OmicSync: Reliability-Aware Spatial Multi-Omics Clustering with Evidence-Constrained LLM Reasoning

Rabeya Tus Sadia; Qiang Ye; Qiang Cheng

cs.CVAug 24, 2026

OmicSync: Reliability-Aware Spatial Multi-Omics Clustering with Evidence-Constrained LLM Reasoning

Authors: Rabeya Tus Sadia, Qiang Ye, Qiang Cheng

Organizations: Department of Computer Science, University of Kentucky · Department of Mathematics, University of Kentucky

Abstract

Spatial multi-omics technologies jointly profile gene expression, surface proteins, and histology at each tissue spot, yet most spatial domain discovery methods provide only cluster assignments, without indicating assignment reliability, modality contributions, or why a domain decision should be trusted. We present OmicSync, a reliability-aware spatial multi-omics framework that couples unsupervised domain clustering with evidence-constrained LLM reasoning using model-derived per-spot signals, including assignment confidence, epistemic routing uncertainty, and modality-routing weights. These signals are converted into structured evidence dictionaries and used to generate standard, stepwise, counterfactual, contrastive, and uncertainty-focused explanations. OmicSync integrates a KAN-GCN backbone with spatial encoding, cross-modal fusion, uncertainty-aware routing, cell-type supervision, and missing-modality imputation. We further introduce OmicSync-R, which closes the reasoning-clustering loop by using automatically computed reasoning-quality scores as REINFORCE rewards, allowing reasoning coherence to shape the latent structure without backpropagating through the language model. Across four 10x CytAssist FFPE spatial proteomics benchmarks, OmicSync achieves the best average rank on Human Tonsil (1.44), Glioblastoma (1.78), and Tonsil Add-on (1.22), and second-best on Human Breast Cancer (2.33). OmicSync-R further improves ARI on Human Breast Cancer from 45.73 to 46.72 and outperforms existing methods on six of nine clustering metrics. Together, OmicSync and OmicSync-R enable reliability-aware, spot-level auditable spatial domain discovery guided by evidence-constrained reasoning.

Figures & tables

Figure 1: Overview of the OmicSync architecture and training workflow. OmicSync integrates RNA expression, ADT protein abundance, H&E image embeddings, and spatial coordinates within a unified spatial multi-omics framework. RNA and ADT profiles are normalised and projected into PCA feature spaces, histological information is represented using pretrained image-patch embeddings, and spatial coordinates are used to construct neighbourhood graphs. The OmicSync backbone combines spatial position encoding, modality-specific graph encoders, intra-modality attention, cross-modal Transformer fusion, and uncertainty-aware mixture-of-experts routing to obtain a shared latent representation. The representation is optimised through reconstruction, clustering, optional auxiliary annotation, missing-modality imputation, and uncertainty-regularisation objectives. The framework outputs spatial domain assignments and model-derived per-spot reliability signals, which are assembled into structured evidence dictionaries and passed to an evidence-constrained reasoning module to generate natural-language reliability reports.

Dataset	Predicted classes	Spatial pattern
Tonsil	11	Mixed local domains
Breast Cancer	15	Region-level structure
Glioblastoma	11	Spatially coherent regions
Tonsil Add-on	9	Fine-grained mixed labels

Table 2: Task B qualitative output summary. Task B is used as a semi-supervised regularisation branch rather than a standalone cell-type benchmark because external ground-truth cell-type annotations are not available. The maps in Fig. 2 visualise the spatial distribution of predicted pseudo-cell-type labels.

Panel of Figure 2 — Figure 2: Task B pseudo-cell-type prediction maps across four CytAssist FFPE spatial proteomics datasets.

Strategy	GR $\uparrow$	CA $\uparrow$	SC $\uparrow$
Standard	1.000	0.650	0.700
Stepwise	1.000	1.000	0.975
Counterfactual	0.941	0.600	0.675
Contrastive	1.000	0.800	0.650
Uncertainty	0.074	1.000	0.500
Macro-average	0.803	0.810	0.700

Table 3: Reasoning quality by strategy. Each strategy is evaluated on the same 40 spots, comprising 10 spots from each dataset (200 generated justifications in total). GR = grounding rate; CA = confidence alignment; SC = spatial consistency.

