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@@ -27,6 +27,7 @@ In 2016, members of this community co-founded the [CytoData Society](https://www
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The following year, the team contributed the landmark [Caicedo et al. 2017](https://doi.org/10.1038/nmeth.4397) review in _Nature Methods_ that established the field's foundational analysis standards.
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Since 2021, the [Way Lab](https://www.waysciencelab.com/) has driven a major expansion of the ecosystem — migrating from R to Python with `Pycytominer` and building a modern infrastructure stack including `CytoTable` and `coSMicQC`.
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In 2026, members of the CytoData community published an updated review, [Serrano et al.](https://doi.org/10.1038/s44320-026-00197-7) in _Molecular Systems Biology_, surveying progress and new challenges in image-based profiling.
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Today, Cytomining tools are used by research groups worldwide for drug discovery, functional genomics, and cell biology.
description: "Next-generation tools under active development in the WayScience organization."
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description: "Tools under active development in the WayScience organization, forming the foundation of the next Cytomining ecosystem."
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The following tools are under active development in the [WayScience](https://github.com/WayScience) organization and represent the next generation of the Cytomining ecosystem.
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The following tools are under active development in the [WayScience](https://github.com/WayScience) organization and form the backbone of the Cytomining roadmap.
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`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.
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`copairs` is a Python package for evaluating the quality of morphology profiles by measuring how well a perturbation's profile can be retrieved relative to controls.
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It implements mean Average Precision (mAP) and related metrics widely used in the image-based profiling community.
`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.
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`CytoDataFrame` extends the familiar pandas DataFrame interface to let researchers view and analyze single-cell morphology profiles alongside their corresponding microscopy images and segmentation masks — all within a Jupyter notebook.
`CytoTable` harmonizes output from different high-content image analysis tools — including CellProfiler, `DeepProfiler`, and IN Carta — into a single, analysis-ready format.
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It scales to large datasets using Apache Parquetand DuckDB under the hood.
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It scales to large datasets using Apache Parquet, DuckDB, and Parsl under the hood.
Next-generation tools under active development in the <ahref="https://github.com/WayScience" style="color: inherit; text-decoration: underline; text-decoration-color: #d1d5db;">WayScience</a> organization,
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designed to become the foundation of Cytomining v2.
The current Cytomining stack was designed around 2D single-cell data from CellProfiler.
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As the field moves toward <strong>3D organoid imaging</strong>, <strong>larger-scale archives</strong>, and <strong>deep learning feature extraction</strong>,
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several gaps have emerged: no standardized image catalog, images and features stored separately, no 3D support, and hit calling that collapses single-cell heterogeneity.
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The tools below are purpose-built to close each of these gaps — together forming a fully traceable, format-agnostic, 3D-capable profiling pipeline.
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As the field moves toward <strong>3D organoid imaging</strong>, <strong>single-cell profiling</strong>, <strong>larger-scale screens</strong>, and <strong>deep learning feature extraction</strong>,
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several gaps have emerged: no standardized image catalog, images and features stored separately, limited 3D support, and hit calling that collapses single-cell heterogeneity.
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We are building the tools below to close each of these gaps and raise the computational pipeline to support the demands of data collection and goals of the moment.
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