SAINT: Sequence-Aware Integration for Spatial Transcriptomics Multi-View Clustering
Authors: Zeyu Zhu
KE LIANG
Lingyuan Meng
Meng Liu
Suyuan Liu
Renxiang Guan
Miaomiao Li
Wanwei Liu
Xinwang Liu
Venue: NeurIPS 2025 poster ()
Keywords: Multi-view Clustering
Graph Learning
AI4SCI
Primary area: general_machine_learning

Abstract: Spatial transcriptomics (ST) technologies provide gene expression measurements with spatial resolution, enabling the dissection of tissue structure and function. A fundamental challenge in ST analysis is clustering spatial spots into coherent functional regions. While existing models effectively integrate expression and spatial signals, they largely overlook sequence-level biological priors encoded in the DNA sequences of expressed genes. To bridge this gap, we propose SAINT (Sequence-Aware Integration for Nucleotide-informed Transcriptomics), a unified framework that augments spatial representation learning with nucleotide-derived features. We construct sequence-augmented datasets across 14 tissue sections from three widely used ST benchmarks (DLPFC, HBC, and MBA), retrieving reference DNA sequences for each expressed gene and encoding them using a pretrained Nucleotide Transformer. For each spot, gene-level embeddings are aggregated via expression-weighted and attention-based pooling, then fused with spatial-expression representations through a late fusion module. Extensive experiments demonstrate that SAINT consistently improves clustering performance across multiple datasets. Experiments validate the superiority, effectiveness, sensitivity, and transferability of our framework, confirming the complementary value of incorporating sequence-level priors into spatial transcriptomics clustering.

SAINT: Sequence-Aware Integration for Spatial Transcriptomics Multi-View Clustering
Zeyu Zhu'
Ke Liang"*
Lingyuan Meng' Meng Liu' Suyuan Liu!
Renxiang Guan'
Miaomiao Li National University of Defense Technology. Changsha, China
Wanwei Liu' Xinwang Liu"
Changsha College, Changsha, China
Abstract
Spatial transcriptomics (ST) technologies provide gene expression measurements with spatial resolution, enabling the dissection of tissue structure and function. A fundamental challenge in ST analysis is clustering spatial spots into coherent functional regions. While existing models effectively integrate expression and spatial signals, they largely overlook sequence-level biological priors encoded in the DNA sequences of expressed genes. To bridge this gap, propose SAINT (Sequence-Aware Integration for Nucleotide-informed Transcriptomics) unified framework that augments spatial representation learning with nucleotide-derived features. We construct seguence-anngmented datasets across 14 tissue sections from three widely used ST benchmarks (DLPFC, HBC, and MBA), retrieving reference DNA sequences for each expressed gene and encoding them using a pretrained Nucleotide Transformer. For each spot, gene-level embeddings are ag- gregated via expression-weighee and attention-based pooling, then fused with representations through a late fusion module. Extensive ex- periments demonstrate that SAINT consistently improves clustering performance across multiple datasetss Experiments validate the superiority, effectiveness, sensi- tivity, and transferability of our framework, confirming the complementary value incorporating sequence-level priors into spatial transcriptomics clustering.
Introduction
Spatial transcriptomics (ST) technologies measure gene expression while preserving the spatial lay- out of tissue sections, enabling the study of molecular organization in space [E3] HG, 511 544]. A key analysis task is clustering spatial spots into biologically meaningful regions, which reveals tissue structures and spatially patterned gene programs. This task can be naturally viewed as a multi-view clustering problem, where each spot is associated with both gene expression profile and spatial coordinates [522] 4X IR, 29]. Specifically, gene expression captures cell identity and state, while spatial proximity informs local organization and neighborhood relationships. Effectively integrating these heterogeneous views key to producing biologically meaningful clusters.
Early attempts on spatial transcriptomics clustering primarily relied on unsupervised methods ap- plied to gene expression profiles alone, ignoring the spatial structure inherent in the data [H5, [3] To incorporate spatial context, later approaches introduced regularization terms handcrafted spa- tial distances that penalize discontinuities across neighboring spots. While effective to some extent, these methods often struggle to generalize to irregular tissue geometries or heterogeneous microen- vironments. More recently, graph-based neural models have become the dominant paradigm due to their fiexibility in capturing complex spatial relationships(24) 25
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Corresponding authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Expusssion Proflle A
Expression Profile
Expression Proffle
Expression Profile HI
Sindler Exprossion
Similar Expresiea
ACCOCCOAG
Leads fn
OCCAGACGTE
Spott
Spot
Spot
Spot GOTAACOCT
GAGGATGGT TGTT
Sequence-Aware
Eacoding
Nucleotide
Transformer
X Misclustered existing
MALATT (mon-coding RNA)
Spot B: MT-ND4 (mitochonddrall genc)
Coll
Cell type B
Modal
Multi-Modal Late Fusson
Coll type C
Cell D
SAINT
Better cluster
Figure 1: Motivation of SAINT. Existing ST clustering models may miscluster spatial spots with similar expression profiles but divergent gene functions. For example, Spot A and Spot B share highly similar expression profiles, yet express functionally divergent genes such MALATI and MT-ND4 respectively. This leads to incorrect clustering by expression-onle models (left). By incorporating gene sequence features via a pretrained nueleotide encoder and multi-modal fusion. SAINT better distinguishes such cases and improves clustering accuracy (right).
SpaGCN [[S] constructs a spatial graph based on physical coordinates and applies graph convolu- tions jointly encode spatial proximity and gene expression. Building on this, Spatial-MGCN [97] aggregates multiple spatial graphs constructed from different biological priors (e.g., histological sim- ilarity, spatial distance), enabling richer representation of local neighborhoods. Further, MAFN [62] proposes late-fusion strategy that combines spatial, feature, and contrastive graphs through an adaptive attention module, improving clustering robustness under noise and sparsity. These GNN- based methods have consistently achieved stae-offthe-aae results across multiple ST benchmarks, highlighting the effectiveness of learning spot embeddings over spatial graphs.
However, existing ST clustering methods rely solely on observed gene expression and spatial prox- imity, while ignoring the rich biological information encoded in gene sequences. In practice, each spatial spot expresses set of genes, and each gene is uniquely associated with a DNA sequence composed of nucleotides. These sequences often reflect regulatory elements biochemical roles that are not evident from expression levels alone. As illustrated in Figure II spots A and exhibit similar expression profiles. However, their dominant genes, MALATI (a non-coding RNA) and MT- ND4 (a mitochondrial protein-coding gene), indicate distinct biological roles. This discrepancy can lead clustering models to mistakenly group spatial spots into the same region simply because their expression profiles look similar, even though the expressed genes may have fundamentally different biological functions. Despite this, no prior work has systematically incorporated nucleotide- sequence information into ST clustering, largely due to the following two key challenges in incorpo- rating gene sequence knowledge into spatial transcriptomies clustering.
(1) Lack of Annuence-Annottedd Datasets. Existing ST datasets do not contain nucleotide-level annotations, making it difficult to explore how gene sequences influence spatial gene expression or regional identity.
(2) Cross-Modal Representation Integration. Even with sequence information available, it re- mains unclear how to effectively encode gene sequences and integrate them with spatial and expression features for clustering. Naïvely combining modalities may lead to noise semantic mismatch.
To fill this gap. we propose SAINT (Sequence-Awar Awre Integration for Nucleotide-informed Transcriptomics). a sequence-informed framework that augments spatial transcriptomics with gene- level nucleotide embeddings. First, construct sequence-augmentte datasets across 14 tissue

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sections spanning three widely used benchmarks: DLPFC (12 slices), HBC. and MBA. For each gene expressed within a spatial spot, we retrieve the corresponding reference DNA sequence from NCBI and organize the data into spotgenesequence mappings, enabling the integration of nucleotide- level information into the modeling pipeline. Second, we encode each DNA sequence using the Nucleotide Transformer, large-scale pretrained language model for genomics. This yields rich, high-dimensional embeddings that capture regulatory signals and sequence-level semantics. These embeddings are projected into lower-dimensional space and aggregated at the spot level to form sequence-derived spot representations. After that, we observe that each spot typically expresses dozens of genes, but not all are equally informative. To mitigate the influence of noisy redundant sequences, we filter out low-variane genes and apply a lightweight attention mechanism to assign adaptive weights to the remaining gene embeddings, producing compact and spot-specific represen- tations. Finally, the learned sequence-aware embeddings are integrated with expression and spatial features using late fusion module. Experiments conducted evaluate the capacity of SAINT from four aspects: superiority, effectiveness, sensitivity, and transferability. The main contributions of our work summarized as follows.
Problem. Existing spatial transcriptomies (ST) clustering methods rely primarily on expres- sion profiles and spatial coordinates, overlooking the biological priors encoded in gene se- quences. This omission limits the semantic expressiveness of current representations and may lead to functionally mismatched clusters.
Dataset. To address this, we construct multiple sequence-augmented ST datasets spanning tissue sections from three benchmarks (DLPPC, HBC, MBA). Each spatial spot anno- tated with the reference DNA sequences of its expressed genes, enabling the first systematic exploration of nucleotide-level information in ST clustering.
Method. We propose SAINT, a sequence-infoormed multi-modal learning framework. SAINT encodes gene sequences using pretrained genomic transformer model, filters uninformative genes based on expression variability, and applies attention- aggregation to derive com- pact spot-level embeddings. These then fused with expression and spatial representations through a lightweight late-fusion architecture.
Experiment. Extensive experiments demonstrate that SAINT consistently improves cluster- ing performance across multiple datasets. We evaluate SAINT from four perspectives: supe- riority, effectiveness, sensitivity, and transferability, confirming the complementary value of integrating nucleotide-level features into ST representation learning.
Related Work
This section summarizes recent related works from three aspects: (1) multiview clustering methods in spatial transcriptomics data, (2) genomic language modeling for sequence representation, and (3) sequenceaugmented spatial transcriptomies clustering. Due to space limitations, please refer to Appendix A.2 for more detailed discussion.
3 Method
In this section, we present SAINT, multi-modal learning framework for spatial transcriptomics clustering that integrates spatial coordinates, gene expression, and sequence-derived biological pri- ors into unified spot-level embeddings. Unlike prior methods that rely solely spatial and ex- pression features, SAINT incorporates nuclcotide-level representations to enhance spatial domain discovery. The framework of SAINT is shown in Fig.D
The pipeline begins with data preprocessing, where each spatial spot associated with its expres- sion vector and the DNA sequences of its expressed genes. After normalization and selection of highly variable genes (HVGs), SAINT extracts complementary representations through two parallel modules. The structure-awant graphs from HVGs, and encodes them using graph convolutional networks (GCNs). These multi- view embeddings are then aggregated via attention to form graph-derived representation geraph In parallel, the sequence-aware encoder tokenizes DNA sequences and feeds them into a pretrained Nucleotide Transformer to produce gene-level embeddings, which are aggregated and projected into
graph embedding module constructs spatial, feature, and combined
3

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Structerre-wware Graph Embedding Module
Games spo4s
Fegrownis Sermmsleatio
sere prant
Calt
tye
Spucill graph
Col
GCN
MLP
Call type
(c) Mott--to /odal
Fesiun Module
Data Pre-prucssing
GCN
Teya DN3
NI-Encader
Fusion
Dec.
Nadbolile Trandeeteer
Scale
Saftims
MLP
n"
(b) Tramsformer-Bacce Sequence-Awer Aware Eacoder
Figure 2: An overview of the SAINT framework. The pipeline starts with preprocessing, where each spatial spot is linked its gene expression vector and DNA sequences. After normalization and HVG selection, two parallel modules extract structural and sequence-level features. (a) A graph embedding module encodes spatial, feature, and combined graphs via GCNs and fuses them through attention to obtain Zemph, (b) A transformer-basee encoder maps tokenized gene sequences to con- textual embeddings, which are aggregated form Z (c) The two representations are fused and passed to a ZINB decoder for expression reconstruction, and the resulting embeddings are used for clustering.
spot-level features 700 The two representations are integrated via late fusion module to obtain unified embeddings, which are then passed to a ZINB decoder for reconstructing gene expression. Clustering is performed in this fused latent space, capturing both structural topology and sequence semantics.
