### Example Workflow

This is an example workflow for processing a pooled CRISPR-based screen using the provided sample data. See the various manpages for additional visualization options and algorithmic details.

Load dependencies and data

    library(Biobase)
    library(limma)
    library(gCrisprTools)

    data("es", package = "gCrisprTools")
    data("ann", package = "gCrisprTools")
    data("aln", package = "gCrisprTools")

Make a sample key, structured as a factor with control samples in the first level

    sk <- relevel(as.factor(pData(es)$TREATMENT_NAME), "ControlReference")
    names(sk) <- row.names(pData(es))

Generate a contrast of interest using voom/limma; pairing replicates is a good idea if that information is available.

    design <- model.matrix(~ 0 + REPLICATE_POOL + TREATMENT_NAME, pData(es))
    colnames(design) <- gsub('TREATMENT_NAME', '', colnames(design))
    contrasts <-makeContrasts(DeathExpansion - ControlExpansion, levels = design)

Optionally, trim of trace reads from the unnormalized object (see man page for details)

    es <- ct.filterReads(es, trim = 1000, sampleKey = sk)

Normalize, convert to a voom object, and generate a contrast

    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)

Edit the annotation file if you used `ct.filterReads` above

    ann <- ct.prepareAnnotation(ann, fit, controls = "NoTarget")

Summarize gRNA signals to identify target genes of interest

    resultsDF <-
      ct.generateResults(
        fit,
        annotation = ann,
        RRAalphaCutoff = 0.1,
        permutations = 1000,
        scoring = "combined",
        permutation.seed = 2
      )

Optionally, just load an example results object for testing purposes (trimming out reads as necessary)

    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)),]

Crispr-specific quality control and visualization tools (see man pages for details):

    ct.alignmentChart(aln, sk)
    ct.rawCountDensities(es, sk)

Visualize gRNA abundance distributions

    ct.gRNARankByReplicate(es, sk)
    ct.gRNARankByReplicate(es, sk, annotation = ann, geneSymb = "NoTarget")  #Show locations of NTC gRNAs

Visualize control guide behavior across conditions

    ct.viewControls(es, ann, sk, normalize = FALSE)
    ct.viewControls(es, ann, sk, normalize = TRUE)

Visualize GC bias across samples, or within an experimental contrast

    ct.GCbias(es, ann, sk)
    ct.GCbias(fit, ann, sk)

View most variable gRNAs/Genes (as % of sequencing library)

    ct.stackGuides(es,
                   sk,
                   plotType = "gRNA",
                   annotation = ann,
                   nguides = 40)

    ct.stackGuides(es,
                   sk,
                   plotType = "Target",
                   annotation = ann)

    ct.stackGuides(es,
                   sk,
                   plotType = "Target",
                   annotation = ann,
                   subset = names(sk)[grep('Expansion', sk)])

View a CDF of genes/guides

    ct.guideCDF(es, sk, plotType = "gRNA")
    ct.guideCDF(es, sk, plotType = "Target", annotation = ann)

View top enriched/depleted candidates

    ct.topTargets(fit,
                  resultsDF,
                  ann,
                  targets = 10,
                  enrich = TRUE)
    ct.topTargets(fit,
                  resultsDF,
                  ann,
                  targets = 10,
                  enrich = FALSE)

View the gRNA behavior of gRNAs targeting a particular gene of interest

    ct.viewGuides("Target1633", fit, ann)
    ct.gRNARankByReplicate(es, sk, annotation = ann, geneSymb = "Target1633")

View ontological enrichment within the depleted/enriched targets

    enrichmentResults <-
      ct.PantherPathwayEnrichment(
        resultsDF,
        pvalue.cutoff = 0.01,
        enrich = TRUE,
        organism = 'mouse'
      )

Test a gene set for enrichment within target candidates

    data("essential.genes", package = "gCrisprTools")
    ROCs <- ct.ROC(resultsDF, essential.genes, stat = "deplete.p")
    PRCs <- ct.PRC(resultsDF, essential.genes, stat = "deplete.p")

Make reports in a directory of interest

    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')