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Tools

The Cytomining ecosystem provides a suite of production-ready tools for every stage of the image-based profiling workflow.

For upcoming capabilities and in-progress projects, see the Roadmap, which includes our experimental tools under active development. For a focused plan across OME-Arrow, single-cell scale efficiency, and AI-ready workflows, see the focused development initiative.

copairs PyPI version for copairs GitHub stars for cytomining/copairs
Downstream evaluation — measures how reproducibly perturbation profiles retrieve their matched replicates, quantifying profile quality and similarity.
coSMicQC icon coSMicQC PyPI version for cosmicqc GitHub stars for cytomining/coSMicQC
Quality control step — flags and removes low-quality cells before profile processing to prevent artifacts from propagating downstream.
CytoDataFrame icon CytoDataFrame PyPI version for cytodataframe GitHub stars for cytomining/CytoDataFrame
Interactive exploration — view and inspect morphology profiles alongside their source cell images directly in Jupyter notebooks.
CytoTable icon CytoTable PyPI version for cytotable GitHub stars for cytomining/CytoTable
Upstream ingestion — converts raw CellProfiler, DeepProfiler, and IN Carta outputs into scalable, analysis-ready Parquet tables.
DeepProfiler icon DeepProfiler PyPI version for deepprofiler GitHub stars for cytomining/DeepProfiler
Alternative feature extraction — deep learning embeddings from raw microscopy images, bypassing classical segmentation-and-measurement pipelines.
Pycytominer icon Pycytominer PyPI version for pycytominer GitHub stars for cytomining/pycytominer
Core processing pipeline — aggregates, normalizes, and feature-selects morphology profiles for downstream analysis.