Across 2025, Glencoe Software is making improvements to the performance and usability of analytical data managed within OMERO Plus, including building new inferfaces for sharing and mining rich datasets from a web browser.

Glencoe Software’s PathViewer and Pageant enable efficient interaction with large image analysis datasets managed within OMERO Plus. These include both image overlays (segmentation, heatmaps, etc.), stored in the cloud-native OME-Zarr format, as well as tabular datasets, stored as OMERO.tables. Routinely, these tables contain millions of rows (representing cells, nuclei, etc.) and hundreds of columns (measurements, classifications, features, gene expression levels, etc.). Glencoe’s omero2pandas is an open-source Python library designed to streamline data exchange between OMERO Plus and varied analytical pipelines.

See more about the workflows enabled via OMERO.tables and OME-Zarr label images here.

In our final release of 2024, Glencoe Software announced substantial performance improvements for the management and mining of large tabular analytical data by integrating a new OMERO.tables backend and developing an associated API. These changes significantly improve performance of the creation and interrogation of large analytical results via OMERO Plus.

Now, we are excited to share what is coming next in 2025, both back-end functionality for developers and new interfaces for our end users. See a preview of these new interfaces at the end of this post.

Enabling remote analysis

Analysis results can become massive, requiring dedicated high-performance storage for the generation and downstream consumption. Just like in-place importing images, OMERO Plus will manage complex analytical outputs without requiring its own special copy of the data. This means no data duplication and the flexibility to store analytical data where you want.

Improved developer tools to create, interrogate, and update remote analysis data

Glencoe Software has developed a new JSON API and CLI tools for registering both OMERO.tables and associated OME-Zarr label images from remote storage locations. In addition to initial registration, these APIs support the update or deletion of these registered locations in the event data is moved.

These features are available as a beta release. If your organization is interested in evaluating these new tools, reach out to our support team!

Extended support for object storage via AWS S3

Both TileDB and OME-Zarr, the formats of choice for analytical data in OMERO Plus, are well suited for working directly via object storage. Glencoe Software has proven its expertise in implementing and optimizing support for OME-Zarr image data (including label image overlays) within OMERO Plus, enabling cloud native image data management workflows for our customers. Both public and private data are supported, and we’ve previously shared our support for various authorization strategies, including access keys and instance profiles. Glencoe plans to validate the developer tooling described in the section above with S3 URIs in addition to file paths. This will include validation of currently supported authorization strategies. Finally, Glencoe will validate OMERO.tables in TileDB format within S3 for workflows in PathViewer, Pageant, and omero2pandas.

New interfaces for end users

With greater performance and flexibility of the backing data stores, both PathViewer and Pageant are evolving. We have previously shared ways to use these interfaces to generate populations based on object features managed in OMERO.tables. Expect updates this year on new ways to:

  • Generate spatial classifiers based on regions of interest drawn in PathViewer
  • Manually bin segmented objects as input for training downstream classifiers or as a controlled review workflow
  • Manage and export populations of classified objects, including creation of boolean combinations

All within OMERO Plus and via your browser of choice.

See a preview of these new interfaces in our poster presented with RareCyte at AACR in Chicago in April:

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Orion datasets were providing by RareCyte.