This repository contains all code to reproduce the use cases presented in A scalable, reproducible and open-source pipeline for morphologically profiling image cytometry data.
It contains
To execute the workflows in this repository you need to install Snakemake.
The workflows have been tested with Python 3.9.13.
Reproducing the use cases is done by executing SCIP to profile the images, and Snakemake workflows to generate downstream analysis results.
The required configurations to run SCIP are in the scip_configs directory.
The following commands expect Snakemake to be available. Snakemake can be executed using conda environments or a pre-existing environment containing all required packages.
To reproduce a use-case, open a terminal where you cloned this repository and execute:
snakemake --configfile config/use_case.yaml --directory root_dir use_casewhere
use_caseis one ofWBC,CD7orBBBC021,root_dirpoints to where you downloaded the use case files
Make sure to update the config file to your situation; mainly setting the parts to the amount of output partitions SCIP generated.
This expects the environment to contain all required dependencies. Add --use-conda to let
Snakemake create a conda environment containing all requirements.
Data and features (for SCIP and IDEAS) can be downloaded at the Bioimage Archive
Data and SCIP features can be downloaded at the Bioimage Archive
Data can be downloaded at the Broad Bioimage Benchmark Collection. Features can be downloaded from Zenodo. You can download metadata BBBC021_v1_image.csv and BBBC021_v1_moa.csv from the supplementary materials "Data S2" in [1].
[1] Ljosa, V., Caie, P. D., Ter Horst, R., Sokolnicki, K. L., Jenkins, E. L., Daya, S., ... & Carpenter, A. E. (2013). Comparison of methods for image-based profiling of cellular morphological responses to small-molecule treatment. Journal of biomolecular screening, 18(10), 1321-1329.