Experimental on Cytomining

buscar

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

buscar is a Python package for reproducible hit calling in high-content screening. 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.

Key capabilities:

Define on-target and off-target morphology signatures from reference profiles
Score perturbation efficacy via Earth Mover’s Distance
Assess specificity with off-target scoring to reduce false positives
Preserve single-cell heterogeneity throughout hit calling
Integrates directly with pycytominer, coSMicQC, and CytoTable workflows

View on GitHub →

iceberg-bioimage

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

iceberg-bioimage is a Python package that catalogs bioimaging data using Apache Iceberg. 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.

Key capabilities:

Scan image stores into canonical ScanResult objects
Publish image metadata with PyIceberg for versioned, queryable catalogs
Export Cytomining-compatible Parquet warehouses for profiling workflows
Validate profile tables against microscopy join contracts
Supports Zarr, OME-TIFF, and Parquet source formats

View documentation →

OME-arrow

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

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. This makes bioimaging data fast to query, easy to share, and compatible with modern tensor-based ML workflows.

Key capabilities:

Store images, metadata, and derived features together in a single table
Support for TIFF, OME-Zarr, NumPy, and Parquet source formats
Lazy reading and region-of-interest (ROI) access for large datasets
Tensor-focused output compatible with PyTorch, JAX, and DLPack
Visualization integrations for matplotlib, PyVista, and Napari

View documentation →

zedprofiler

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

zedprofiler is a CPU-first toolkit for extracting morphological features from 3D volumetric microscopy images. 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.

Key capabilities:

Extract features from 3D volumetric (z-stack) single-cell images
Multi-channel fluorescence microscopy support
Anisotropic voxel spacing correction for accurate 3D measurements
Modular, extensible architecture for custom feature sets
CPU-optimized for high-throughput processing without GPU dependency

View on GitHub →