omicstra-r4-v3

omicstra R4_v3 - cross-attention alignment head

The winning arm of omicstra's v3 alignment grid: a 2.1 M-parameter head that maps frozen H&E and spatial-transcriptomics embeddings into a shared 512-d space, so a niche in one modality retrieves its match in the other.

It is not an encoder. Virchow2 and Novae stay frozen and are not included here; this is the bridge between them.

• code: https://github.com/teslajoy/omicstra
• install: pip install omicstra
• grant: doi:10.55277/researchhub.6ou1w3h3

what it does

H&E   Virchow2 tile tokens  1280-d ─┐
                                    ├─ cross-attention ─→ shared 512-d
ST    Novae 64-d + gpath2vec 512-d ─┘
      = 576-d per niche

R4 is the only arm in the grid that uses cross-attention rather than mean- pooling the tiles before projection. That distinction is the finding: keeping local morphological heterogeneity un-pooled is what wins retrieval.

the number, and its scope

Patient-held-out is the only honest split here: the three subarrays per patient are consecutive 16 µm sections of one frozen block, so splitting below patient level reports subject identity as if it were method quality.

limits - read before using

This evidence is about one cohort, and does not transfer by assumption.

• Platform. Trained on the original Spatial Transcriptomics platform (Stahl et al. 2016): 100 µm spots, 150 µm centre-to-centre, ~200 cells per spot. Not 10x Visium. A different platform derives its own evidence or, in omicstra's terms, is not routable.
• Tissue. 92 triple-negative breast cancer patients (Wang et al. 2024).
• Encoder compatibility is empirical, not assumed. Novae was trained on image-based ST (MERSCOPE, Xenium, CosMx) at subcellular resolution, which does not overlap this platform.
• A caveat that travels with the H2 results: the structural-coherence reference label is a 14-class NMF factorisation of the same expression matrix, and it scores NMI 0.539 against patient identity - above the 0.5 confounder bar this project applies elsewhere. It is a derived reference, not expert annotation, and the seed cohort binds no annotation role at all.

The exact split is in split.json, the configuration in run_config.json, and the full grid with all ten arms is in the repository.

files

training

InfoNCE, τ 0.07, cross-attention fusion, MLP projection, dropout 0.3, lr 5e-4, weight decay 1e-3, max 50 epochs with patience 10, seed 42, CPU. No supervision signal: the positive pair is physical co-registration, not a label.

That last point matters. H&E and ST here come from the same 16 µm section - stained, imaged, then coverslip removed and permeabilised in place - so the matched pair is a fact of geometry with no registration error, rather than a label someone assigned.

using it

The checkpoint is a plain PyTorch state dict. In omicstra it is one arm of a grid, and the server will only route a question to it when a cohort's own recorded evaluation supports doing so:

pip install omicstra
export OMICSTRA_PROJECT_DIR=/path/to/a/cohort
omicstra route cross_modal_retrieval

A cohort with no evidence pack is reported as not routable rather than inheriting this one's winner. That refusal is the intended behaviour.

citation

@software{sanati2026omicstra,
  author  = {Sanati, Nasim},
  title   = {omicstra: a multi-agent MCP server for cross-modal embedding
             alignment and evidence-based routing in spatial biology},
  year    = {2026},
  doi     = {10.5281/zenodo.22666752},
  url     = {https://github.com/teslajoy/omicstra},
  license = {MIT}
}

Seed cohort: Wang et al. 2024, Nat. Commun. 15:10232. Funded by the ResearchHub Foundation.