Main clinical bottlenecks: variability between platforms/cohorts, limited sample sizes, integration with histology/clinical metadata, and regulatory/validation requirements.

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Feature engineering — what you did well and what to add

You already capture cellular niches — great. To increase biological & spatial context consider adding:

1. Neighborhood-level statistics (beyond mean expression):

cell-type composition vectors per niche, richness/diversity, proportions of immune vs stromal cells.

2. Cell–cell interaction & signaling features:

ligand–receptor interaction scores aggregated per niche (e.g., CellPhoneDB-like summaries).

3. Spatial autocorrelation & topology:

Moran’s I / Geary’s C for genes or module scores; niche-level clustering tightness; nearest-neighbor distance distributions.

4. Morphology embeddings:

deep features from paired H&E / IF images (patch embeddings) aligned to ST spots — these often add orthogonal signal.

5. Multi-scale features:

local (immediate neighbors), meso (niche), global (regional gradients) — capture via hierarchical aggregation. HEIST-style hierarchical representations are promising.

6. Cell state / trajectory features:

niche-level scores for inflammatory programs, fibrosis, epithelial regeneration (pseudotime or gene module activation).

7. Technical covariates & QC features:

spot complexity, mitochondrial fraction, sequencing depth — include to let the model correct for batch/quality effects.

These augmentations improve both classification and interpretability because they tie model signals to known biology.

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Models and architectures — alternatives to MLP

An MLP on engineered features is a solid baseline (fast, interpretable). But for spatially structured ST data consider architectures that explicitly model relationships:

Graph Neural Networks (GNNs) — model cells/spots as nodes with edges to neighbors; naturally encodes spatial relations and neighborhood interactions (many recent ST tools use GNNs). Good for leveraging topology.

Hierarchical Graph / Graph Transformers — capture multi-scale tissue structure (e.g., HEIST hierarchical graph transformer). Useful if you want cross-level message passing (cell ↔ niche ↔ region).

Contrastive / Representation learning (CL) — learn robust embeddings integrating gene expression, location, and histology (helps domain transfer / small-sample regimes).

Transformer + GNN hybrids — for joint modeling of spot sequences/patches and graphs (seen in recent ST/histology work).

Ensembles / stacking — combine MLPs on engineered features + GNN on raw spot graph + image CNN embeddings; stacking often outperforms single architectures in heterogenous data.

Recommendation: keep the MLP baseline, then test a GNN and a hierarchical graph transformer. Use representation-learning pretraining (contrastive objective) if you plan to scale to other cohorts.

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Scaling to larger / more diverse cohorts

Practical steps to scale and increase robustness:

1. Harmonization & batch correction

Use ST-specific integration tools and multi-slice alignment (recent reviews list many options). Rigorously correct for platform and library differences.

2. Pretrain then fine-tune

Pretrain encoders on pooled ST datasets (self-supervised) then fine-tune on IBD labels — improves generalization.

3. Domain adaptation & augmentation

Simulate technical variation (depth, spot sizes), apply adversarial domain adaptation, or use contrastive objectives to learn invariant features.

4. Federated / multi-site training

If privacy prevents pooling, federated learning reduces distributional bias across centers.

5. Active learning / human-in-the-loop

Prioritize acquiring labels for boundary cases where model uncertainty is high.

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Interpretability — go beyond permutation importance & causal graphs

Permutation importance is useful but limited. Practical, complementary interpretability methods:

SHAP (global + local explanations) — quantifies feature contributions per prediction; good for tabular engineered features.

Integrated gradients / saliency maps — for deep models (GNNs/CNNs) to highlight genes, spots, or image patches driving a prediction.

Concept-based explanations (TCAV / concept activation vectors) — test whether abstract, human-readable concepts (e.g., “fibrotic niche”, “neutrophil-rich niche”) drive predictions. This links model outputs to clinician-meaningful concepts.

Counterfactual explanations — show minimal changes to features that flip a prediction (clinically useful: “if marker X were lower, prediction changes to UC”).

Prototype / case-based explanation — show representative spots/niches the model uses as exemplars.

Graph explainers — GNNExplainer, PGExplainer to identify subgraphs and node features important for predictions. Useful when using GNNs.

Finally, validate interpretability by checking whether the model’s top features align with known biology (literature + expert pathologist review) and by perturbation tests (knock out a feature and confirm model behavior changes as expected).

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Evaluation & validation — important tests to include

Cross-cohort validation (hold out whole sites) — crucial to show generalizability.

Calibration / reliability — e.g., reliability diagrams, expected calibration error.

Robustness checks — noise injection, spot downsampling, leave-one-patient-out.

Ablation studies — show which feature groups (morphology, neighborhood, signaling) drive performance.

Clinically meaningful metrics — sensitivity/specificity for clinically actionable thresholds, decision-curve analysis.

Explainability validation — correlate model explanations with orthogonal assays (IHC, ancestry of cell types, spatial proteomics if available).

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Practical experiment suggestions (low effort, high info)

1. Train MLP on current engineered features → obtain SHAP summaries.

2. Train a simple GNN on spot graph with same features → compare accuracy + explanations (use GNNExplainer).

3. Add H&E patch embeddings (pretrained CNN) to features; test performance gain.

4. Perform leave-one-site-out validation (if you have multi-center data or can simulate).

5. Produce a “clinician-friendly” report: top 3 niche types and genes that led to each prediction — test with pathologist blind review.

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Risks / translational challenges (and mitigations)

Inter-platform variability — mitigate with rigorous batch correction and multi-site validation.

Small n, high p — use pretraining/contrastive learning, regularization, and interpretable engineered features.

Regulatory / clinical adoption — need reproducible pipelines, clear interpretability, and prospective validation cohorts.

Human factors — deliver explanations in clinician language (niche descriptions, actionable biomarker thresholds), not just feature lists.

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Suggested short reply you can paste in the thread

> This is an exciting and timely framework — congrats! A few concrete suggestions: (1) expand engineered features to include ligand–receptor interaction scores, spatial autocorrelation (Moran’s I), niche diversity, and histology embeddings; (2) benchmark MLPs against graph-based approaches (GNNs / hierarchical graph transformers like HEIST) and contrastive pretraining — these explicitly model spatial topology and often improve transfer; (3) strengthen interpretability with SHAP + concept-level tests (TCAV), integrated gradients for deep models, and graph explainers for GNNs; (4) validate across cohorts with leave-one-site-out experiments and perform ablation studies so you can say which feature families are essential. Happy to help prototype any of these experiments or produce a reproducible pipeline.

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Useful recent reading (for citing in the thread)

Spatial single-cell technologies for exploring gastrointestinal disease (mini-review).

HEIST: hierarchical graph transformer for spatial transcriptomics (foundation model ideas).

SPACEL / other deep learning methods for spatial architecture characterization.

Review on XAI methods (SHAP/LIME, etc.).

Multi-slice ST alignment & integration review (practical tools for harmonization).