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‎config/_default/menus.en.toml‎

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pageRef = "about"
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weight = 40
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[[main]]
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name = "History"
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[[main]]
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name = "Contact"

‎content/about/_index.md‎

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---
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title: "About"
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description: "About the Cytomining ecosystem and community."
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title: About
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description: About the Cytomining ecosystem and community.
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---
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Cytomining is an open-source software ecosystem that maintains essential tools, workflows, and best practices for single-cell image-based profiling and microscopy data analysis research.
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We are proud members of the [CytoData](https://www.cytodata.org/) scientific community.
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Cytomining is an open-source software ecosystem for single-cell image-based profiling — the practice of measuring thousands of cell morphology features from microscopy images to understand how cells respond to genetic, chemical, or environmental perturbations.
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Core tools are maintained under the [cytomining GitHub organization](https://github.com/cytomining) by [contributors](https://github.com/orgs/cytomining/people) from the [Way Lab](https://www.waysciencelab.com/) at the University of Colorado Anschutz Medical Campus and beyond.
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Experimental and emerging tools are developed under the [WayScience GitHub organization](https://github.com/WayScience) before graduating into the core ecosystem.
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## Impact
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Cytomining tools have been adopted across academia and industry for large-scale drug discovery and functional genomics.
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`pycytominer` underpins some of the largest publicly available image-based profiling datasets, including the [JUMP Cell Painting dataset](https://jump-cellpainting.broadinstitute.org/) (over 136,000 chemical and genetic perturbations profiled across 12 partner sites) the [LINCS Drug Repurposing](https://github.com/broadinstitute/lincs-cell-painting) Cell Painting dataset, and the [EU-OPENSCREEN](https://www.eu-openscreen.eu/) Bioactive Compound Set profiled across multiple imaging sites.
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It has also processed many of the 31+ datasets in the [Cell Painting Gallery](https://broadinstitute.github.io/cellpainting-gallery/).
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The foundational [Caicedo et al. 2017](https://doi.org/10.1038/nmeth.4397) review has accumulated over 670 citations, and individual tool papers have together been cited more than 150 times since 2024.
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Packages are downloaded thousands of times per month via [PyPI] download statistics (https://pypistats.org/).
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## History
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The ecosystem traces its roots to the [Imaging Platform](https://www.broadinstitute.org/imaging) at the Broad Institute, where early contributors including Anne Carpenter, Shantanu Singh, Allen Goodman, and others developed `cytominer`, an R package for processing high-content imaging data.
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In 2016, members of this community co-founded the [CytoData Society](https://www.cytodata.org/) to unite researchers across academia and industry around image-based profiling.
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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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Today, Cytomining tools are used by research groups worldwide for drug discovery, functional genomics, and cell biology.
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## Community
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We are proud members of the [CytoData](https://www.cytodata.org/) community, which hosts an annual symposium bringing together researchers and developers working on image-based profiling.

‎content/experimental/buscar.md‎

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---
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title: "buscar"
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description: "Hit calling — identifies biologically active perturbations from single-cell morphological profiles using distribution-level scoring."
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problem: "Population-level hit calling averages away cell-to-cell variation, hiding heterogeneous responses and rare subpopulations."
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logoUrl: "https://raw.githubusercontent.com/WayScience/buscar/main/logo/just-icon.png"
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<img class="logo-light" src="https://raw.githubusercontent.com/WayScience/buscar/main/logo/with-text-for-light-bg.png" alt="buscar logo" width="400">
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<img class="logo-dark" src="https://raw.githubusercontent.com/WayScience/buscar/main/logo/with-text-for-dark-bg.png" alt="buscar logo" width="400">
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buscar is a Python package for reproducible hit calling in high-content screening.
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Rather than averaging cells into population-level summaries, it operates on individual cell distributions to preserve biological heterogeneity and identify perturbations with on-target morphological signatures.
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**Problem:** Population-level hit calling averages away biologically meaningful cell-to-cell variation, making heterogeneous responses and rare subpopulations invisible to standard metrics.
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`buscar` scores perturbations directly on single-cell distributions using Earth Mover's Distance, preserving heterogeneity throughout hit calling.
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**Key capabilities:**
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- Define on-target and off-target morphology signatures from reference profiles
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- Score perturbation efficacy via Earth Mover's Distance
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- Assess specificity with off-target scoring to reduce false positives
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- Preserve single-cell heterogeneity throughout hit calling
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- Integrates directly with pycytominer, coSMicQC, and CytoTable workflows
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- Integrates directly with `pycytominer`, `coSMicQC`, and `CytoTable` workflows
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**[View on GitHub →](https://github.com/WayScience/buscar)**

