Grit is defined by Merrian Webster as "firmness of mind or spirit; unyielding courage in the face of hardship or danger". We borrow this concept to define a metric by the same name. Grit represents the phenotypic strength of a perturbation in a profiling experiment.

Whether an image-based profiling experiment (e.g. Measuring cell morphology via Cell painting) or a gene expression profiling experiment (e.g. perturb-Seq), grit can be calculated for each perturbation.

Grit is data-driven and requires minimal user intervention. The user just needs to define (1) replicate and (2) control profiles. The user does not need to determine an empirical null distribution, nor set any thresholds.

Grit combines two concepts:

1. How reproducible are perturbations of the same group (e.g. replicates)
2. How different are perturbations from a control group (e.g. DMSO treatment)

Additionally, since grit is based on z-scores, the magnitude can be easily compared between perturbations and is a directly interpretable value. E.g. A grit score of 5 for compound X compared to a DMSO control means that "on average, compound X is 5 standard deviations more similar to replicates than to DMSO controls".

We use the grit implementation in the cytominer-eval python package.

The cytominer-eval package is still under active development, but can still be installed:

# Install via pip
pip install git+git://github.com/cytomining/cytominer-eval@@56bd9e545d4ce5dea8c2d3897024a4eb241d06db

Also see environment.yml for a full package listing required to reproduce the analyses presented in this repository.

Calculating grit is a simple multi-step procedure:

Grit is directly interpretable: