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

Jun 7, 2022

A bug fix regarding #14. Thanks @DaneseAnna for reporting the issue.

Dec 4, 2020

If you are experiencing an issue during the installation, it would be possible due to multtest package hasn't been installed. If so, please use the following snippet to install the package.

install.package("BiocManager") # can be omitted if you have installed the package
install.packages("multtest")

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("hyunhwan-jeong/CB2")

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

Rscript -e "install.packages('devtools'); devtools::install_github('hyunhwan-jeong/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!