Optical Pooled Screens 2019 Edition

This repository has been superseded by OpticalPooledScreens. Core functionality is largely the same but the analysis pipeline has been reorganized and expanded.

Example in situ sequencing-by-synthesis data (in example_data/) and analysis code (in ops/) for the publication Optical pooled screens in human cells.

We are currently in the process of depositing complete screening datasets (paired in situ sequencing and p65 translocation phenotyping) in the Cell-IDR database.

Installation (OSX)

Download the OpticalPooledScreens directory (e.g., on Github use the green "Clone or download" button, then "Download ZIP").

In Terminal, go to the OpticalPooledScreens project directory and create a Python 3 virtual environment using a command like:

python3 -m venv venv

If the python3 command isn't available, you might need to specify the full path. E.g., if Miniconda is installed in the home directory:

~/miniconda3/bin/python -m venv venv

This creates a virtual environment called venv for project-specific resources. The commands in install.sh add required packages to the virtual environment:

sh install.sh

The ops package is installed with pip install -e, so the source code in the ops/ directory can be modified in place. The package is compatible with Python 2.7 and Python 3.6, however results may not be numerically identical.

Running example code

Once installed, activate the virtual environment from the project directory:

source venv/bin/activate

You can then launch a project-specific notebook server:

jupyter notebook

The notebook ops_python.ipynb demonstrates step-by-step analysis using the high-level functions in ops.firesnake.Snake. The analysis pipeline can also be run using snakemake (after activating the virtual environment):

cd example_data
snakemake -s Snakefile_20180707_201