Application Mode

Application mode reconstructs SVCs for real spatial transcriptomics datasets and then uses notebooks for downstream biological analysis.

Use this mode when your goal is a usable reconstructed tissue object rather than a ground-truth benchmark metric. The wrappers publish notebook-compatible files while the unified pipeline also keeps canonical artifacts and provenance under the resolved run directory.

Choose an Application Route

Route	Best fit	Wrapper	Published outputs
`application_sp`	hST data where spatial refinement and bin-to-cell correction are the main goals.	`application_sp_SVC_recon.py`	`sp_SVC.h5ad`
`application_sc`	iST data where selected-cell-type molecular completion is the main goal.	`application_sc_SVC_recon.py`	`sc_SVC_expr.h5ad` and `sc_SVC_spatial.h5ad`
`application_sc_sst`	Spot-based sST data routed through sc-SVC super-resolution.	Python API	Canonical artifacts under the run directory.

sp-SVC Application

Use sp-SVC for hST data such as Visium HD, where spatial refinement and bin-to-cell correction are the primary goal.

python application_sp_SVC_recon.py \
  --raw_data_path raw_data/Real_application \
  --sample_name P1CRC \
  --st_file HD.h5ad \
  --sc_ref_file adata_sc_all_reanno.h5ad

The wrapper defaults to output/sp_SVC_case and publishes a notebook-friendly copy at:

output/sp_SVC_case/<sample_name>/sp_SVC.h5ad

The sp-SVC wrapper enables legacy-compatible filenames so existing case notebooks can read the published sp_SVC.h5ad file directly. Canonical unified artifacts and provenance are also retained in the resolved run directory.

sc-SVC Application

Use sc-SVC for iST data such as Xenium, where matched scRNA-seq references are used to refine selected cell types and restore molecular completeness.

python application_sc_SVC_recon.py \
  --sample_name P2CRC \
  --data_type Xenium \
  --raw_data_path raw_data/Real_application \
  --sc_ref_file adata_sc_all_reanno.h5ad \
  --select_ct T

The wrapper defaults to output/sc_SVC_case and publishes notebook-friendly copies at:

output/sc_SVC_case/<sample_name>_<data_type>/<select_ct>/sc_SVC_expr.h5ad
output/sc_SVC_case/<sample_name>_<data_type>/<select_ct>/sc_SVC_spatial.h5ad

Use --legacy_mode when you also need legacy filenames inside the run directory itself. The published notebook copies are written regardless.

The reconstruction notebooks under reproduce/case build these files for the paper cell-type cases. The analysis notebooks read the published output layout and produce downstream figures for cell states, pathway activity, spatial patterns, and communication analyses.

Advanced Application Profiles

The root wrappers cover the paper-facing hST and iST paths. The unified Python API also exposes additional application profiles:

from revise.framework import REVISEPipeline

pipeline = REVISEPipeline()
svc = pipeline.run(
    profile="application_sc_hyper",
    io_overrides={
        "data_root": "raw_data/Real_application",
        "output_root": "output/sc_SVC_hyper_case",
        "sample_name": "P2CRC",
        "st_file": "Xenium.h5ad",
        "sc_ref_file": "adata_sc_all_reanno.h5ad",
    },
    set_overrides=["sc.select_ct=T"],
)

application_sc_hyper enables hyperresolution sc-SVC routing through ScSvcHyperApplicationStrategy. application_sc_sst routes spot-based sST data through the sc-SVC super-resolution application strategy.

Rendered paper case notebooks and additional maintained application notebooks are collected in Gallery, which also documents the Zenodo data download step for notebook reproduction. This page stays focused on application routes, wrapper commands, and the output files those notebooks consume.