# CB2 v1.1

Monthly downloads

## CRISPR Pooled Screen Analysis using Beta-Binomial Test

Provides functions for hit gene identification and quantification of sgRNA (single-guided RNA) abundances for CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) pooled screen data analysis.
Details are in Jeong et al. (2018) <doi:10.1101/309302> and Baggerly et al. (2003) <doi:10.1093/bioinformatics/btg173>.

## Readme

## What is CB^{2}

CB^{2}(CRISPRBetaBinomial) is a new algorithm for analyzing CRISPR data based on beta-binomial distribution.
We provide CB^{2} as a R package, and the interal algorithms of CB^{2} are also implemented in CRISPRCloud.

## How to install

Currently CB^{2} is now on `CRAN`

, and you can install it using `install.package`

function.

```
instll.package("CB2")
```

Installation Github version of CB^{2} can be done using the following lines of code in your R terminal.

```
install.packages("devtools")
devtools::install_github("hyunhwaj/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 CB^{2} 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)
)
}
}
sgrna_count <- run_sgrna_quant(FASTA, df_design)
sgrna_stat <- run_estimation(sgrna_count$count, df_design, "Base", "Low")
gene_stat <- measure_gene_stats(sgrna_stat)
```

## Functions in CB2

Name | Description | |

measure_gene_stats | A function to perform gene-level test using a sgRNA-level statistics. | |

plot_PCA | A function to plot the first two principal components of samples. | |

run_estimation | A function to perform a statistical test at a sgRNA-level | |

plot_corr_heatmap | A function to show a heatmap sgRNA-level corrleations of the NGS samples. | |

plot_count_distribution | A function to plot read count distribution. | |

Sanson_CRISPRn_A375 | A benchmark CRISPRn pooled screen data from Sanson et al. | |

Evers_CRISPRn_RT112 | A benchmark CRISPRn pooled screen data from Evers et al. | |

plot_dotplot | A function to visualize dot plots for a gene. | |

quant | A C++ function to quantify sgRNA abundance from NGS samples. | |

run_sgrna_quant | A function to run a sgRNA quantification algorithm from NGS sample | |

calc_mappability | A function to calculate the mappabilities of each NGS sample. | |

fit_ab | A C++ function to perform a parameter estimation for the sgRNA-level test. It will estimate two different parameters `phat` and `vhat,` and we assume input count data follows the beta-binomial distribution. Dr. Keith Baggerly initially implemented this code in Matlab, and it has been rewritten it in C++ for the speed-up. | |

join_count_and_design | A function to join a count table and a design table. | |

get_CPM | A function to normalize sgRNA read counts. | |

No Results! |

## Vignettes of CB2

Name | ||

cb2-tutorial.Rmd | ||

No Results! |

## Last month downloads

## Details

Type | Package |

Date | 2019-02-18 |

License | MIT + file LICENSE |

LinkingTo | Rcpp, RcppArmadillo |

RoxygenNote | 6.1.1 |

Encoding | UTF-8 |

VignetteBuilder | knitr |

NeedsCompilation | yes |

Packaged | 2019-02-19 06:07:31 UTC; hyunhwan |

Repository | CRAN |

Date/Publication | 2019-02-19 08:20:08 UTC |

imports | dplyr , ggplot2 , glue , magrittr , metap , pheatmap , Rcpp (>= 0.12.16) , stringr , tibble , tidyr |

suggests | knitr , rmarkdown , testthat |

depends | R (>= 3.5.0) |

linkingto | RcppArmadillo |

Contributors |

#### Include our badge in your README

```
[![Rdoc](http://www.rdocumentation.org/badges/version/CB2)](http://www.rdocumentation.org/packages/CB2)
```