Dataset	GR $\uparrow$	CA $\uparrow$	SC $\uparrow$	Avg. unc. $\downarrow$
Tonsil	0.800	0.940	0.620	0.0878
Tonsil Add-on	0.811	0.780	0.840	0.0526
Breast Cancer	0.800	0.780	0.580	0.0736
Glioblastoma	0.800	0.740	0.760	0.0664

Table 4: Reasoning quality by dataset, aggregated across all five strategies. Ten spots are evaluated per dataset, corresponding to 50 generated justifications per dataset. Avg. unc. = mean epistemic routing uncertainty per spot.

Figure 3: Task C reasoning analysis across four CytAssist FFPE datasets. (A) Reasoning quality by strategy: Stepwise achieves the highest overall faithfulness (GR = CA = 1.00, SC = 0.975); the uncertainty strategy sacrifices gene grounding (GR = 0.074) to achieve perfect confidence alignment (CA = 1.00). (B) Reasoning quality by dataset. (C) Average epistemic uncertainty per dataset; Tonsil Add-on achieves the lowest uncertainty (0.053), consistent with its best clustering rank (1.22). (D) Negative correlation ( $r=-0.42$ ) between assignment confidence and epistemic uncertainty across all 40 explained spots. (E) Dominant modality distribution; Glioblastoma shows the highest RNA dominance (5/10 spots), consistent with transcriptional heterogeneity at tumour margins. (F) Three-tier reliability audit: high-confidence spots exhibit $3.3\times$ lower epistemic uncertainty and higher spatial neighbourhood homogeneity than low-confidence spots. (G) Evidence grounding rate per strategy and dataset; the uncertainty strategy consistently produces near-zero gene grounding by design.

Tier	Confidence	$n$	Avg. unc.	Spatial hom.
High	$\geq 0.35$	9	0.034	75.6%
Medium	$0.20$ – $0.35$	23	0.070	61.7%
Low	$<0.20$	8	0.111	60.0%

Table 5: Three-tier reliability audit across the 40 explained spots. Low-confidence spots exhibit approximately

3.3\times

the mean epistemic routing uncertainty of high-confidence spots, whereas high-confidence spots show greater spatial neighbourhood homogeneity.

Epoch	GR $\uparrow$	CA $\uparrow$	SC $\uparrow$	$\bar{R}\uparrow$	$b_{t}$
50	0.980	0.370	0.450	0.551	0.505
100	1.000	0.578	0.481	0.650	0.517
175	1.000	0.589	0.500	0.661	0.541
225	1.000	0.775	0.417	0.706	0.555
350	1.000	0.688	0.500	0.700	0.581
425	0.925	0.313	0.229	0.437	0.571

Table 6: OmicSync-R reward trajectory on Human Breast Cancer (selected epochs). GR = grounding rate; CA = confidence alignment; SC = spatial consistency;

\bar{R}

= mean composite reward;

b_{t}

= EMA baseline. All 22 update steps are reported in the supplementary material.

Metric	OmicSync	OmicSync-R	$\Delta$
ARI $\uparrow$	45.73	46.72	$+0.99$
NMI $\uparrow$	54.10	52.20	$-1.90$
FMI $\uparrow$	51.75	53.54	$+1.79$
SilC $\uparrow$	28.92	30.20	$+1.28$
AMI $\uparrow$	43.72	45.79	$+2.07$
Jaccard $\uparrow$	35.70	38.78	$+3.08$

Table 7: OmicSync-R clustering results on Human Breast Cancer (

k=10

) compared to base OmicSync.

\Delta

= OmicSync-R minus OmicSync. Bold = best.

Clustering

Clustering groups data points by shared structure, developing methods to discover, validate, and interpret groups across diverse data and constraints.

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