r-CB2

CRISPR Pooled Screen Analysis using Beta-Binomial Test.

CB²

CB²(CRISPRBetaBinomial) is a new algorithm for analyzing CRISPR data based on beta-binomial distribution. We provide CB² as a R package, and the interal algorithms of CB² are also implemented in CRISPRCloud.

Update

May 26, 2020

• Regarding #9, CB² now provides logFC of gene-level analysis with two different modes. The default option is the same as the previous version, and setting logFC parameter value of measure_gene_stats to gene will provide the logFC calculate by gene-level CPMs.

April 14, 2020

• Regarding #6, now users can use join_count_and_design function.

December 16, 2019

• Regarding #4, CB² now supports gzipped FASTQ file.
• Regarding #5, calc_mappability() provide total_reads and mapped_reads columns.

July 2, 2019

There are several updates.

• We have change the function name for the sgRNA-level test to measure_sgrna_stats. The original name run_estimation has been deprecated.
• CB² now supports a data.frame with character columns. In other words, you can use

How to install

Currently CB² is now on CRAN, and you can install it using install.package function.

install.package("CB2")

Installation Github version of CB² can be done using the following lines of code in your R terminal.

install.packages("devtools")
devtools::install_github("LiuzLab/CB2")

Alternatively, here is a one-liner command line for the installation.

Rscript -e "install.packages('devtools'); devtools::install_github('LiuzLab/CB2')"

A simple example how to use CB² in R

FASTA <- system.file("extdata", "toydata",
                     "small_sample.fasta",
                     package = "CB2")
df_design <- data.frame()
for(g in c("Low", "High", "Base")) {
  for(i in 1:2) {
    FASTQ <- system.file("extdata", "toydata",
                         sprintf("%s%d.fastq", g, i), 
                         package = "CB2")
    df_design <- rbind(df_design, 
      data.frame(
        group = g, 
        sample_name = sprintf("%s%d", g, i),
        fastq_path = FASTQ, 
        stringsAsFactors = F)
      )
  }
}

MAP_FILE <- system.file("extdata", "toydata", "sg2gene.csv", package="CB2")
sgrna_count <- run_sgrna_quant(FASTA, df_design, MAP_FILE)
  
sgrna_stat <- measure_sgrna_stats(sgrna_count$count, df_design, 
                                  "Base", "Low", 
                                  ge_id = "gene",
                                  sg_id = "id")
gene_stat <- measure_gene_stats(sgrna_stat)

Or you could run the example with the following commented code.

sgrna_count <- run_sgrna_quant(FASTA, df_design)
sgrna_stat <- measure_sgrna_stats(sgrna_count$count, df_design, "Base", "Low")
gene_stat <- measure_gene_stats(sgrna_stat)

More detailed tutorial is available here!