Pith Number

pith:LVRAYLFY

pith:2026:LVRAYLFYGYBZXYDGHF3AG67LOW

not attested not anchored not stored refs resolved

BaySC: Uncovering Tissue Architecture in Spatial Multi-Omics via Probabilistic Spatial Clustering

Guanyu Hu, Hanwen Ning, Lulu Shang, Xiaofei Dong, Xiao Wang, Xin Li, Xinyuan Song, Zhenke Duan

BaySC automatically infers the number of spatial domains from data and integrates multi-omics layers while enforcing local tissue coherence.

arxiv:2605.15291 v1 · 2026-05-14 · stat.AP

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{LVRAYLFYGYBZXYDGHF3AG67LOW}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

BaySC inherently learns the true number of spatial domains from the data by employing a Mixture of Finite Mixtures (MFM) prior. Tissue topology is modeled via a Markov Random Field (MRF) applied to discrete cellular assignments, a strategy that enforces local spatial coherence without distorting the underlying gene expression features. This enables BaySC to accurately map contiguous tissue layers as well as geographically scattered, transcriptionally identical cell populations.

C2weakest assumption

The modeling choice that applying an MRF to discrete cellular assignments enforces local spatial coherence without distorting the underlying gene expression features, and that the weighted log-likelihood fusion assigns biologically meaningful weights to modalities; if either assumption fails on real tissues, the claimed preservation of topography and multimodal integration would not hold.

C3one line summary

BaySC introduces an integrative Bayesian spatial clustering model with MFM prior for automatic domain count, MRF for local coherence, and weighted log-likelihood fusion for multi-omics data, validated on twelve datasets with competitive metrics and better spARI.

References

65 extracted · 65 resolved · 0 Pith anchors

[1] Three-dimensional intact-tissue sequencing of single-cell transcriptional states , author=. Science , volume=

[2] Genome Medicine , volume=

[3] Bayesian Analysis , volume= 2023

[4] Nature Methods , volume=

[5] Nature Methods , volume=

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:00:51.007526Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

5d620c2cb836039be0663976037beb75bb951834e2e6032070132ca47c6516dd

Aliases

arxiv: 2605.15291 · arxiv_version: 2605.15291v1 · doi: 10.48550/arxiv.2605.15291 · pith_short_12: LVRAYLFYGYBZ · pith_short_16: LVRAYLFYGYBZXYDG · pith_short_8: LVRAYLFY

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/LVRAYLFYGYBZXYDGHF3AG67LOW \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 5d620c2cb836039be0663976037beb75bb951834e2e6032070132ca47c6516dd

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "4e8ef4077c965792dda587104c3eac8ea48699441d2d4231fb8c618521b778ec",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "stat.AP",
    "submitted_at": "2026-05-14T18:07:26Z",
    "title_canon_sha256": "0be6d271afaea53040c7055a4d6c38906d5ba030721110a4c4d9c4b91f4948a8"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15291",
    "kind": "arxiv",
    "version": 1
  }
}