Optical Pooled Screens of Essential Genes

This repository contains code and computational tools related to the publication, The phenotypic landscape of essential human genes.

For new projects using optical pooled screens, it is highly recommended to use the Github repository accompanying our Nature Protocols paper, Pooled genetic perturbation screens with image-based phenotypes: https://github.com/feldman4/OpticalPooledScreens.

More about this repository

This repository contains additional application-specific resources for our study of essential gene function using optical pooled screens.

This includes:

• Many additional image features implemented as functions operating on scikit-image RegionProps objects (features come from CellProfiler and additional sources)
• Functions for analyzing live-cell optical pooled screens (calling TrackMate for cell tracking)

Installation (OSX)

WARNING: many versions of dependencies will have trouble installing on Python 3.8. It is currently recommended to use Python 3.6. Setting up a Python 3.6 conda environment may be a convenient solution, set-up guide here.

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.

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

source venv/bin/activate

Data Access

Raw image data from the fixed-cell screen presented in the publication can be accessed from the public BioImage Archive data repository (study S-BIAD394). Note that the web interface for this study is currently inactive. However, all data can be accessed using Aspera and the corresponding command line tool, ascp.

First, download the IBM Aspera Command Line Interface (includes ascp) from here, and complete the install by following the platform-specific user guides available here.

Then, the image data can be downloaded using the following helper function, executed from the command line while the virtual environment created during installation of this repository is activated:

python -m ops.download download_from_bioimage_archive \
<local filesystem destination> \
--plate <plate> \
--dataset <dataset>
--well <well> \
--site <tile> \
--ascp <path/to/ascp/executable>

Where valid plates are:

20200202_6W-LaC024A
20200202_6W-LaC024B
20200202_6W-LaC024C
20200202_6W-LaC024D
20200202_6W-LaC024E
20200202_6W-LaC024F
20200206_6W-LaC025A
20200206_6W-LaC025B

dataset must be either sequencing or phenotype. Any or all of plate, well, and tile can be set to all to download all of the corresponding images. Note that if ascp is in your path, you can use --ascp=ascp. The full list of available files is present in this repository here.