## ----include=FALSE------------------------------------------------------------
library(BiocStyle)
knitr::opts_chunk$set(echo = TRUE, fig.align = "center", message = FALSE, warning = FALSE)

## ----eval = FALSE-------------------------------------------------------------
#  if (!requireNamespace("BiocManager", quietly = TRUE))
#      install.packages("BiocManager")
#  BiocManager::install("CRISPRball")

## ----eval = FALSE-------------------------------------------------------------
#  library("CRISPRball")
#  CRISPRball()

## ----fig.cap="Screenshot of the `CRISPRball` application, when launched as a server where users can directly upload MAGeCK RRA or MLE output. Information on the format of the expected data are provided in the following sections.", echo=FALSE----
knitr::include_graphics("upload_cb.png")

## -----------------------------------------------------------------------------
# Create lists of results summaries for each dataset.
d1.genes <- read.delim(system.file("extdata", "esc1.gene_summary.txt",
    package = "CRISPRball"
), check.names = FALSE)
d2.genes <- read.delim(system.file("extdata", "plasmid.gene_summary.txt",
    package = "CRISPRball"
), check.names = FALSE)

d1.sgrnas <- read.delim(system.file("extdata", "esc1.sgrna_summary.txt",
    package = "CRISPRball"
), check.names = FALSE)
d2.sgrnas <- read.delim(system.file("extdata", "plasmid.sgrna_summary.txt",
    package = "CRISPRball"
), check.names = FALSE)

count.summ <- read.delim(system.file("extdata", "escneg.countsummary.txt",
    package = "CRISPRball"
), check.names = FALSE)
norm.counts <- read.delim(system.file("extdata", "escneg.count_normalized.txt",
    package = "CRISPRball"
), check.names = FALSE)

# Look at the first few rows of the gene summary for the ESC vs plasmid comparison.
head(d1.genes)

## ----eval = FALSE-------------------------------------------------------------
#  genes <- list(ESC = d1.genes, plasmid = d2.genes)
#  sgrnas <- list(ESC = d1.sgrnas, plasmid = d2.sgrnas)
#  
#  CRISPRball(
#      gene.data = genes, sgrna.data = sgrnas,
#      count.summary = count.summ, norm.counts = norm.counts
#  )

## ----eval = FALSE-------------------------------------------------------------
#  # Create lists of results summaries for each dataset.
#  genes <- read_mle_gene_summary(system.file("extdata", "beta_leukemia.gene_summary.txt",
#      package = "CRISPRball"
#  ))
#  
#  count.summ <- read.delim(system.file("extdata", "escneg.countsummary.txt",
#      package = "CRISPRball"
#  ), check.names = FALSE)
#  norm.counts <- read.delim(system.file("extdata", "escneg.count_normalized.txt",
#      package = "CRISPRball"
#  ), check.names = FALSE)
#  
#  CRISPRball(
#      gene.data = genes,
#      count.summary = count.summ, norm.counts = norm.counts
#  )

## ----fig.cap="Screenshot of the `CRISPRball` application, when launched with MAGeCk RRA output provided.", echo=FALSE----
knitr::include_graphics("rra_qc_cb.png")

## ----fig.cap="Screenshot of the `CRISPRball` Gene (Overview) tab.", echo=FALSE----
knitr::include_graphics("gene_cb.png")

## ----eval = FALSE-------------------------------------------------------------
#  library("msigdbr")
#  
#  # Retrieve MSigDB Hallmark gene sets and convert to a named list.
#  gene.sets <- msigdbr(species = "Homo sapiens", category = "H")
#  gene.sets <- gene.sets %>% split(x = .$gene_symbol, f = .$gs_name)
#  
#  # Can also add genesets manually.
#  gene.sets[["my_fav_genes"]] <- c("TOP2A", "FECH", "SOX2", "DUT", "RELA")
#  
#  CRISPRball(
#      gene.data = genes, sgrna.data = sgrnas, count.summary = count.summ,
#      norm.counts = norm.counts, genesets = gene.sets
#  )

## ----eval = FALSE-------------------------------------------------------------
#  library("depmap")
#  library("pool")
#  library("RSQLite")
#  
#  # This will take a few minutes to run.
#  # The database will be named "depmap_db.sqlite" and placed in the working directory.
#  build_depmap_db()
#  
#  CRISPRball(
#      gene.data = genes, sgrna.data = sgrnas, count.summary = count.summ,
#      norm.counts = norm.counts, genesets = gene.sets, depmap.db = "depmap_db.sqlite"
#  )

## ----fig.cap="Screenshot of the `CRISPRball` application, with focus on the DepMap tab for CDK2.", echo=FALSE----
knitr::include_graphics("depmap_cb.png")

## ----echo = FALSE-------------------------------------------------------------
sessionInfo()