‎content/experimental/iceberg-bioimage.md‎

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title: "iceberg-bioimage"
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description: "Data cataloging — scans bioimaging stores and publishes image metadata to Cytomining-compatible Parquet warehouses via Apache Iceberg."
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problem: "Raw bioimaging archives have no standard catalog — finding, versioning, and joining images to downstream data requires bespoke scripts per lab."
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logoUrl: "https://raw.githubusercontent.com/WayScience/iceberg-bioimage/main/docs/src/_static/iceberg-bioimage-logo.png"
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---
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<img src="https://raw.githubusercontent.com/WayScience/iceberg-bioimage/main/docs/src/_static/iceberg-bioimage-logo.png" alt="iceberg-bioimage logo" width="400">
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iceberg-bioimage is a Python package that catalogs bioimaging data using Apache Iceberg.
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It scans image stores across formats, publishes structured metadata tables, and exports layouts compatible with the Cytomining profiling ecosystem — bridging raw image archives and downstream analysis pipelines.
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**Problem:** Raw bioimaging archives have no standard catalog — finding, versioning, and joining images to downstream data requires bespoke scripts per lab.
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`iceberg-bioimage` scans any image store into a versioned Apache Iceberg catalog that directly exports Cytomining-compatible Parquet warehouses.
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**Key capabilities:**
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‎content/experimental/ome-arrow.md‎

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title: "OME-arrow"
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description: "Image storage — stores microscopy images alongside metadata and derived data in a unified, queryable Apache Arrow format."
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problem: "Images and feature tables live in separate systems — linking a numeric outlier back to its source cell requires error-prone manual joins across formats."
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logoUrl: "https://raw.githubusercontent.com/WayScience/OME-arrow/main/docs/src/_static/ome-arrow-logo.png"
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<img src="https://raw.githubusercontent.com/WayScience/OME-arrow/main/docs/src/_static/ome-arrow-logo.png" alt="OME-arrow logo" width="400">
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OME-arrow brings Open Microscopy Environment (OME) specifications to Apache Arrow, enabling microscopy images to be stored directly in data tables alongside their metadata and derived features as multilayer structs.
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This makes bioimaging data fast to query, easy to share, and compatible with modern tensor-based ML workflows.
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**Problem:** Images and feature tables live in separate systems — linking a numeric outlier back to its source cell requires error-prone manual joins across formats.
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`OME-arrow` embeds images as first-class columns in Apache Arrow tables, so features, metadata, and pixel data travel together and can be queried or exported as tensors.
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**Key capabilities:**
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‎content/experimental/zedprofiler.md‎

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title: "zedprofiler"
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description: "3D feature extraction — extracts morphological features from volumetric microscopy images for CPU-efficient high-content profiling."
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problem: "Classical profiling tools only extract 2D features, leaving organoid and z-stack experiments without a CPU-efficient extractor."
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zedprofiler is a CPU-first toolkit for extracting morphological features from 3D volumetric microscopy images.
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It is designed for high-content and high-throughput workflows where classical segmentation-and-measurement pipelines need to scale to single-cell features in z-stacks.
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**Problem:** Classical profiling tools extract only 2D features, leaving organoid, cleared-tissue, and z-stack experiments without a CPU-efficient extractor.
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`zedprofiler` extracts morphological features directly from 3D volumetric images, including anisotropic voxel spacing correction — no GPU required.
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**Key capabilities:**
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‎content/history/_index.md‎