3.1 Problem Formulation
ST datasets consist of N spatial spots, each profiled with transcriptome-wide gene expression and associated spatial coordinates. Formally, each spot E 1, 1,...,, is described by three components: (1) a gene expression vector xi RR over G genes, (2) 2D spatial location s; E RRa and (3) a set of expressed genes Gi = 9.1, 9i2...... 9sM,, where each gene giz is associated with a DNA sequence dij of variable length. These sequences are encoded into fixed-length embeddings us- ing pretrained genomic language model. The objective of spatial transcriptomics clustering is to partition the N spots into K spatial domains C = C1. C2. ... Ck, such that spots within the same cluster share similar expression programs, spatial context, and sequence informed regulatory features. Unlike traditional transeriptomics clustering, this task involves integrating heterogeneous modalities, including gene expression, physical location, and sequence-derived embeddings, each with potentially different dimensionality, semantic structure, and noise characteristics.
sequence-informed
Formally, our aim is to learn a unified embedding function f (x., s;, si,di)) + z; E IRd for each spot i, such that the resulting embeddings .i...., Nn be effectively clustered into biologically mean- ingful domains. This requires (1) extracting semantically rich features from raw DNA sequences, (2) adapting to the varying number of genes per spot, and (3) designing robust fusion strategy that preserves complementary signals while mitigating cross modal redundancy.
3.2 Structure-Aware Graph Embedding Module
In parallel, SAINT models the spatial and expression views using graph-based encoders. We con- struct spatial neighbor graph G, = (V,E,) where nodes correspond to spatial spots and edges connect neighboring spots based on 2D distance thresholding. We also build feature graph Gy based on k-nearest neighbors in the gene expression space. Both graphs are encoded via GCNs.
To capture spatial proximity, expression similarity, and their interaction, we construct three graphs: the spatial graph G., the feature graph Gy, and the combined graph G.. All graphs share the same node set (spots), but differ in adjacency structure.

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Each graph is encoded separately using Graph Convolutional Network (GCN). Given a graph Ge with adjacency matrix A, and node features X, the GCN propagates features via as follows.
H 1,,
1/2
(I)
where A.,. A, + adds self-loops, D, is the degree matrix, W, is trainable weight matrix, and a(.) ReLU activation.
To model complementary topology between views, we define the combined graph Ge by aggregating the adjacency matrices of the spatial and feature graphs.
A A, +
(2)
This combined structure captures both spatial continuity and expression similarity in a unified topol- ogy. The corresponding node embeddings H., Hy, and Hc are then fed into an attention-based fusion module.
H F Famm H, H H.)),
where [] denotes feature-wise concatenation, and Fam(()) is attention-based MLP that putes normalized weights across views to adaptively control the fusion process.
3.3 Transformer-Baut Sequence-Aware Encoder
To capture biological priors at the sequence level, employ pretrained genomic language model, Nucleotide Transformer [IC]], to encode raw DNA sequences into dense vector embeddings. For each expressed gene Dij in spot retrieve its reference DNA sequence diy from curated databases (e.g., NCBI), and encode it into dense vector.
Zij NT-Encoder(dgg) E D,
where NT-Encoderr-) denotes the pretrained transformer model, and is the output dimension.
Since each spot may express variable number of genes, we aggregate these gene-level embeddings into fixed-dimensional representation using lightweight attention pooling module. Specifically,
for spot i, let Z; = [Za,... Z,M,] E Dx denote the matrix gene embeddings. We compute attention weights Ouj over genes based on a
scoring function.
exp(w" T tanh Wz.y))
Quj
M, Ex== exp(w" tanh Wz.y))
(5)
where W E R& and w E R. are learnable parameters.
The sequence-derived embedding for spot is follows.
Ogg
Zifr
(6)
To ensure compatibility with downstream fusion and reduce computational overhead, project the sequence embedding into a lower-dimensional space via a two-layer multilayer perceptron (MLP) with ReLU activation[I6].
MLP seg (z)e)) E Rd,
where de is thyer-ppaaaeete denoting the projected dimension.
3.4 Multi-Modal Late Fusion Module
To obtain a comprehensive spot-level representation, we integrate information from both spatial graphs and gene sequences. Specifically, for each spot i, we concatenate the structural embedding h with the sequence-derived embedding hat capturing complementary features from spatial ex- pression patterns and nucleotide-level signals. The concatenated vector then projected into shared latent space using a lightweight multilayer perceptron.
5

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h; = Fluse ([h, pen h, I),
(8)
where [: denotes concatenation, and f-)e (-) is a two-layer MLP with ReLU activation. This fusion module aligns the structural and sequence information into unified representation, enabling the model make better use of both expression and sequence-derived features during clustering.
3.5 Training Objective and Loss Functions
To jointly promote expression reconstruction, structural consistency, and cross-modal complemen- tarity, we incorporate three loss components into the final training objective.
ZINB Reconstruction Loss. To model over-dispersion and dropout noise in ST data, we adopt Cero-Inflated Negative Binomial (ZINB) decoder. The probability of observing count Fig for spot and gene is given as follows.
Tap + (1 Tig)
Osp Biy tie
if Tig = 0
ZINB(IIg pigs Oig. Tsg) =
(1-Tig) NB(rig Mig. Oig),
if Fig > 0
Here, Hig is the predicted mean expression, 0gg is the dispersion parameter, and Tig models the dropout probability. (B(.) denotes the negative binomial distribution.
The total reconstruction loss over all N spots and G' genes is computed as follows.
N
G
CIINB
log ZINB Hig Pig: Tig);
(10)
where Vig denotes the observed count for gene g in spot i.
Graph Contrastive Regularization. To preserve local structural smoothness in the learned embeddings, we encourage proximity between neighboring nodes and separation between non- neighbors via contrastive loss.
LReg ((.)eN [- log a (cos(h;; h,)) + (L)) log(1 - a ( hy)))]
(11)
Here, A denotes neighbor pairs in the spatial or feature graph, cos(-,)) denotes cosine similarity, and a (-)) is the sigmoid function. h; the final embedding of spot i after fusion.
Cross-Modal Redundancy Reduction (DICR). To encourage complementary information be- tween the structural and sequence branches, we minimize feature redundancy using decorrelation loss inspired by [B3]] Let C be the cross-correlation matrix computed between ez-normalieed em- beddings from the two modalities. The loss is defined follows.
SIICR = EC + (CA 1)2
(12)
(
where the first term penalizes off-diagonal correlations and the second term enforces identity align- ment on the diagonal.
Final Objective. We jointly optimize the model by minimizing the following weighted sum of losses.
L = LZNN + a LReg + LDICR:
(13)
where and y are hyper-parameters balancing the topology regularization and cross-modal decorre- lation objectives. For consistency and fair comparison, we adopt the same hyperparameter configu- ration as MAFN [62], using its default values across all experiments without further tuning.

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Experiment
We conduct comprehensive experiments evaluate the performance and robustness of our SAINT across multiple dimensions, i.e., superiority, effectiveness, transferability. sensitivity and case Study. Specifically, we aim answer the following five questions.
Q1: Superiority. Does SAINT outperform existing state-of ofee-of-hhe-ar models on spatial tran- scriptomics clustering benchmarks?
Q2: Effectiveness. How effective are the introduced sequence-aware augmentation strate- gies in enhancing clustering quality?
Q3: Transferability. Can SAINT be flexibly integrated into different clustering back- bones?
Q4: Sensitivity, How sensitive is SAINT to variations in hyper-parametens?
Q5: Case Study. Does SAINT produce biologically meaningful clustering results in real- world spatial transcriptomies datasets?
4.1 Experiment Setting
This section introduces the details of the experiment setting from four aspects, i.e., datasets, imple- mentation details, compared baselines and evaluation metrics. Due to space limitations, details are provided in Appendix A.3.
Table Clustering performance of competing spatial transcriptomics models. Bold entries indicate the best results, and underlined values denote the second-best.
Adjusted Rand Index (ARI)
Method
151507
151508
131509
151510
151669
151670
I51671
151672
HBC
MBA
SCANPY
0.20
0.15
0.19
0.14
0.10
0.09
0.12
0.12
0.49
0.23
SpaGCN
0.43
0.33
0.41
0.37
0.23
0.35
0.51
0.53
0.56
0.34
D
0.55
0.42
0.43
0.50
0.44
0.33
0.52
0.48
0.53
0.25
SCGDL [51]
0.49
0.34
0.32
0.31
0.24
0.26
0.31
0.34
0.35
0.26
stLeam [5
0.49
0.31
0.45
0.44
0.32
0.23
0.39
0.34
0.55
0.38
Spatial-MGCN
0.63
0.46
0.54
0.51
0.39
0.35
0.60
0.77
0.64
0.42
GraphST[Ga]]
0.48
0.49
0.52
0.50
0.48
0.46
0.61
0.63
0.54
0.41
StMMR[55
0.59
0.51
0.69
0.49
0.48
0.68
0.63
0.62
0.44
6AFN
0.68
0.51
0.71
0.61
0.56
0.48
0.82
0.60
0.43
SAINT-G
0.74
0.64
0.73
0.56
0.56
0.83
0.64
SAINT-SA
0.75
0.76
0.58
0.57
0.66
Normalized Mutual Information (NMI)
Method
131307
131508
151509
131510
151669
131670
131671
151672
HBC
MBA
SCANPY 89
0.21
0.21
0.27
0.22
0,16
0.16
0.24
0.23
0.52
0.45
SpaGCN X
0.54
0..42
0.55
0.50
0.42
0.45
0.60
0.61
0.56
0.62
DeepST]651 6
0.62
0.57
0.62
0.62
0.57
0.51
0.59
0.60
0.68
0.57
SCGDL [11]]
0.55
0.44
0.48
0.45
0.38
0.36
0.41
0.46
0.43
0.64
stLeam 6ff
0.64
0.53
0.62
0.59
0.49
0.41
0.54
0.47
0.63
0.66
Spatial- Moatial-MGCNIE MGCN
0.74
0.60
0.68
0.67
0.58
0.56
0.72
0.75
0.69
0.71
GraphST [E]
0.64
0.54
0.64
0.64
0.59
0.68
0.70
0.61
0.67
0.71
[56]
0.72
0.65
0.71
0.71
0.56
0.56
0.72
0.72
0.65
0.68
MAFN 62)
0.74
0.51
0.72
0,68
0.63
0.60
0.78
0.75
0.67
0.73
SAINT-G
0.77
0.69
0.73
0.72
0.62
0.63
0.78
0.79
0.69
0.72
SAINT-SA
0.78
0.71
0.74
0.73
0.64
0.64
0.84
0.80
0.70
0.74
4.2 Main Performance(RQ1)
To assess the effectiveness of SAINT, we compare it against nine state-of-the-an STC(Spatial Tran- scriptomics Clustering) methods on three benchmark datasets: DLPFC, HBC, and MBA. Table reports ARI and NMI scores; full slice results are provided in Appendix A.4. SAINT includes two variants: SAINT-G, which averages gene-level nucleotide embeddings, and SAINT-SA. which in- corporates sequence-aware attention for dynamic aggregation. As shown in Table II SAINT-SA consistently achieves the best or second-best results across nearly all slices, improving ARI from 0.64 to 0.68 and NMI from 0.69 to 0.71 average. On slice 151507, SAINT-SA achieves ARI

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Fusion
W.
12.1%
10.4%
Adjested $5
8.1%
151672
IBCC
MBA
Figure 3: Ablation study of different components in SAINT.
Table Clustering performance of competing spatial transcriptomics models. Bold entries indicate the best results, and underlined values denote the second-best.
