bchen4 / sgrsea

sgRSEA: Enrichment Analysis of Genome-wide CRISPR/Cas9 Screen Data

sgRSEA

Enrichment Analysis of CRISPR/Cas9 Knockout Screen Data

Version : v1.0

Authors

Beibei Chen ([e-mail])

Jungsik Noh ([e-mail])

Maintainer: Beibei Chen

For detailed manual

Prerequisites

• Python 2.7
• Pandas
• Numpy
• statsmodels
• scipy
• biopython

Installation

Option 1: from source code

1 Download sgRSEA

git clone
cd sgRSEA

2 Install required packages

pip install -r requirements.txt

3 Install sgRSEA

python setup.py install

Quick start

sgRSEA can be run on specific steps as well as fastq-to-result using a single command.

#Run the suite from fastq to final results
sgrsea run -d design_file -o my_experiment

#Get the count for single fastq file
sgrsea count -i my_fastq -o output -l sgRNA_lib 

#Get the count matrix of multiple fastq file and generate a matrix
sgrsea count -d design.txt -o output_prefix

#Normalize the count matrix
sgrsea normalization -i count_matrix --ormalize-method total -o outputfile

#Normalize the count matrix. There are sub-libs sequenced separately
sgrsea normalization -i count_matrix --ormalize-method total -o outputfile --split-lib

#Reformat the matrix into sgRSEA 4-column matrix. 
#-t and -c values MUST match group content of the design file
sgrsea reformat -i normalized_matrix -d design.txt -t Heat,Cold,Dry -c Ctrl,Ctrl,Ctrl -o output_prefix --collapse-replicates auto

#Stattest on normalized and formatted matrix
sgrsea stattest -i matrix -o output

License

See the LICENSE file for license rights and limitations MIT.

Also available in R

https://cran.r-project.org/web/packages/sgRSEA/index.html