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Why use Multi‑Sample FICTURE Analysis?

CartLoader supports analyzing ≥2 samples in two ways:

  • Multi‑sample FICTURE analysis (run_ficture2_multi): Jointly learns spatial factors across all samples and writes per‑sample outputs in one parallelizable run.
  • SGE stitch + single‑sample analysis (sge_stitch → run_ficture2): Stitch multiple SGEs into a single mosaic, then train one model on that mosaic.

What to expect

  • Shared factors/comparability: run_ficture2_multi learns a cohort‑wide latent basis and returns per‑sample decodes for direct comparison. The stitch approach yields a single model over the merged mosaic; useful when you need a unified coordinate system (e.g., tiling adjacent sections).
  • Efficiency and scale: run_ficture2_multi fits once for the cohort and decodes per sample, avoiding repeated runs and post‑hoc alignment. Stitching can be simpler for mosaics but often increases I/O and memory due to very large merged files.

Recommendation:

- Prefer `run_ficture2_multi` for most cohorts for clean per‑sample outputs and better computational efficiency; use stitching when a single shared coordinate frame is required.
  • If you choose stitching, plan for higher resource usage (RAM, disk, and I/O). Large mosaics can be slow to generate and train on, and may require substantially more memory and temporary storage than per‑sample runs.