151507
151508
151509
151510
Method
ART
NMI
ART
NMI
ART
NMI
ART
NMI
Spatial-MGCN
0.6305
0.7443
0.4622
0.6023
0.5441
0.6812
0.5158
0.6668
Spatial-MGCN SAINT-G
0.7125
0.7594
0.5888
0.6692
0.6766
0.6840
0.6189
0.6758
Spatial-MGCNA Spatal-MGCNNAAANNSSA
0.7357
0.7690
0.6476
0.6840
0.6986
0.7065
0.7060
0.7042
MAFN
0.6812
0.7402
0.5134
0.5183
0.7128
0.7213
0.6121
0.6822
MAFN+SAINT-G
0.7441
0.7723
0.6414
0.6933
0.7320
0.7279
0.7150
0.7165
MAEN+SAINT-SA
0.7471
0.7790
0.6843
0.7146
0.7426
0.7380
0.7649
0.7318
of 0.75 (+10.3% vs. MAFN), and an NMI 0.84 on 151671 (+16.2% vs. GraphST). Notably, SAINT-G also surpasses most baselines. For example, it improves ARI by 8.8% over MAFN and by 10.3% on slice 151671. These results highlight the value of integrating nuclcotide-level priors, even with simple aggregation. Additionally, SAINT maintains robust performance across both homoge- neous (MBA) and heterogeneous (HBC) tissue contexts.
4.3 Ablation Study (RQ2)
To evaluate the contribution of different components in our framework, we conduct an ablation study comparing three model variants: (I) w.o. Fusion, baseline without sequence integration; (2) w. SA, a variant that uses average-pooled gene sequence embeddings, and (3) SAINT, our com- plete model that incorporates sequence-awar attention and late-stage fusion. As shown in Figure BL SAINT consistently outperforms the reduced variants across benchmarks. For example, in terms of ARI, it yields relative improvements of +12.1% slice 151672, +10.4% HBC, and +8.1 % MBA. Even the average-pooling variant (w: G) surpasses the no-fusion baseline in most cases, in- dicating that sequence representations carry biologically meaningful information that complements expression-based features. Additional NMI results are reported in Appendix A.5.
4.4 Sensitivity Analysis (RQ3)
To evaluate the robustness of SAINT under different sequence embedding dimensions, we conduct a sensitivity analysis by varying di and dz, which represent the embedding dimensions used in SAINT- G and SAINT-SA, respectively. Each is selected from [16, 32, 64, 128, 256], and experiments are performed on three representative datasets, i.c., DLPFC (151507 as an example), HBC, and MBA. The ARI results are shown in Figure g. Specifically, on the 151507 data slice, ARI fluctuates between 0.7351 and 0.7471, with a relative deviation of only 1.63%. For HBC. ARI varies from 0.6289 to 0.6433, corresponding to a 2.30% range. On the more challenging MBA dataset, the ARI spans 0.4269 to 0.4623, yielding slightly wider but still manageable fluctuation of 3.30%. These small variations confirm that SAINT delivers stable clustering performance without requiring extensive hyperparameter tuning. Due to space constraints, the corresponding sensitivity results for NMI are reported in Appendix A.6, which exhibit similar trends.

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045
36
126
206
151507
HBC
MBA
Figure 4: Parameter sensstvvity analysis of the proposed SAINT on DLPFC, HBC and MBA datasets.
SCANPY
DrepST
annotation
(AREIR22 NMEALID)
(ARUO NMI-9.47
Raw
Layeel
Laver3
Theoe Bires Image
Laver5
(ARERAS (MI.L.7)
MAEN
SAINT (ARE9.75 NMI-A.7
IETH
NIKAA
A77064
SAINT
50
E
Raw
SAINT
DerpST
Figure 5: Case study visualizations DLPFC slice 151507. (A) Manual tissue annotation
laid on histological image. (B) Comparison spatial domain identification results produced by competing methods. (C) UMAP projection of learned embeddings. (D) Spatial expression patterns of representative marker genes before and after SAINT enhancement. (E) Violin plots illustrating gene-level expression differences across identified domains.
4.5 Transferability Analysis (RQ4)
We evaluate the transferability of SAINT by integrating into two representative backbonesSpatial- MGCN and MAFN. As shown in Table A both SAINT variants consistently improve clustering per- formance. For example, Spatial-MGCN-SAINN--G achieves an average ARI gain of +6.2%, while SAINT-SA further boosts it +8.3%. On slice 1,51008, SAINT-SA raises the ART 0.6476, relative improvement of +40.2% 5% over the baseline (0.4622). Similar gains are observed with MAFN. On slice 151509, MAFN+SAINT-SA achieves an NMI of 0.7380, outperforming MAFN+SAINT-G by 1.67% and the vanilla MAFN by 2.93%. These results demonstrate that SAINT functions as transferable and effective plug-in module. Meanwhile, this generality arises from the models modu- lar design, where the sequence-awe aware encoder and cross-moda fusion can be seamlessly attached to existing spatial frameworks without retraining from scratch. Such adaptability highlights SAINTs potential as unifying layer for future multi modal spatial transcriptomics methods. Additional analysis provided in Appendix A.7.
4.6 Case Study (RQ5)
We conduct case study on the DLPFC 151507 slice to assess the interpretability and biological relevance of SAINT. As shown in Fig. BIC), SAINT more accurately recovers cortical layer bound-

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aries compared to competing methods. For instance, it captures the transition between Layer 5 and Layer 6 in the lower-right region that is oversmoothed by DeepST and fragmented in SCANPY. To further validate biological plausibility, Fig. (S(D) shows spatial expression maps of representative marker genes (NEFH, FKBPIA, ATP2B4). SAINT enhances spatial coherence and alignment with anatomical structures. Violin plots in Fig. EM)) also demonstrate sharper inter-layer specificity and lower intra-layer variance. Notably, ATP2B4 and CRYM show clearer separation across domains, while B3GALT2 displays improved compactness within WM. These results highlight SAINTs abil- ity to generate biologically meaningful and anatomically consistent representations. Additionally, this improvement mainly stems from the incorporation of nucleotide-informed embeddings, which enable SAINT to better distinguish functionally divergent genes with similar expression levels and thus refine boundary delineation. Such sequence- representations provide mechanistic link between gene regulation patterns and observed spatial organization, further supporting the biological interpretability of the model. Additional case studies are provided in Appendix A.8.
Conclusion
In this work, we present SAINT, sequence-aware multi-modal framework for spatial transcrip- tomics clustering. Unlike previous methods that utilize only gene expression and spatial proximity, SAINT introduces gene-level nucleotide embeddings to capture additional biological priors. To en- able this integration, we construct spotgenesequence mappings across three benchmark datasets and encode sequences using a pretrained genomic language model. We design an attention- based aggre- gation module to summarize sequence features at the spot level, and employ late fusion strategy to combine them with pratial-expressiien embeddings. Extensive experiments across multiple datasets demonstrate that SAINT consistently improves clustering accuracy.
Acknowledgments
This work was supported by the National Key R & D Program of China No.2022YFA the National Natural Science Foundation of China (project No.61872371, 62032024, 62325604, 62441618, 62276271, 62506371).
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A Appendix
A.1
Limitations
While SAINT demonstrates strong performance and broad applicability, several limitations remain. First, the reliance on pretrained genomic models, such as the Nucleotide Transformer, may introduce biases from the training corpus, which predominantly includes well-annotated genes and reference sequences. This could limit generalizability to under--charcttrrize genes or non-model organisms. Second, our current framework treats all expressed genes equally during sequence aggregation after HVG filtering. Although attention mechanisms mitigate some noise, rare but biologically significant genes may still be weighte or omitted. Future work may explore more context-aware gene selection strategies.
A.2 Related Work
In this section, we summarize related work along three aspects. First, we review spatial transcrip- tomics clustering methods through the lens of multi-view learning, highlighting how expression and spatial features have been combined. Second, we introduce genomic language models that extract meaningful representations from DNA sequences. Finally, we discuss recent efforts toward inte- grating sequence information into spatial clustering, which remains an underexplored yet promising direction.
MVC in Spatial Transcriptomics Data. Clustering a central task in spatial transcriptomics (ST), aiming to delineate spatially coherent tissue domains that reflect underlying biological structure and organization. Existing methods for ST clustering can be broadly grouped into three paradigms. (1) Expression-based approaches [5], [14] perform unsupervised clustering purely based on transcrip- tional profiles, typically using K-means or community detection on PCA-reduced features. (2) Spa- tially regularized models [57], M] augment these strategies with spatial smoothing, either through distance-based penalties or spatial Laplacians to encourage neighboring spots to share cluster as- signments. (3) In contrast, graph neural network (GNN)-based methods [[3.
IS.
S. Tz explicitly
model spatial structure via graphs and propagate information using neural message passing. Among these, GNNs have emerged particularly effective due to their flexibility in modeling complex tissue architectures and incorporating multimodal inputs. STAGATE [[3]] introduces a graph atten- tion autoencoder that jointly learns from spatial adjacency and gene expression similarity, achieving state-of-the-ar clustering performance across multiple platforms. SpaGCN [18], in a similar spirit, integrates histological information into the spatial graph, enabling more anatomically consistent clus- tering via graph convolutions. Meanwhile, SOTIP [5.] formulates a spatial multi-task framework that captures nnino-environmental structure and intercellular context through local neighborhood graphs. Moreover, MAFN [[2]] leverages a multi-view fusion strategy, adaptively combining rep- resentations from both spatial graphs and gene-gene similarity graphs to enhance robustness and tissue-domain separation. While these methods have shown strong empirical performance, they are features and spatial graphs. They often overlook additional layers of biological prior knowledge, i.c., such as the regulatory or structural informa- tion embedded in gene sequences, which may influence spatial gene expression patterns but remain underexplored in current models.
all fundamentally built upon sxpression-derive
Genomic Language Modeling for Sequence Representations. While spatial transcriptomics clus- tering has traditionally focused on expression-level and spatial features, another promising source of biological prior lies in the gene sequences themselves. DNA sequences encode regulatory, struc- tural, and evolutionary signals that can influence gene activity and co-expression. Recent years have seen a surge in the development of genomic language models (GLMs), inspired by advances in self- supervised learning from natural language processing. These models treat nuclcotide sequences as form of structured text and learn contextualized embeddings using masked language modeling or next-token prediction objectives. For example, DNABERT [I9]] adapts the BERT architecture [[22] to k-mer tokenized DNA sequences, showing strong performance on transcription factor binding pre- diction and enhancer classification. Building on this foundation, Nucleotide Transformer [I0] scales the model size and diversity of training data to cover multiple species and larger sequence contexts, achieving robust generalization across downstream genomics tasks. Unlike traditional handcrafted motif features or one-hot encodings, GL.Ms capture long-range dependencies, compositional signals, and shared patterns across different genomic loci. These properties make them attractive for repre-
15

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sentation learning in biological applications, especially when labeled data are limited. Moreover, the pretrained embeddings can be transferred and fine-tuned serve various predictive tasks, such as variant effect prediction [ 600 38, 21]], epigenomic state modeling [411 59, 20, E2], and multi- R Despite these advances, the use of sequence-basee embeddings in spatial transcriptomics remains largely unexplored. Existing ST models rarely utilize the nucleotide sequences of expressed genes, thereby missing the opportunity to incorporate regulatory priors that may underlie the observed expression patterns. A few recent efforts have begun exploring protein- level embeddings for single-cell data [32], but nucleotide-leve integration in spatial contexts has not been systematically studied.
omics integration [15%, 27, D7
Sequeece-Augmentee Spatial Transcriptomics Clustering. Recent advances in genomic lan- guage modeling have enabled the extraction of rich sequence-level representations from raw DNA. While these sequence-level embeddings have shown utility in a range of genomies tasks, their incor- poration into ST' clustering remains largely unexplored. This subsection reviews recent advances in augmenting ST models with sequence-derived features. straightforward approach is late fusion, where sequence features are concatenated with spatial representations prior to downstream predic- tion. This method is modular and simple to implement, and has been widely applied multi-modal omics integration [G 28]. However, naive concatenation may fail to capture interactions between modalities and cannot dynamically adjust to varying gene importance across spatial contexts. To mitigate this, attention -based mechanisms have been introduced to assign adaptive weights to gene embeddings based on their contextual relevance. In spatial omics, attention modules have been used to integrate expression profiles with histological context neighborhood structure [T88, M. Soft at- tention mechanisms have also been employed to aggregate gene-level embeddings within each spot, yielding compact and informative representations that emphasize contextually relevant sequences. Transformer-base architectures offer a more flexible alternative by modeling interactions across gene sequences through self-attention. Though effective in natural language and genomics [9,, [Н), such models demand substantial training resources and are not yet widely used in spatial transcrip- tomics. Lightweight alternatives include expression-weigee weighted averaging, which gives more influ- ence to highly expressed genes in embedding aggregation [!!]] Filtering by HVGs provides another practical benefit, reducing redundancy and focusing on the most informative sequence signals [2]. Despite these developments, existing ST clustering methods rarely incorporate sequence-level pri- ors, leaving untapped the potential of regulatory DNA features in spatial organization. To bridge this gap, we introduce a novel framework that integrates gene sequence embeddings into ST clustering.
