Tools on Cytomining

copairs

Mon, 01 Jan 0001 00:00:00 +0000

copairs is a Python package for evaluating the quality of morphological profiles by measuring how well a perturbation’s profile can be retrieved relative to controls. It implements mean Average Precision (mAP) and related metrics widely used in the image-based profiling community.

Key capabilities:

Define matched pairs based on experimental metadata
Compute mean Average Precision (mAP) for retrieval assessment
Evaluate intra- vs. inter-group morphological similarity
Scale efficiently to large screening datasets

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coSMicQC

Mon, 01 Jan 0001 00:00:00 +0000

coSMicQC (Single-cell Morphology Quality Control) identifies and removes low-quality cells from image-based profiling datasets before downstream analysis. It catches common problems such as over-segmented nuclei, poorly segmented cells, and imaging artifacts.

Key capabilities:

Flag over-segmented, under-segmented, and poorly focused cells
Apply threshold-based or z-score-based QC criteria
Generate summary reports of QC outcomes
Integrate seamlessly with CytoTable and pycytominer workflows

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Publication #

bioRxiv Preprint · 2025

Stellar quality control for single-cell image-based profiling with coSMicQC

CytoDataFrame

Mon, 01 Jan 0001 00:00:00 +0000

CytoDataFrame extends the familiar pandas DataFrame interface to let researchers view and analyze single-cell morphological profiles alongside their corresponding microscopy images and segmentation masks — all within a Jupyter notebook.

Key capabilities:

Display cell images inline with profile data in Jupyter
Link numerical features directly to visual representations
Overlay segmentation masks for quality inspection
Built on top of pandas for full compatibility with existing workflows

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CytoTable

Mon, 01 Jan 0001 00:00:00 +0000

CytoTable harmonizes output from different high-content image analysis tools — including CellProfiler, DeepProfiler, and IN Carta — into a single, analysis-ready format. It scales to large datasets using Apache Parquet and DuckDB under the hood.

Key capabilities:

Convert CellProfiler SQLite, CSV, and other formats into Parquet
Harmonize schema differences across analysis tools
Scale to datasets with millions of single cells
Produce outputs compatible with pycytominer and AnnData workflows

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DeepProfiler

Mon, 01 Jan 0001 00:00:00 +0000

DeepProfiler uses deep neural networks to extract morphological features directly from raw microscopy images, bypassing traditional segmentation-and-measurement pipelines. It is designed for high-throughput screens where deep learning representations outperform classical feature sets.

Key capabilities:

Train and apply convolutional neural networks for feature extraction
Support for EfficientNet, ResNet, and custom architectures
Crop and embed single cells from large microscopy images
Produce embeddings compatible with pycytominer and downstream profiling workflows

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pycytominer

Mon, 01 Jan 0001 00:00:00 +0000

pycytominer is the core Python package in the Cytomining ecosystem. It provides a clean, composable API for processing single-cell morphological profiles produced by tools like CellProfiler.

Key capabilities:

Aggregate single-cell data to well- or treatment-level profiles
Annotate profiles with experimental metadata
Normalize features using population-level statistics
Select high-quality features and remove noise
Output analysis-ready profiles in standard formats

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Publication #

Nature Methods · 2025

Reproducible image-based profiling with Pycytominer