Awesome
This package is deprecated and will no longer be supported. Please use at your own risk!
Cytominer-eval: Evaluating quality of perturbation profiles
Cytominer-eval contains functions to calculate quality metrics for perturbation profiling experiments.
Installation
Cytominer-eval can be installed via pip:
pip install cytominer-eval
Since the project is actively being developed, to get up to date functionality, you can also install via github commit hash:
# Example
pip install git+git://github.com/cytomining/cytominer-eval@f7f5b293da54d870e8ba86bacf7dbc874bb79565
Usage
Cytominer-eval uses a simple API for all evaluation metrics.
# Working example
import pandas as pd
from cytominer_eval import evaluate
# Load Data
commit = "6f9d350badd0a18b6c1a76171813aaf9a52f8d9f"
url = f"https://github.com/cytomining/cytominer-eval/raw/{commit}/cytominer_eval/example_data/compound/SQ00015054_normalized_feature_select.csv.gz"
df = pd.read_csv(url)
# Define important function arguments
meta_features = df.columns[df.columns.str.startswith("Metadata_")]
features = df.drop(meta_features, axis="columns").columns.tolist()
replicate_groups = ["Metadata_broad_sample", "Metadata_mg_per_ml"]
# Evaluate profile quality
evaluate(
profiles=df,
features=features,
meta_features=meta_features,
replicate_groups=replicate_groups,
replicate_reproducibility_return_median_cor=False,
operation="replicate_reproducibility",
)
Metrics
Currently, five metric operations are supported:
- Replicate reproducibility
- Precision/recall
- mp-value
- Grit
- Enrichment
- Hit@k
Demos
For more in depth tutorials, see https://github.com/cytomining/cytominer-eval/tree/master/demos.