A.3 Experiment Setting
Experiment settings are introduced from four aspects, i.e., datasets, implementation details, com- pared baselines and evaluation metries.
Datasets. We conduct experiments on three benchmark datasets commonly used in spatial transcrip- tomics clustering:
We evaluate our method on three publicly available and widely used spatial transcriptomics datasets, spanning both human and mouse tissues. These datasets provide diverse anatomical and pathological contexts for robust model evaluation.
LIBD Human Dorsolateral Prefrontal Cortex (DLPFC) Dataset. The DLPFC dataset, curated by the LIBD research group [35]], provides high-resolution spatial transcriptomic profiles from post- mortem human brain tissue, generated using the 10x Genomics Visium platform. It comprises 12 sagittal tissue sections, each covering approximately 3,6004,000 barcoded spots with over 33,000 protein-coding genes profiled per section. These slices encompass the full laminar structure of the neocortex (L1L6) as well as the white matter beneath. The dataset includes domains based on histological examination, facilitating benchmarking of computational clustering models.
spatial
10x Visium Human Breast Cancer (HBC) Dataset. The HBC dataset [50] contains spatially re- solved gene expression measurements from human breast tumor sections. Each spot captures the expression of approximately 36,000 genes across diverse histological structures. The dataset is an- notated with 20 spatial domains, encompassing pre-invasive ductal carcinoma in situ (DCIS), lobular carcinoma in situ (LCCIS), invasive ductal carcinoma (IDC), tumor stroma, immune infiltration zones,
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Table 3: Statistics of the constructed sequence-augmentt datasets. Each dataset corresponds to tissue section or benchmark. "#B. class" denotes the number of unique barcodes (spots). "#G. class" indicates the number of distinct genes with available nucleotide sequences, and "Num" refers to the total number of (barcode, gene, sequence) triplets after filtering.
Dataset
#B. class
#G. class
Num
Dataset
#B. class
#G. class
Num
151507
721
237
24,968
151672
578
319
15,551
151508
849
190
11,940
151673
465
423
16,855
151509
697
249
12,489
151674
373
561
20,542
151510
737
233
12,092
151675
572
386
14,486
151669
563
341
16,034
151676
517
382
14,606
151670
596
316
15,089
HBC
182
250
32,734
151671
531
330
15,999
MBA
182
300
40,729
and adjacent normal tissue. This rich spatial annotation supports fine-grained exploration of tumor heterogeneity and microenvironmentall interactions.
Mouse Brain Anterior Tissue (MBA) Dataset. The MBA dataset [26]] includes gene expression profiles from a sagittal-anterior section of the adult mouse brain, obtained using the Illumina No- vaSeq 6000 platform. It contains 2,695 spatial spots and over 32,000 genes, capturing molecular pat- across anatomical regions such as the cortex, hippocampus, and basal forebrain. This dataset enables detailed investigation of spatial gene regulation and inter-regional signaling in mammalian neural context. is publicly available through the 10x Genomics data repository.
Sequence-Auummented Triplet Construction.
To integrate mucleotide-level information into spatial transcriptomics clustering, we construct struc- tured sauuence-augmeted dataset for each benchmark. For every expressed gene within spatial spot (barcode), we retrieve its corresponding reference DNA sequence from the NCBI nucleotide database using standardized gene identifiers. Each data entry is formatted as a (barcode, gene, sequence) triplet. To ensure biological relevance and avoid noisy sparse entries, we remove barcodes expressing fewer than genes with valid sequences. This filtering step ensures that each spatial spot contributes sufficient nucleotide- marizes the resulting datasets. For each tissue slice or benchmark, we report the number unique barcodes (#B. class), the number of distinct genes with matched sequences (#G. class), and the total number of triplets (Num). These triplets serve as the input for sequence encoder modules and enable the downstream modeling of spatial domains with mucleotide-ifformed priors.
context for representation learning. Table E sum-
Implementation Settings. All models are implemented in PyTorch 2.0.1 and trained using the Adam optimizer [22]] on workstation with Intel Core i9-9900K CPU, 64GB RAM, and an NVIDIA RTX 3090 Ti GPU. Following MAFN [62],, we adopt consistent training settings and learn- ing rate schedules. For sequence embedding, we evaluate two aggregation variants:
SSANNT-:: gene sequence embeddings are averaged without expression weighting.
SAINT-SA: expression-awe attention pooling applied to gene embeddings per spot.
The projection dimensions d1 (for SAINT-G) and d2 (for SAINT-SA) are selected from {16,32 32, 64, 128, 256).
Compared Baselines. To comprehensively evaluate the effectiveness of SAINT, we compare against wide range of state-of-the-art spatial transcriptomics clustering methods, including both traditional approaches and recent GNN-baser frameworks.
SCANPY [X]] is widely used single-cell analysis toolkit that performs PCA-based di- mensionality reduction and graphbased clustering on highly variable genes without incor- porating spatial context.
SpaGCN [[8] constructs spatial graph from tissue coordinates and employs graph convo- lution to jointly model gene expression and spatial dependencies for anatomically coherent clustering.
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193
A011%
NMH
138
Figure 6: Transfer study of SAINT variants the DLPFC 151507 slice under the Spatial-MGCN backbone.
DeepST [5_]] learns spatially informed spot embeddings by jointly modeling transcriptional profiles and physical locations, enabling accurate tissue domain segmentation.
SCGDL [II]] utilizes residual gated graph neural network augmented with deep graph information maximization module to capture hierarchical and long-range dependencies in spatial transcriptomic graphs.
stLearn [36]] integrates histological morphology with gene expression and spatial coordi- nates through self-supervised learning pipeline that regularizes clustering via spatially constrained random fields.
stMMR [56] applies Markov random field regularization to smooth clustering labels and enforce spatial coherence in noisy or irregular tissue regions.
GraphST [S=]] proposes spatially guided contrastive learning framework that enhances
intra-domain cohesion and inter-domain separation across spot embeddings.
Spatial-MGGN [ fuses multiple spatial and expression graphs using multi-vies graph
convolutions and attention mechanism to capture heterogencous tissue patterns.
MAFN [62] employs multi-branch graph convolutional architecture with adaptive late- fusion, allowing flexible integration of spatial and gene-gene similarity representations for robust clustering.
Evaluation Metrics. We adopt two widely used metrics for evaluating clustering performance:
Adjusted Rand Index (ARI) [67]]] Measures similarity between predicted and ground-trut truth cluster assignments, adjusted for chance. Given contingency table between true labels and predicted clusters, ART is computed as:
ARI Rlobs RL
(14)
max RI) RIand
where Rlahs is the observed Rand index measuring the similarity between predicted and ground- truth clusterings. RI denotes the expected index under random labeling, and max(RI) is the maximum attainable value.
Normalized Mutual Information (NMI) A predicted clusters. Defined as follows.
Quantifies mutual dependence between true and
2 2-I(Y;) NMI
(15)
H(Y) + H(Y)
where T(Y; Y) is the mutual information between true labels Y and predicted labels V, and H(-)) denotes entropy. Higher ARI and NMI values indicate hetter clustering alignment with biological ground truth.
A.4 Extended Analysis for Main Performance (RQ1)
To supplement the main performance discussion in Section 4.2, we provide detailed analysis of SAINT across all individual slices from the DLPFC, HBC, and MBA datasets, reported in Table !!
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W.O. Fusion
W. G
SAINT
Normalized Mutual Information (NMI)
441
3495
3.99
672
+333%
6.7%
14:0
3.9%
14:0-
672
14.3
34/99
3655
151672
HBC
MBA
Figure 7: Ablation study of different components in SAINT.
Table 4: Full clustering performance of competing spatial transcriptomics models. Bold entries
indicate the best results, and underlined values denote the second-best.
Mechod
151508
151510
151670
Adiusted
(ARD)
1S1673
HBC
SCANPY 0.20
0.15
0.19
0.14
0.10
0.09
0.12
0.12
0.20
0.22
0.22
0:39
0.23
SpaGCN
0.33
0.41
0.37
0.23
0.35
0.51
0.53
0.40
0.31
0.33
0.28
0.56
DeepST
0.55
0.42
0.43
0.50
0.44
0.33
0.52
0.48
0.54
0.55
0.53
0.56
0.53
0.25
SCGDL.
0.49
0.34
0.32
0.31
0.24
0.26
0.31
0.34
0.33
0.27
0.30
0.29
0.35
0.26
s(Learn
0.49
0.31
0.45
0.44
0.32
0.23
0.39
0.34
0.30
0.38
0.35
0.40
0.55
0.38
MGCN
0.63
0.46
0.54
0.51
0.39
0.35
0.60
0.77
0.61
0.69
0.54
0.57
0.64
0.42
GraphST
0.48
0.49
0.52
0.50
0.48
0.46
0.61
0.63
0.63
0.43
0.55
0.55
0.54
0.41
sMMM
0.59
0.51
0.58
0.69
0.49
0.48
0.68
0.63
0.60
0.51
0.57
0.55
0.62
0.44
MAFN
0.68
0.51
0.71
0.61
0.56
0.18
0.82
0.76
0.57
0.50
0.46
0.53
0.60
0.43
SAINT-G
0.74
0.64
0.73
0.71
0.56
0.56
0.83
0.56
0.57
0.5%
0.64
0.45
SAINT-SA
0.66
Method
151508
151510
1ST669
1516700
151671
151672
151673
151676
HBC
SCANPY
0:21
0.21
0.27
0.22
0.16
0.166
0.224
0:23
0.29
0.31
0.32
0.31
0:32
0:45
SpaGCN
0.42
0.55
0.50
0.42
0.45
0.60
0.61
0.55
0.46
0.46
0.46
0.56
0.62
DeepST
0.62
0.57
0.62
0.62
0.57
0.51
0.59
0.66
0.69
0.69
0.66
0.68
0.68
0.57
SOGDL
0.55
0.44
0.48
0.45
0.38
0.36
D:11
0.46
0.42
0.38
0.11
0.42
0.43
0.64
siLcarn
0.64
0.53
0.62
0.59
0.49
0.41
0.54
0.47
0.49
0.34
0.56
0.56
0.63
0.66
SpuNial-MGCN MGCN
0.74
0.60
0.68
0.67
0.58
0.56
0.72
0.75
0.68
0.69
0.67
0.67
0.69
0.71
GrapSST
0.64
0.54
0.64
0.64
0.59
0.68
0.70
0.61
0.74
0.61
0.63
0.06
0.67
0.71
sMMR
0.72
0.65
0.71
0.71
0.56
0.56
0.72
0.72
0.68
0.62
0.66
0.66
0.65
0.68
MAFN
0.74
0.51
0.72
0.68
0.63
0.60
0.78
0.75
0.62
0.60
0.67
0.67
0.73
SAINTEG
0.70
Overall Trends. SAINT-SA consistently achieves either the best or second-best performance across the vast majority of the 12 DLPFC slices. On average, it improves ARI from 0.64 (Spatial- MGCN) 0.68 and NMI from 0.69 to 0.71. Notably, SAINT-Gdespite its simpler averaging based sequence integrationalready outperforms all baselines in several slices, demonstrating the standalone benefits of incorporating gene-level nucleotide priors.
DLPFC Dataset. On DLPFC slices such as 151507, 151508, and 151509, SAINT-SA achieves ARI scores of 0.75, 0.68, and 0.74 respectivelyeach being the highest among all compared methods. Particularly, SAINT-G and SAINT-SA both outperform MAFN and GraphST by large margins. For instance, on slice 151671, SAINT-SA yields an NMI of 0.84, a +16.2% improvement over GraphST (0.72) and even surpasses MAFN by +7.7%. We also observe stable improvements on more chal- lenging slices such 151669 and 151670, where most baselines underperform (e.g., SCANPY 0.1 ARD). SAINT-SA boosts ARI to 0.58 and 0.57, respectively, offering clear gains in low-signal scenarios.
