SPATIA
Getting Started
Installation
Conda Environment
Package Installation
Generation Dependencies
Notes
Data & Training
MIST Dataset Construction
Stage A: Crop Cell Images into LMDB
Stage B: Build Annotated h5ad and Register in lamindb
Stage C: Merge Per-Dataset LMDBs
Data Loading at Training Time
Representation Training (Stage 1)
Prerequisites
SPATIA-scprint (Primary)
Configure paths
Run training
Extract embeddings (SPATIA-scprint)
SPATIA-scgpt (Alternative)
Run training
Extract embeddings (SPATIA-scgpt)
Architecture Summary
Downstream Tasks
Prediction Tasks
Table 2: Cross-Platform Clustering (Xenium + CosMx)
Step 1 — Extract SPATIA embeddings
Step 2 — Run multi-seed clustering
Table 3: Biomarker Prediction (HEST Benchmark)
Table 4: scRNA-seq Clustering & Annotation (GSE155468)
Option A — SPATIA only
Option B — All baselines
Cell Annotation
Labels and Reproducibility
Generation Pipeline (Stages 2 & 3)
Stage 2: OT-Based Perturbation Pairing
Cell-level pairing
Niche-level pairing (grid-based)
Stage 3: Flow-Matching Image Generation
Pretrained Checkpoints
Configure data paths
Training
Output structure
Evaluation (FID)
Morphology proxy encoder
Niche-Level Generation
Reference
Citation & Contact
Citation
Contact
SPATIA
Index
Edit on GitHub
Index