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‎content/media/_index.md‎

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title: "Media"
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description: "Videos, events, posters, and more from the Cytomining community."
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title: Media
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description: Talks, posters, and publications from the Cytomining community.
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Find videos, conference posters, event recordings, and other media from the Cytomining and CytoData communities here.
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## Talks
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- **CytoData 2020** — Gregory Way — _Pycytominer: a computational ecosystem supporting
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reproducible image-based profiling_ —
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[Watch on YouTube](https://www.youtube.com/watch?v=XJuGOnZygOU)
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## Posters
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- **SBI2 2024** — Jenna Tomkinson — _Single-cell Morphology Quality Control (coSMicQC)_ —
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[View poster](https://doi.org/10.5281/zenodo.13829960)
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- **SciPy 2023** — Dave Bunten — _Cross-language Data Grammar for Single-cell Research Feature
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Engineering_ —
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[View poster](https://doi.org/10.5281/zenodo.8137185)
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- **SBI2 2023** — Dave Bunten — _CytoTable: High performance and scalable single-cell morphology
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feature engineering_ —
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[View poster](https://doi.org/10.5281/zenodo.16945320)
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- **ParslFest 2025** — Dave Bunten — _With great Parsl comes great portability: Using Parsl
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through CytoTable for harmonizing single-cell data_ —
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[View slides](https://doi.org/10.5281/zenodo.16995782)
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## Publications
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### Reviews
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- Serrano E, Peters J, Way GP et al. (2026) — Progress and new challenges in image-based
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profiling — _Molecular Systems Biology_ —
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[10.1038/s44320-026-00197-7](https://doi.org/10.1038/s44320-026-00197-7)
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- Caicedo JC, Cooper S, Singh S, Carpenter AE et al. (2017) — Data-analysis strategies for
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image-based cell profiling — _Nature Methods_ 14(9):849–863 —
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[10.1038/nmeth.4397](https://doi.org/10.1038/nmeth.4397)
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### Tools
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- Serrano E, Chandrasekaran SN, Bunten D, Way GP et al. (2025) — Reproducible image-based
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profiling with Pycytominer — _Nature Methods_ 22:677–680 —
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[10.1038/s41592-025-02611-8](https://doi.org/10.1038/s41592-025-02611-8)
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- Kalinin AA, Arevalo J, Serrano E, Way GP, Singh S et al. (2025) — A versatile information
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retrieval framework for evaluating profile strength and similarity — _Nature Communications_ —
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[10.1038/s41467-025-60306-2](https://doi.org/10.1038/s41467-025-60306-2)
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- Moshkov N, Bornholdt M, Caicedo JC et al. (2024) — Learning representations for image-based
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profiling of perturbations — _Nature Communications_ —
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[10.1038/s41467-024-45999-1](https://doi.org/10.1038/s41467-024-45999-1)
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- Bunten D, Tomkinson J, Serrano E, Way GP et al. (2026) — Scalable data harmonization for
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single-cell image-based profiling with CytoTable — _Patterns_ 7(5):101514 —
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[10.1016/j.patter.2026.101514](https://doi.org/10.1016/j.patter.2026.101514)
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- Serrano E, Li WS, Way GP (2026) — Single-cell hit calling in high-content imaging screens
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with Buscar — _bioRxiv_ preprint —
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[10.64898/2026.04.15.718737](https://doi.org/10.64898/2026.04.15.718737)
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- Serrano E, Tomkinson J, Bunten D, Way GP et al. (2025) — Stellar quality control for
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single-cell image-based profiling with coSMicQC — _bioRxiv_ preprint —
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[10.1101/2025.10.14.682427](https://doi.org/10.1101/2025.10.14.682427)

‎content/tools/copairs.md‎

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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 morphological 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.
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**Key capabilities:**

‎content/tools/cosmicqc.md‎

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<img class="logo-light" src="https://raw.githubusercontent.com/cytomining/coSMicQC/main/media/logo/with-text-for-light-bg.png" alt="coSMicQC logo" width="400">
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<img class="logo-dark" src="https://raw.githubusercontent.com/cytomining/coSMicQC/main/media/logo/with-text-for-dark-bg.png" alt="coSMicQC logo" width="400">
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coSMicQC (Single-cell Morphology Quality Control) identifies and removes low-quality cells from image-based profiling datasets before downstream analysis.
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`coSMicQC` (Single-cell Morphology Quality Control) identifies and removes low-quality cells from image-based profiling datasets before downstream analysis.
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It catches common problems such as over-segmented nuclei, poorly segmented cells, and imaging artifacts.
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**Key capabilities:**
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- Flag over-segmented, under-segmented, and poorly focused cells
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- Apply threshold-based or z-score-based QC criteria
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- Generate summary reports of QC outcomes
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- Integrate seamlessly with CytoTable and pycytominer workflows
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- Integrate seamlessly with `CytoTable` and `pycytominer` workflows
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**[View documentation →](https://cytomining.github.io/coSMicQC/)**
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