HBC Dataset. The Human Breast Cancer (HBC) dataset features heterogeneous tumor microen- vironments. Here, SAINT-G and SAINT-SA again dominate, reaching ARI scores 0.64 and 0.66, and NMI scores of 0.70 and 0.70. These surpass MAFN by +3.1% ARI and +3.7% NMI. Com- pared to SpaGCN and DeepST, SAINT provides more spatially coherent and functionally aligned clustering, as further confirmed in qualitative case studies (see Appendix A.8).
MBA Dataset. On the more homogeneous mouse brain anterior (MBA) tissue, SAINT maintains strong performance, with ARI and NMI reaching 0.46 and 0.74. These results demonstrate the
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A
Raw
sus sos avd evs 6r9
SAINT
SAINT
A1928
FS37AA
0111
C279
0271
199
1477
LOND
5J5 1.0
Figure 8: (A) Spatial expression of six marker genes before (Raw) and after SAINT enhancement. (B) Violin plots comparing layer-specific expression distributions these genes in Raw and SAINT outputs.
A
amnotation
B
(ARL:0.29 NMI-8.21)
GraphST (ARE:9.4 NML:0664)
(ARL-055 NMI-0.62)
DepST
(ARL:C49 NML:8.55)
Layer2
Layer3
Layor5
Layoró
SAINT
Thssee hires Image
NML1864)
NMB.0.72
C
SCANPY
GraphST
DeePST
SCGDL
sMMMR
MANN
SAINT
Figure 9: Case study of the proposed SAINT DIPFF((51507) dataset.
generalizability of SAINT across diverse spatial contexts. The minimal drop in performance from HBC to MBA suggests that the framework robust to differences tissue type, gene expression scale, and biological variability.
Overall, these improvements reflect the effectiveness of macleotid-innformel representations en- hancing spatial transcriptomies clustering.
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6.7
690
81
865
050
151507
HBC
MBA
Figure 10: Sensitivity analysis of SAINT on NMI metrics.
A.5 Detailed Ablation Study on NMI
To complement the ARI-based evaluation in the main text, we further analyze the NMI performance of different SAINT variants across three representative datasets: DLPFC 151672, HBC, and MBA. Figure 0 presents the corresponding NMI results.
Across all datasets, the full SAINT model achieves the highest NMI, validating the effectiveness of incorporating nucleotide-level priors through attention- ttased fusion mechanism. On DLPFC slice 151672, SAINT improves over the baseline by +6.7% in NMI. Similarly, on HBC and MBA, relative gains of +3.9% and +3.3% are observed. Notably, even the average-pooling variant (denoted as W. SA) provides substantial improvements over the no-fusion baseline. For instance, on slice 151672, w. SA achieves NMI of 0.79, compared to 0.75 for the baseline. These findings suggest that gene sequence embeddings capture biological context that complements spatial transcriptomic signals, even the absence of attention. The consistent improvements across both tumor (HBC) and healthy (MBA, DLPFC) tissues further demonstrate that sequence priors enhance clustering generalizability both homogeneous and heterogeneous spatial domains.
A.6 Extended Sensitivity Analysis (NMI)
To complement the ARI-based sensitivity results, we further report the corresponding Normalized Mutual Information (NMI) scores under varying sequence embedding dimensions. shown in Figure IΣI we evaluate performance across a grid of values for di and dythe embedding dimensions used in the SAINT-G and SAINT-SA modules, respectivelychoeen from {16, 32, 64, 128, 256}.
Across all three datasets (151507, HBC, and MBA), we observe that SAINT consistently maintains stable NMI scores under different dimension combinations. On slice 151507, NMI ranges from 0.7224 to 0.7386, with a relative variation of only 2.25%. For the HBC dataset, NMI varies from 0.6276 0.6550, corresponding dataset, scores range between 0.6212 and 0.6677, reflecting a 6.99% difference. These trends align with the ARI results and confirm that SAINT exhibits low sensitivity to the choice of sequence embedding dimensions. The model is capable of achieving strong performance across a broad range of d, and d2 values without requiring delicate tuning.
4.37% fluctuation. On the more structurally complex MBA
A.7 Impact of Sequence Embedding Dimension
To further investigate the effect of sequence embedding dimensionality on model performance, conduct an ablation study by varying the dimensionality d of nucleotide sequence embeddings across five values: {16, 32, 64, 128, 256). The results, presented in Figure E report ARI and NMI scores on the DLPFC 151507 slice for three models: the original Spatial-MGCN, Spatia-MMCN-SSAINT.. and Spatial-MGCN-SAINTSS.
We observe that both SAINT variants consistently outperform the base Spatial-MGCN across all dimensions. Notably, SAINT-SA achieves the best performance at d = 32 and d = 64, reaching an ARI of 73.57% and 72.38% respectively. This corresponds to a relative ARI gain of +10.5% and +9.3% compared to SAINT-G at the same dimensions, and + +16.7% and + 15.0% over the baseline
21

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A
Mannal anaotation
B
SCANPY (ARta8.49 SML:U53)
(AREaS4 NMI:
GraphST
DeepST (AREEES) NML:669)
(ARI=8.15 NMI:1.4)
0C51051
GCS40L5
Fellyy2
[XC
Tisuee hires Image
(AREaLS5 NME-663
NMB39667
0C5
brncste))
C
SCANPY
GraphST
DepST
SCGDL
MAEN
Figure 11: Case study of the proposed SAINT on HBC dataset.
Spatial-MGCN. Similarly, for NMI, SAINT-SA peaks at 64 with a score of 76.90%, surpassing SAINT-G and Spatial-MGCN by +0.96% and +2.47%, respectively. SAINT-G also consistently im- proves upon the base model, particularly at lower embedding sizes. At d 32, SAINT-G achieves 71.25% ARI and 75.00% NMIgains of +8.2% (ARI) and +0.57% (NMI) over the base model. How- ever, its performance tends to plateau or slightly decline at higher dimensions (d 128 or 256), suggesting potential overfitting redundancy without the attention-guided fusion mechanism.
These findings indicate that the sequence-aware attention module SAINT-SA enables more effec- tive utilization of high-dimensional embeddings, maintaining robust and discriminative representa- tions dimensionality increases. In contrast, the simple averaging strategy used in SAINT-G is more sensitive to dimensionality, showing diminishing returns beyond d = 64. Overall, the di- mensional analysis validates the robustness and scalability of SAINT, especially in its full attention- based variant. It further confirms that integrating gene sequence representations can enhance spatial clustering performance in a dimension-an manner.
A.8
Case Study Analysis
To further investigate the biological interpretability SAINT, we perform case studies on three representative spatial transcriptomics datasets (DLPFC, HBC, and MBA) to visually examine the clustering quality, gene expression continuity, and alignment with anatomical ground truth.
DLPFC: Layer-specific Marker Recovery and Cortical Architecture. Figure BA shows the spa- tial expression patterns of six canonical marker genesATP2B4, B3GAL72, CRYM, FKBPIA. NEFH, and RXFP/on slice 151507 before (Raw) and after (SAINT) model reconstruction. The raw data presents noisy, fragmented spatial signals. contrast, SAINT smooths out spurious variations and reveals clear laminar boundaries that correspond well to cortical layers. In Figure NB violin plots further demonstrate improved expression stratification across layers. Genes like ATP2B4 and CRYM exhibit sharper peaks in layer-specific distributions, indicating enhanced intra-cluster consistency. This suggests that SAINT captures biologically meaningful spatial organization that obscured in raw measurements.
To further validate the spatial fidelity of SAINT, Figure compares the clustering outputs of SAINT and six baseline methods slice 151507 of the DLPFC dataset. Panel A displays the manually anno- tated ground truth histological image for reference, while Panel B shows clustering results from competing models. As observed, classical methods such as SCANPY fail to capture spatial struc- ture, resulting noisy and biologically implausible regions. More advanced models like GraphST and DeepST partially recover cortical lamination but exhibit irregular boundaries or fragmented do-
22

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B SCANPY
GraphST (ARDE&A NMBRCT)
DeepST (AREHR2S NMBILST)
SCGDL
Mannal
hirvs
(ARESI,3 NMI18.45)
(AREO126 NML3L44)
<d Parn
aMMI
MAEN (ARFIR4) NABO0.7
SAINT
(ARD9935 NML8.60)
(ARD NXH1R.)))
(ARD1046 NML99.0
AD8
AAh
C
SCANPY
GraphST
DePST
SOGDL
MARK
SAINT
Figure 12: Case study of the proposed SAINT on MBA dataset.
mains. In contrast, SAINT produces well-aligned spatial clusters that closely match the manually defined cortical layers. Each layer is sharply delineated with minimal mixing, particularly in deeper layers such as Layer 5 and WM (white matter), where most other models show noticeable confusion. This suggests that the incorporation logical interpretability. Panel C presents the 2D UMAP projections of spot embeddings. Compared to scattered or overlapping clusters generated by prior methods, SAINT yields more compact and clearly separable groups, further confirming its ability preserve anatomical structure in the latent space.
nucleotide-level priors improves spatial coherence and bio-
HBC: Tumor-edge Delincation and Mieroenvironment Disentanglement.
As illustrated in Figure III SAINT shows improved resolution of complex tumor microenvironments in the HBC dataset. Compared to other methods that often over-fragment or blur tumor boundaries, SAINT delineates ductal carcinoma in situ (DCIS), invasive ductal carcinoma (IDC), and tumor- edge zones with higher coherence and spatial continuity. UMAP embeddings (Figure IIC) con- firm this observation. Clusters identified by SAINT are compact, well-separated, and show clearer boundaries between IDC subtypes (e.g., IDC1 to IDCs). Notably, transitional tumor-edge areas are correctly positioned at cluster boundaries, reflecting subtle expression gradients across tissue regions. These results demonstrate SAINTs ability to model fine-grained spatial heterogeneity in complex pathological samples.
MBA: Resolving Anatomical Hierarchies in the Mouse Brain. In the MBA dataset (Figure 12). SAINT excels reconstructing intricate anatomical subregions such as olfactory cortex (MOB), thalamus (TH), and hypothalamus (HY). While prior methods (e.g., SCANPY, SCGDL) tend to over-fragment or mix transitional areas, SAINT preserves spatial coherence and respects anatomical continuity. For instance, in regions like FRP::L2/3 and TH:RT, SAINT accurately recovers localized clusters that align with the brains hierarchical organization. The corresponding UMAP projection reveals compact and non-overlapping clusters, indicating that the learned embedding reflects both macro-structure and local transcriptional variation.
A.9 Theoretical Complexity Analysis
The computational complexity of SAINT is mainly determined by the multi-view GCN layers, where each branch processes a feature matrix E NN with adjacency A E N resulting in O(3) E F), where N is the number of spots, F the feature dimension, and |E| the number of edges. The sequence embedding branch only adds lightweight projection from precomputed embeddings of dimension de to dp with complexity O(Nd,dp), and the ZINB decoder adds negligible O(Nd,)
23

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term. Thus, the total complexity remains O F(),, comparable to GCN-based methods like MAFN, with only a small linear overhead from the sequence branch.
A.10 Comparison with Spatial-MGCN and MAFN
This study follows the experimental setup, datasets, and benchmarking protocols used in |AFN ensure fair comparison. The DLPFC, HBC, and MBA datasets, together with the same evaluation metrics, are adopted for consistency. Nevertheless, the proposed SAINT framework introduces several essential innovations beyond Spatial-MGCNjer and MAFN
First, previous methods mainly integrate spatial and gene expression information through graph con- volutional networks and adaptive fusion strategies, without considering the biological knowledge contained in gene nucleotide sequences. SAINT explicitly incorporates nucleotide-level representa- tions into the clustering process, allowing biologically distinct genes with similar expression profiles to be differentiated.
Second, SAINT employs sequence-aware encoder based on the pretrained Nucleotide Transformer, which converts gene sequences into high-dimensional embeddings that capture functional and regu- latory genomic information not accessible from expression data alone.
