Skip to main content
Strand AI predicts spatial protein marker channels directly from an H&E whole-slide image using Lattice, our spatial-proteomics imputation model. Each marker below has a model checkpoint trained against paired multiplex ground truth, predominantly from tumor biology. Predictions outside an oncology context are exploratory; see the intro page for what this means for non-cancer tissue.

​
Public panel

The public panel is the validated 8-channel set below: a nuclear counterstain plus seven biological markers spanning the T cell, B cell, macrophage, epithelial, proliferation, and checkpoint compartments. It is the panel we publish public cohorts and accuracy claims against. Additional markers are available to contracted partners under agreement. The expanded panel changes as checkpoints are validated, so we do not publish a fixed count: GET /markers is the authoritative list for your account. Contact support@strandai.com to discuss an expanded panel for your cohort.

​
Discovering markers programmatically

The GET /markers endpoint returns the exact set your account may request, so agents and scripts can discover valid names upfront instead of guessing. The list matches what a prediction will accept: a self-signup account sees the public panel; a contracted account sees its expanded panel.
The MCP server exposes the same list as the list_markers tool.

​
Reading the predictions

Predictions are returned as a multi-channel OME-Zarr aligned to the slide’s pixel grid. Each requested marker becomes a separate channel; the SDK helpers convert it to AnnData (Python) or SpatialExperiment (R) so you can work with it using your existing multiplex tooling. See the Quickstart for an end-to-end example and the Python / R SDK reference for the conversion helpers.

​
Accuracy caveats

  • Predictions are model outputs, not ground truth. Use them as a hypothesis-generation surface, particularly on cohorts that look out-of-distribution to the training data.
  • Per-marker confidence varies. Lineage and structural markers (e.g. PanCK, CD8) generally calibrate better than functional / activation markers (e.g. PDL1).
  • We do not yet publish per-tissue or per-organ accuracy numbers. Those will be added as benchmarks become available.
Reach out at support@strandai.com if you want pre-publication benchmarks on a specific tissue or marker.