Data analysis scripts for Datlinger et. al, 2017 (doi:10.1038/nmeth.4177)
Python R Makefile
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README.md

DOI

Pooled CRISPR screening with single-cell transcriptome readout

Paul Datlinger, André F Rendeiro*, Christian Schmidl*, Thomas Krausgruber, Peter Traxler, Johanna Klughammer, Linda C Schuster, Amelie Kuchler, Donat Alpar, Christoph Bock. (2017) Nature Methods. doi:10.1038/nmeth.4177

*These authors contributed equally to this work

Paper: http://dx.doi.org/10.1038/nmeth.4177

Website: http://crop-seq.computational-epigenetics.org

This repository contains scripts used in the analysis of the data of the data presented in this paper. Future updates will be shared at https://github.com/epigen/crop-seq/.


Analysis

On the paper website you can find the key results of the bioinformatic analysis.

Here are a few steps needed to reproduce it:

  1. Clone the repository (git clone git@github.com:epigen/crop-seq.git) or download it from here: https://github.com/epigen/crop-seq/releases/tag/final_version
  2. Install required software for the analysis:make requirements or pip install -r requirements.txt
  • This includes looper (v0.7.2) [and pypiper (v0.6) - if you want to rerun the raw data].

If you wish to reproduce the processing of the raw data (all data have been deposited at GEO), run these steps:

  1. Download the data locally from GEO.
  2. Prepare a Looper configuration file similar to these that fits your local computing environment.
  3. Prepare a genome annotation containing gRNA sequences using make makeref and adapt the pipeline configuration file to point to the created files.
  4. Run samples through the pipeline: make preprocessing or looper run metadata/config.yaml

To run the analysis, you can either use the output from reprocessed data (make analysis) or download the gene expression matrices that include cell metadata (replicate, perturbed gene, gRNA assignments) from GEO with accession number GSE92872.