Third, while SpaNial-MGCN and MAFN both use expression and spatial graphs, SAINT integrates sequence-derived representations through cross-modal decorrelation loss (DICR). This loss pro- motes complementary information across modalities rather than simple consistency.
Fourth, SAINT further contributes by constructing benchmarks, enabling reproducible evaluation of sequence-informed spatial models.
datasets for widely used
While the overall architectural design inherits certain effective components from previous frame- work, such as the graph embedding backbone and the zero-inflated negative binomial (ZINB) re- construction loss, these elements serve as well-established foundations spatial tramscriptomics modeling. The ZINB loss is particularly suited to the sparse and overdispersed characteristies of gene expression data, offering better statistical fidelity than alternatives such as mean squared er- (MSE). On top of this foundation, SAINT introduces biologically motivated enhancements, in- cluding attention-based aggregation that dynamically weights nucleotide features according to their relevance, thereby emphasizing informative signals and suppressing noise.
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Reviews:
[{"review_id": "qw3LcpuOwz", "reviewer": "Reviewer_QgX6", "summary": "The paper proposes SAINT (Sequence-Aware Integration for Nucleotide-informed Transcriptomics), a novel multi-modal framework for clustering in spatial transcriptomics. It augments conventional spatial and gene expression signals with gene sequence-level priors, encoded using a pretrained genomic language model. SAINT integrates graph-based spatial and expression features with sequence-derived embeddings via attention mechanisms and late fusion. Experiments across multiple datasets demonstrate superior clustering accuracy, robustness, and transferability over prior methods.", "questions": "How does SAINT perform when the number of genes with valid nucleotide sequences is low? Does the method degrade gracefully?\nHow exactly is the spatial graph constructed? Is it k-NN in spatial coordinates, a radius threshold, or another method? What specific hyperparameters are used (e.g., k)?\nAre w. SA in Figure 3 and SAINT-G in Table 1 referring to the same variant? If not, what is the difference between them?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\nThe paper is well-written and easy to follow, with clear explanations of motivation, methodology, and results. The notations used are intuitive and easy to understand, which helps make the complex integration of modalities accessible. The idea of incorporating gene sequence priors into spatial transcriptomics is novel and addresses a meaningful gap in the field. The inclusion of attention-based aggregation for sequences and late fusion is well-motivated and effective. Results demonstrate that the method consistently outperforms strong baselines on benchmarks, and ablation studies highlight the complementary value of sequence-level information.\nWeaknesses\nWhile the paper proposes a novel framework, it largely combines existing components without much consideration of how each could be adapted or improved specifically for spatial transcriptomics data. The paper does not compare against scGPT‑spatial, which is a relevant pre-trained foundation model for spatial transcriptomics. Runtime and computational cost are not discussed, which is important given the use of large pretrained models and multi-view graphs. The graph construction details (e.g., k-NN, radius, hyperparameters) are not clearly specified. No ablation isolating the three graph types (spatial, feature, combined) to assess their individual contributions. Similarly, no ablation studying the impact of each loss term. The meaning of d1 and d2 in Figure 4 is unclear and should be explained. It’s ambiguous whether w. SA in Figure 3 and SAINT-G in Table 1 refer to the same variant.\n\nOverall, I think the paper has strong potential, but several important details and results are missing, and clarifying them would significantly strengthen the case for the proposed method.", "was_revised": true, "final_justification": "I raised originality score given the satisfactory answers to questions and additional evidence.", "rebuttal": "Thanks for your valuable suggestions! We respond to your questions point by point carefully, and we hope our responses can address your concerns.\n\n**Q1: The paper does not compare against scGPT‑spatial, which is a relevant pre-trained foundation model for spatial transcriptomics.**\n\n**RQ1:** We appreciate the reviewer's valuable suggestion. We fully agree that comparing our method with scGPT-spatial would strengthen our manuscript and provide valuable insights.\n\nIndeed, scGPT-spatial represents an important advancement with its continual pretraining and innovative architecture tailored to spatial transcriptomic data. Regrettably, only limited zero-shot inference demo is publicly available, which prevents us from conducting a comprehensive evaluation. We have reached out to the authors to inquire about the availability of the full codebase.  Upon its release, we will promptly integrate detailed comparative analyses into our revised manuscript.\n\n**Q2: Runtime and computational cost are not discussed, which is important given the use of large pretrained models and multi-view graphs.**\n\n**RQ2:** Thanks for your suggestions. To address this concern, we provide both a theoretical complexity analysis and empirical runtime measurements.\n\n**(1) Theoretical complexity analysis.** The computational complexity of SAINT is mainly determined by the multi-view GCN layers, where each branch processes a feature matrix $X \\in \\mathbb{R}^{N \\times F}$ with adjacency $A \\in \\mathbb{R}^{N \\times N}$, resulting in $O(3|E|F)$, where $N$ is the number of spots, $F$ the feature dimension, and $|E|$ the number of edges.  The sequence embedding branch only adds a lightweight projection from precomputed embeddings of dimension $d_s$ to $d_p$ with complexity $O(N d_s d_p)$, and the ZINB decoder adds a negligible $O(N d_p)$ term.  Thus, the total complexity remains $O(|E|F)$, comparable to GCN-based methods like MAFN, with only a small linear overhead from the sequence branch.\n\n**(2) Empirical runtime measurements.** We further measured the actual runtime on DLPFC slice 151507, using MAFN and SAINT variants under the same hardware environment (Intel Core i9-9900K CPU, 64GB RAM, with an NVIDIA RTX 3090Ti GPU). The results are summarized in the table.\n\n| Model | Runtime / epoch | Total runtime(250 epochs) |\n| --- | --- | --- |\n| MAFN | ~1.62s | 407.23s |\n| SAINT-G | ~1.63s | 407.72s |\n| SAINT-SA | ~1.65s | 411.51s |\n\nThese results show that the sequence-aware variants of SAINT incur only a marginal increase in runtime (less than 1\\% compared to MAFN), demonstrating that the integration of nucleotide-derived features introduces negligible computational overhead.\n\n**Q3: The graph construction details (e.g., k-NN, radius, hyperparameters) are not clearly specified.**\n\n**RQ3:** Thanks for your suggestions. In our implementation, the spatial graph is built by connecting each spot to its $k = 14$ nearest neighbors based on Euclidean distances, with an additional radius threshold of  $r = 560$ pixels to restrict edges to local neighborhoods. For training, we set the number of epochs to 250, the learning rate to 0.001, and the weight decay to $5 \\times 10^{-4}$. The hidden dimensions of the two GCN layers are 128 and 64, respectively, and dropout is set to 0.  We will explicitly describe these parameters in the revised manuscript to ensure clarity and reproducibility.\n\n**Q4: No ablation isolating the three graph types (spatial, feature, combined) to assess their individual contributions.**\n\n**RQ4:** Thanks for your valuable advice. To address this concern, we performed an ablation study isolating the spatial, feature, and combined graphs, and compared these settings with the full SAINT-SA model on three datasets (DLPFC-151507, HBC, and MBA). The results, reported in terms of ARI and NMI, are summarized in the table below.\n\n|  |  | **ARI** |  |  |\n| --- | --- | --- | --- | --- |\n| **Dataset\\Graph type** | **w. spatial** | **w. feature** | **w. combine** | **SAINT-SA** |\n| 151507 | 0.6047 | 0.2120 | 0.6317 | 0.7471 |\n| HBC | 0.6556 | 0.3800 | 0.6535 | 0.6622 |\n| MBA | 0.3821 | 0.3763 | 0.4334 | 0.4627 |\n|  |  | **NMI** |  |  |\n| **Dataset\\Graph type** | **w. spatial** | **w. feature** | **w. combine** | **SAINT-SA** |\n| 151507 | 0.7451 | 0.2940 | 0.7422 | 0.7792 |\n| HBC | 0.6934 | 0.4997 | 0.6893 | 0.7038 |\n| MBA | 0.7210 | 0.6145 | 0.7290 | 0.7436 |\n\nThese results reveal several insights. Firstly, using only the feature graph yields the weakest performance across all datasets, indicating that expression similarity alone is insufficient to capture spatial domains. Secondly, the spatial graph consistently performs better, confirming the importance of local spatial connectivity. Thirdly, combining the two graphs further improves ARI and NMI, demonstrating that integrating both spatial and feature information is beneficial. Finally, the full SAINT-SA model, which incorporates sequence-aware features in addition to the combined graph, achieves the highest performance on all datasets.\n\n**Q5: No ablation studying the impact of each loss term.**\n\n**RQ5:** We appreciate the reviewer’s request.  To address this, we conducted an ablation study where each loss component, including the ZINB reconstruction loss, the graph regularization loss, and the dcir contrastive loss, was removed individually while keeping the others unchanged. The ARI and NMI results on the DLPFC-151507, HBC, and MBA datasets are summarized below.\n\n|  |  | **ARI** |  |  |\n| --- | --- | --- | --- | --- |\n| **Dataset\\Loss term** | **w.o ZINB** | **w.o reg** | **w.o dcir** | **SAINT-SA** |\n| 151507 | 0.6940 | 0.6270 | 0.7219 | 0.7471 |\n| HBC | 0.5678 | 0.6058 | 0.5252 | 0.6622 |\n| MBA | 0.4158 | 0.4537 | 0.4204 | 0.4627 |\n|  |  | **NMI** |  |  |\n| **Dataset\\Loss term** | **w.o ZINB** | **w.o reg** | **w.o dcir** | **SAINT-SA** |\n| 151507 | 0.7551 | 0.7159 | 0.7615 | 0.7792 |\n| HBC | 0.6487 | 0.6286 | 0.6145 | 0.7038 |\n| MBA | 0.7151 | 0.7060 | 0.7132 | 0.7436 |\n\nThe ablation results demonstrate that removing any loss term reduces performance, confirming that all three components are essential.  In particular, excluding the ZINB loss causes a clear drop in ARI, highlighting its role in reconstructing gene expression distributions. Omitting the graph regularization term weakens the capture of local structures, while removing the dcir loss reduces cross-view consistency.  The full SAINT-SA model, integrating all losses, consistently achieves the best ARI and NMI, demonstrating their complementary contributions.\n\n**Q6: The meaning of $d_1$ and $d_2$ in Figure 4 is unclear and should be explained. It’s ambiguous whether w. SA in Figure 3 and SAINT-G in Table 1 refer to the same variant.**\n\n**RQ6:** Thanks for your valuable advice.  Regarding the first point, in Figure 4 $d_1$ corresponds to the embedding dimension used in SAINT-G, where gene-level sequence embeddings are averaged, while $d_2$ corresponds to the dimension used in SAINT-SA, where expression-aware attention is applied during aggregation.\n\nFor the second point, “w.  SA” in Figure 3 and “SAINT-G” in Table 1 refer to different variants.   The former is a simplified version with sequence embeddings but without the full fusion design, whereas the latter corresponds to the gene-level averaged sequence embedding variant.  We will make this distinction explicit in the revision.\n\n**Q7: How does SAINT perform when the number of genes with valid nucleotide sequences is low?**\n\n**RQ7:** Thanks for your question. To address this, we conducted an experiment where we progressively reduced the proportion of genes with valid nucleotide sequences and evaluated the performance of SAINT-SA. The reduction was simulated by randomly masking a fraction of the sequence embeddings, as shown in the table below.\n\n| Ratio of Valid Sequences | 1.0 | 0.8 | 0.6 | 0.4 | 0.2 | 0.0 |\n| --- | --- | --- | --- | --- | --- | --- |\n| ARI | 0.7471 | 0.7283 | 0.6924 | 0.6577 | 0.6445 | 0.6091 |\n| NMI | 0.7792 | 0.7467 | 0.7440 | 0.7358 | 0.7170 | 0.6888 |\n\nThe results show that although performance decreases as fewer valid sequences are available, SAINT-SA remains competitive even with only 20% of genes retaining sequence information.  When sequence data are entirely removed, its performance approaches that of the non-sequence variant, demonstrating the model’s robustness to missing nucleotide information.\n\n**Q8: Does the method degrade gracefully?**\n\n**RQ8:** To investigate whether our method degrades gracefully, we tracked the performance of both SAINT-G and SAINT-SA during training, as shown in the table below.