ann,
subset = names(sk)[grep('Expansion', sk)]
)
## ---- fig.width=6, fig.height = 4---------------------------------------------
ct.viewGuides("Target1633", fit, ann)
## ---- eval= FALSE-------------------------------------------------------------
# #Not run:
# path2Contrast <-
# ct.makeContrastReport(eset = es,
# fit = fit,
# sampleKey = sk,
# results = resultsDF,
# annotation = ann,
# comparison.id = NULL,
# identifier = 'Crispr_Contrast_Report')
## ---- eval=FALSE--------------------------------------------------------------
# #Not run:
# path2report <-
# ct.makeReport(fit = fit,
# eset = es,
# sampleKey = sk,
# annotation = ann,
# results = resultsDF,
# aln = aln,
# outdir = ".")
## ---- eval= FALSE-------------------------------------------------------------
# #Not run:
# enrichmentResults <-
# ct.PantherPathwayEnrichment(
# resultsDF,
# pvalue.cutoff = 0.01,
# enrich = TRUE,
# organism = 'mouse'
# )
#
# > head(enrichmentResults) #Note: Pathway names have been edited for display purposes.
# PATHWAY nGenes sigGenes expected odds p FDR
# 1 EGF receptor signaling pathway 200 14 5.240550 3.332647 0.0004498023 0.03958260
# 2 FGF signaling pathway 230 14 5.949958 2.869779 0.0016284304 0.07165094
# 3 Insulin/MAP kinase cascade 138 9 3.714916 2.785331 0.0101632822 0.20272465
# 4 CCKR signaling map ST 331 15 8.211061 2.148459 0.0126368744 0.20272465
# 5 p38 MAPK pathway 145 9 3.891333 2.641968 0.0135928688 0.20272465
# 6 B cell activation 204 11 5.336192 2.368705 0.0146442541 0.20272465
## ---- fig.width=6, fig.height = 6, warning=FALSE------------------------------
data("essential.genes", package = "gCrisprTools") #Artificial list created for demonstration
data("resultsDF", package = "gCrisprTools")
ROC <- ct.ROC(resultsDF, essential.genes, stat = "deplete.p")
str(ROC)
## ---- fig.width=6, fig.height = 6, warning=FALSE------------------------------
PRC <- ct.PRC(resultsDF, essential.genes, stat = "deplete.p")
str(PRC)
## ---- fig.width=6, fig.height = 6, warning=FALSE------------------------------
targetsTest <- ct.targetSetEnrichment(resultsDF, essential.genes, enrich = FALSE)
str(targetsTest)
## -----------------------------------------------------------------------------
sessionInfo()
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.