\n\n| Epoch | 1 | 50 | 100 | 150 | 200 | 250 |\n| --- | --- | --- | --- | --- | --- | --- |\n| Total loss | 0.92 | 0.66 | 0.62 | 0.61 | 0.60 | 0.60 |\n| Relative Drop(%) | — | 28.30 | 6.10 | 1.60 | 1.60 | 0.00 |\n\nThe results show a larger drop in the early stage (up to 50 epochs), followed by only minor decreases as training continues, and eventually stabilizing at epoch 200–250. This consistent trend demonstrates that the proposed method degrades smoothly over time rather than experiencing abrupt performance collapses.\n\n**Q9: How exactly is the spatial graph constructed? Is it k-NN in spatial coordinates, a radius threshold, or another method?  What specific hyperparameters are used (e.g., k)?**\n\n**RQ9:** Thanks for your question. In our framework, the spatial graph is built using a radius-based neighbor search, connecting spots whose Euclidean distance is below a predefined threshold. We set the radius to 560 pixels to ensure each spot has a reasonable number of neighbors without creating overly dense connections. The adjacency matrix is then symmetrized to form an undirected graph. We will clarify this construction and its hyperparameters in the revised manuscript.\n\n**References.**\n\n[1] Wang, Chloe, et al. \"scGPT-spatial: Continual pretraining of single-cell foundation model for spatial transcriptomics.\" *bioRxiv* (2025)."}, {"review_id": "vXbcO9qmYp", "reviewer": "Reviewer_a1Hg", "summary": "This paper presents a novel spatial transcriptomics clustering method, SAINT, designed to address the issue in existing methods that rely solely on expression data and spatial location information, while overlooking the biological priors encoded in gene sequences. The main contributions of this work are: (i) The integration of gene sequence information, filling a gap in current research, and the use of a pretrained Nucleotide Transformer model to generate high-quality gene embeddings; (ii) The adoption of a late fusion architecture to integrate the learned nucleotide-derived gene expression information with spatial transcriptomic expression data, thereby achieving high-quality clustering performance. Experimental validation on 14 datasets demonstrates that the SAINT model excels in superiority, effectiveness, sensitivity, and transferability, achieving excellent clustering performance.", "questions": "1. SAINT has two variants: SAINT-G and SAINT-SA. However, in the subsequent experimental analysis (e.g., in section 4.3 Ablation Study, RQ2), it is not clearly specified which model was used.\n2. In section 4.4 Sensitivity Analysis (RQ3), although a sensitivity analysis was conducted, it primarily investigates the impact of embedding dimensions from different datasets on the results, without discussing the effect of the model's hyperparameters α and γ on clustering performance.\n3. Please provide a clear explanation of how the model derives the clustering results from the final embedding representations. Is a clustering algorithm (e.g., K-means, Leiden, etc.) directly applied to generate the results?\n4. The manuscript does not clearly specify the parameter settings for the comparison methods. Is the default parameter setting used? Furthermore, are the experimental results based on a single experiment or repeated multiple experiments? If the results are from a single experiment, how does the author ensure the fairness and repeatability of the results?", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "The paper integrates nucleotide-sequence embeddings with spatial expression data, while also addressing the challenges of noise and redundancy in new information. The application of attention mechanisms for dynamic learning and subsequent fusion clustering introduces a novel approach. These innovations substantially enhance the handling of spatial-omics data, showcasing a high level of originality.\nThe authors designed a comprehensive ablation study comparing SAINT-G, which uses simple averaging of nucleotide embeddings, with SAINT-SA, which integrates sequence-aware attention mechanisms. The performance improvements of the model were validated from four perspectives: superiority, effectiveness, sensitivity, and portability. Additionally, biological function analysis of key marker genes further reinforces the credibility of the conclusions.\nThe writing is generally clear, and the experimental results are well-analyzed, with figures and tables effectively presenting quantitative results. \n\nSome aspects of the experimental design are lacking. Although the paper references the MFAN paper for hyperparameter settings, sensitivity analysis of the model could have been expanded to include a more detailed examination of the model’s own hyperparameters, in addition to the analysis of embedding dimensions.\nThe experimental setup section is insufficiently detailed. In particular, the subsequent analysis does not specify whether SAINT-G or SAINT-SA was used for the corresponding parts of the analysis, which may cause confusion for readers. Additionally, some acronyms (e.g., \"STC\") are not defined, which lowers the readability of the paper.\nThe paper’s impact is somewhat limited by presentation issues that obscure the full extent of its contributions.", "was_revised": true, "final_justification": "I raised originality score given the satisfactory answers to questions and additional evidence.", "rebuttal": "Thanks for your valuable suggestions! We respond to your questions point by point carefully, and we hope our responses can address your concerns.\n\n**Q1: Although the paper references the MFAN work for hyperparameter settings, the sensitivity analysis provided in Section 4.4 (RQ3) is not sufficiently comprehensive. While this section examines the impact of embedding dimensions across different datasets, it does not analyze the influence of the model's own loss-related hyperparameters, such as $𝛼$ and $𝛾$, on clustering performance. Expanding the analysis to include these factors would provide a more complete understanding of the model's behavior.**\n\n**RQ1:**  We sincerely thank the reviewer for the helpful suggestion to expand the sensitivity analysis of our model's hyperparameters. In response, we not only examined the impact of embedding dimensions as requested but also performed an additional analysis on the two loss-related hyper-parameters, $α$  and $γ$.\n\nFor the embedding dimensions $d_1$ and $d_2$, the results show that ARI and NMI fluctuate within a narrow range, with variations not exceeding approximately 7%.  This confirms that the model exhibits stable performance and is not particularly sensitive to the choice of embedding dimensions.\n\n| **ARI** |  |  |  |  |  |  | **NMI** |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| $d_1$ \\\\ $d_2$ | 32 | 64 | 128 | 256 | 512 |  | $d_1$ \\\\ $d_2$ | 32 | 64 | 128 | 256 | 512 |\n| 32 | 0.7145 | 0.7445 | 0.7331 | 0.7262 | 0.7031 |  | 32 | 0.7665 | 0.7318 | 0.7504 | 0.7551 | 0.7227 |\n| 64 | 0.7031 | 0.6980 | 0.7401 | 0.7263 | 0.7319 |  | 64 | 0.7560 | 0.7301 | 0.7239 | 0.7762 | 0.7772 |\n| 128 | 0.6961 | 0.7471 | 0.7384 | 0.7061 | 0.7045 |  | 128 | 0.7679 | 0.7792 | 0.7258 | 0.7605 | 0.7461 |\n| 256 | 0.7046 | 0.7109 | 0.7223 | 0.7175 | 0.7102 |  | 256 | 0.7272 | 0.7493 | 0.7220 | 0.7738 | 0.7353 |\n| 512 | 0.7269 | 0.7023 | 0.7102 | 0.7141 | 0.7188 |  | 512 | 0.7592 | 0.7385 | 0.7508 | 0.7524 | 0.7309 |\n\nMeanwhile, for the loss hyper-parameters $𝛼$ and $𝛾$, the results exhibit somewhat larger fluctuations, with ARI and NMI varying by up to around 12% across the tested settings.  This variation is expected given the wide scale of parameter values explored.  The best performance is observed when both $𝛼$ and $𝛾$ are set to 0.1, suggesting this configuration is particularly effective.\n\n| **ARI** |  |  |  |  |  |  | **NMI** |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| $γ$ \\\\ $α$ | 0.001 | 0.01 | 0.1 | 1 | 10 |  | $γ$ \\\\ $α$ | 0.001 | 0.01 | 0.1 | 1 | 10 |\n| 0.001 | 0.6779 | 0.6857 | 0.7007 | 0.5980 | 0.6063 |  | 0.001 | 0.7431 | 0.7408 | 0.7459 | 0.7233 | 0.7279 |\n| 0.01 | 0.6769 | 0.6999 | 0.7223 | 0.6580 | 0.6118 |  | 0.01 | 0.7273 | 0.7406 | 0.7501 | 0.7398 | 0.7238 |\n| 0.1 | 0.6738 | 0.6548 | 0.7471 | 0.6214 | 0.6196 |  | 0.1 | 0.7191 | 0.7264 | 0.7792 | 0.7046 | 0.7015 |\n| 1 | 0.6854 | 0.6063 | 0.6441 | 0.7426 | 0.6307 |  | 1 | 0.7429 | 0.7250 | 0.7226 | 0.7560 | 0.7326 |\n| 10 | 0.6290 | 0.6383 | 0.7001 | 0.7106 | 0.6527 |  | 10 | 0.7105 | 0.6781 | 0.7493 | 0.7549 | 0.6931 |\n\nOverall, these results confirm that our model maintains robust clustering performance over reasonable variations in hyper-parameter settings.\n\n**Q2: The experimental setup section is insufficiently detailed.   In particular, SAINT has two variants: SAINT-G and SAINT-SA.  However, in the subsequent experimental analysis (e.g., in section 4.3 Ablation Study, RQ2), it is not clearly specified which model was used.**\n\n**RQ2:** We thank the reviewer for the insightful comments. We acknowledge that some parts of the experimental setup and the corresponding analyses were not sufficiently clear, particularly in Section 4.3 (Ablation Study, RQ2). In the original Figure 3, there was a labeling mistake: the middle column labeled w. SA should actually be w. G, which corresponds to the variant SAINT-G that uses averaged gene-level embeddings. The rightmost column labeled SAINT correctly refers to SAINT-SA, our final model incorporating sequence-aware attention.\n\nIn the revised manuscript, we have corrected this labeling error and added clearer descriptions of the experimental settings, including the selection of model hyperparameters, the construction of graphs, and so on.  We have also unified the naming conventions across all figures and tables to avoid any further confusion. Thank you again for highlighting this important point!\n\n**Q3: Some acronyms (e.g., \"STC\") are not defined, which lowers the readability of the paper.**\n\n**RQ3:** We thank the reviewer for pointing out this issue. We acknowledge that some acronyms, such as **STC**, were not explicitly defined in the original submission. In the revised manuscript, we have clarified that **STC** stands for **Spatial Transcriptomics Clustering** and have ensured that all acronyms are properly introduced upon first use to improve readability.\n\n**Q4: Please provide a clear explanation of how the model derives the clustering results from the final embedding representations. Is a clustering algorithm (e.g., K-means, Leiden, etc.) directly applied to generate the results?**\n\n**RQ4:** We appreciate the reviewer's interest in clarifying how clustering results are obtained from the learned embeddings.  In our framework, after the model outputs the final spot-level embeddings, we apply the K-means algorithm directly on these embeddings to obtain the clustering results.   The number of clusters $𝐾$ is set to match the number of annotated tissue regions for evaluation purposes.   The predicted cluster assignments are then compared with the ground-truth labels to compute ARI and NMI scores.   We will clarify this procedure explicitly in the revised manuscript.\n\n**Q5: The manuscript does not clearly specify the parameter settings for the comparison methods. Is the default parameter setting used? Furthermore, are the experimental results based on a single experiment or repeated multiple experiments?  If the results are from a single experiment, how does the author ensure the fairness and repeatability of the results?**\n\n**RQ5:** We sincerely appreciate the reviewer’s detailed question. For all comparison methods, we used the default parameter settings reported in their original publications to ensure fairness. For our proposed model, the reported results are based on multiple repeated experiments rather than a single run, thereby accounting for randomness during training. Similar to previous works such as Spatial-MGCN [1], MAFN [2], and GraphST [3], we fixed the random seed in all experiments to guarantee reproducibility.\n\nTo further evaluate stability, we conducted experiments with five different random seeds and reported the mean and standard deviation of ARI and NMI. The results, summarized in the table below, show that variations across seeds are small (standard deviations typically below 0.015), confirming the reliability and fairness of our reported results.\n\n| Method | Dataset | ARI | NMI |\n| --- | --- | --- | --- |\n| SAINT-G | 151507 | 0.7403 ± 0.0056 | 0.7723 ± 0.0137 |\n|  | HBC | 0.6418 ± 0.0110 | 0.6908 ± 0.0121 |\n|  | MBA | 0.4524 ± 0.0052 | 0.7208 ± 0.0147 |\n| SAINT-SA | 151507 | 0.7471 ± 0.0087 | 0.7823 ± 0.0145 |\n|  | HBC | 0.6613 ± 0.0123 | 0.7043 ± 0.0110 |\n|  | MBA | 0.4602 ± 0.0066 | 0.7404 ± 0.0066 |\n\n**Q6: Paper Formatting Concerns: The tables presented in the manuscript have insufficient row height, resulting in partial data being obscured. We recommend increasing the table height appropriately to ensure complete display of all results.**\n\n**RQ6:** We sincerely thank the reviewer for pointing out this issue. We acknowledge that, due to space constraints in the original submission, the row height and spacing of some tables were compressed, which resulted in partial data appearing unclear. In the revised manuscript, we have adjusted the formatting of all tables and figures to increase row height and spacing, ensuring that all results are displayed clearly and are easy to read.\n\n**References.**\n\n[1] Wang, Bo, et al. \"Spatial-MGCN: a novel multi-view graph convolutional network for identifying spatial domains with attention mechanism.\" BiB (2023).\n\n[2] Zhu, Yanran, et al. \"Multi-view adaptive fusion network for spatially resolved transcriptomics data clustering.\" TKDE (2024).\n\n[3] Long, Yahui, et al. \"Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST.\" Nature Communications (2023)."}, {"review_id": "qfF45SEpaB", "reviewer": "Reviewer_8jMd", "summary": "The paper proposes a novel model for multi-view clustering of spatial omics data by incorporating nucleotide-features into spatial representation learning. Their presented method, SAINT, is evaluated against a range of existing methods for clustering spatial transcriptomics data for 14 spatial samples. For the presented datasets, it either improves on or reaches comparable clustering performance to alternative methods in terms of the agreement with manual annotations of spatial domains.", "questions": "- How do you determine ground-truth clusters? Do you trust the data and annotation quality? Are there any dataset-specific considerations e.g. variations in annotation methodology or annotator agreement that could influence your results? It might be useful to mention in more detail in the limitations.\n\n- Could your report your analysis for a larger set of spatial datasets and include uncertainty estimates in your evaluation?\n\n- Did you base your experimental setups / dataset choices / benchmark choices on any pre-existing experiments in other works?\n\n- How did you decide which samples to report in the main table and which in the appendix? It might be fairer to report all results in the main paper, also ones for which your method did not perform best. Why have chosen not to visualise some of the methods from Tabel 4 in the later results in e.g. Figure 5 A-C as well?\n\n- Have you considered any further downstream tasks beyond clustering spatial observations? \n\n- Figure 5 (A) What does tissue hires image mean? Maybe it is a typo?", "limitations": "The authors mention limitations regarding the nucleotide transformer in the appendix. However, they could more clearly highlight the similarities and differences between their methodology and experimental results to existing works, as well as comment on the annotation and data quality.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "The presented paper is well-written and presents an interesting proposal in incorporating nucleotide representations into spatial domain clustering. It contains a detailed explanation of the proposed methodology and the 14 tissue samples analysed for this study. The benchmark compares with a large number of up-to-date baseline models for clustering these spatial transcriptomics data. Results show encouraging performance indicating that including nucleotide information gives better clustering performance as measured via the agreement with expert-annotations of spatial datasets. \n\nOne main limitations of the experiment is the **low number of evaluated spatial samples**, which are each illustrated individually. While data availability and annotation quality remain major concerns in spatial omics analysis, the presented samples are mostly taken from one single study (the LIBD Human Dorsolateral Prefrontal Cortex (DLPFC) Dataset). Hence, improvements in performance might not necessarily hold for different datasets. For example, the samples HBC and MBA reported in Table 1 already show fewer performance differences than on the samples from DLPFC. More extensive evaluation across further datasets would be needed to assess model performance across variations in sequencing technology, studies, and manual expert annotations.\n\nAnother major limitation is that **uncertainty in the results is not evaluated.** It is therefore difficult to assess the significance and certainty in the main results reported in Figure 3, Table 1, Figure 4, or Figure 5. The experimental evaluation would be greatly improved by considering setups for which uncertainty could be reported e.g. by summarising model performance across a larger range of spatial samples, spatial transcriptomics datasets, or varying random seeds.\n\nI have some concerns on **how similar the proposed model architecture is to e.g. Spatial-MGCN by Wang et al. (2023) or MAFN by Zhu et al. (2024)** as these papers and their experimental evaluation show high similarities. This warrants further discussion on the novelty of the proposed methodology and experimental results compared to existing works, specifically regarding the spatial and expression representations. I would like to ask the authors on please comment to which degree inspiration was taken from existing works. If the presented model architecture or experimental evaluation does indeed build up on existing models for multi-view clustering of spatial omics data, I would ask the authors to more clearly discuss and highlight these similarities in revisions and reference existing assets whenever appropriate (e.g. in Section 3.2. or 3.5). \n\nBased on the lack of uncertainty evaluation, the small number of spatial samples, and the high conceptual similarity with existing works on multi-view clustering, the paper presented in its current form presents **more major weaknesses than strengths.** While the incorporation of nucleotide-features is very interesting and promising, the evaluation of the presented method could be considerably improved in future work.\n\n\n**References**\n\n[1] Bo Wang, Jiawei Luo, Ying Liu, Wanwan Shi, Zehao Xiong, Cong Shen, and Yahui Long. Spatial-mgcn: a novel multi-view graph convolutional network for identifying spatial domains with attention mechanism. Briefings in Bioinformatics, 24(5):bbad262, 2023.\n\n[2] Yanran Zhu, Xiao He, Chang Tang, Xinwang Liu, Yuanyuan Liu, and Kunlun He. Multiview adaptive fusion network for spatially resolved transcriptomics data clustering. IEEE Transactions on Knowledge and Data Engineering, 2024.", "was_revised": true, "final_justification": "Resolved:\n- The authors have included uncertainty estimates by reporting results across varying random seeds.\n- Analysis of two more datasets shows high performance confirming the proposed method.\n- The authors plan to more clearly highlight and discuss similarities with existing methods in revisions.\n- This is the first work to include nucleotide information into spatial omics clustering.\n\nUnresolved:\n- The proposed architecture has a lot of components in common with existing models. Other than the nucleotide sequence aggregation module, the novelty of the spatial and gene-encoding components remain limited.\n- The planned revisions and additional experiments are moderate in scope.\n\nFinal Justification:\n- The authors have addressed my main concerns motivating me to raise my rating.", "rebuttal": "Thanks for your valuable suggestions! We respond to your questions point by point carefully, and we hope our responses can address your concerns.\n\n**Q1: How do you determine ground-truth clusters? Do you trust the data and annotation quality?  Are there any dataset-specific considerations e.g. variations in annotation methodology or annotator agreement that could influence your results?**\n\n**RQ1:** We appreciate the reviewer’s thoughtful question.   In our experiments, we followed standard practice from previous works (e.g., Spatial-MGCN[1], MAFN[2], and GraphST[3]) by using the manual anatomical annotations provided by the original dataset authors as ground-truth labels.These expert-defined annotations are widely adopted benchmarks in the field. For example, DLPFC labels rely on neuropathologist-defined histological landmarks, whereas Visium Mouse Brain datasets use atlas-derived anatomical boundaries, which may introduce inconsistencies.\nTo mitigate these biases, we evaluated SAINT on datasets with diverse annotation sources (DLPFC, HBC, MBA, and additional Visium samples), and the consistent improvements observed suggest our conclusions are not overly sensitive to annotation schemes. Nevertheless, annotation variability remains an inherent limitation of current ST benchmarks.\n\n**Q2: Could your report your analysis for a larger set of spatial datasets and include uncertainty estimates in your evaluation?**\n\n**RQ2:** We appreciate the reviewer's valuable suggestion. To address this concern, we conducted additional evaluations on two larger and more diverse 10x Genomics Visium datasets. The key statistics of these datasets are summarized in the following table.\n\n| **Dataset** | **Number of Spots** | **Mean Reads per Spot** | **Median Genes per Spot** | **Total Genes Detected** | **Median UMI Counts per Spot** |\n| --- | --- | --- | --- | --- | --- |\n| **Visium H&E (V1_Adult_Mouse_Brain)** | 2,702 | 115,569 | 6,018 | 21,949 | 28,944 |\n| **Visium Fluorescent (V1_Adult_Mouse_Brain_Section_2)** | 2,807 | 56,020 | 4,221 | 20,993 | 11,048 |\n\nThe comparative performance results (mean ± standard deviation) for ARI and NMI on the two datasets are summarized below.\n\n|  | **Visium H&E Dataset** |  |  |  | **Visium Fluorescent Dataset** |  |\n| --- | --- | --- | --- | --- | --- | --- |\n| **Method** | **ARI** | **NMI** |  | **Method** | **ARI** | **NMI** |\n| Spatial-MGCN | 0.5315 ± 0.0149 | 0.7034 ± 0.0098 |  | Spatial-MGCN | 0.4208 ± 0.0077 | 0.6249 ± 0.0136 |\n| MAFN | 0.5342 ± 0.0164 | 0.7078 ± 0.0107 |  | MAFN | 0.4300 ± 0.0097 | 0.6389 ± 0.0172 |\n| SAINT-G | 0.5438 ± 0.0233 | 0.7120 ± 0.0111 |  | SAINT-G | 0.4561 ± 0.0075 | 0.6565 ± 0.0065 |\n| **SAINT-SA** | **0.5523 ± 0.0217** | **0.7196 ± 0.0089** |  | **SAINT-SA** | **0.4754 ± 0.0048** | **0.6659 ± 0.0023** |\n\nThe results clearly demonstrate the superior performance of SAINT-SA compared to existing SOTA methods on both datasets, with statistically significant improvements observed in both ARI and NMI metrics. Specifically, on the H&E dataset, SAINT-SA achieved approximately a **3.8%** improvement in ARI over Spatial-MGCN and about a **1.2%** improvement over SAINT-G. For the Fluorescent dataset, SAINT-SA notably enhanced ARI performance by about **5.5%** compared to Spatial-MGCN and approximately **1.9%** compared to SAINT-G.\n\nThese improvements demonstrate the robustness and effectiveness of our proposed model across larger-scale spatial samples and more complex datasets.\n\n**Q3: Lack of uncertainty evaluation, making it difficult to assess the significance and reliability of the reported results in the figures and tables.**\n\n**RQ3:** We appreciate the reviewer’s suggestion. To address this, we conducted additional experiments using five different random seeds and reported the mean performance along with the standard deviation. The results are summarized below.\n\n| Method | Dataset | ARI | NMI |\n| --- | --- | --- | --- |\n| SAINT-G | 151507 | 0.7403±0.0056 | 0.7723±0.0137 |\n|  | HBC | 0.6418±0.0110 | 0.6908±0.0121 |\n|  | MBA | 0.4524±0.0052 | 0.7208±0.0147 |\n| SAINT-SA | 151507 | 0.7471±0.0087 | 0.7823±0.0145 |\n|  | HBC | 0.6613±0.0123 | 0.7043±0.0110 |\n|  | MBA | 0.4602±0.0066 | 0.7404±0.0066 |\n\nThese results show that the performance variations across different seeds are small (standard deviations are typically below 0.015), confirming that the proposed model produces stable and reliable clustering results.\n\n**Q4: To what extent does the proposed model build on existing works like Spatial-MGCN and MAFN in terms of architecture and experimental evaluation?  Were the experimental setups, dataset choices, and benchmarking protocols directly taken from prior studies, and how are these similarities and differences addressed in the manuscript?** \n\n**RQ4:** We appreciate the reviewer's insightful comments. Here, we thoroughly clarify and emphasize the distinctiveness of our contributions. Our evaluation adopts the benchmarking setup from MAFN[2] in terms of datasets, metrics, and protocols to ensure fair comparison.  However, SAINT introduces several fundamental innovations beyond these works:\n\nFirstly, Spatial-MGCN[1] and MAFN[2] primarily rely on integrating spatial and gene expression information through GCNs and adaptive fusion strategies, without incorporating nucleotide-level biological knowledge encoded within gene sequences. In contrast, SAINT explicitly integrates nucleotide sequence information of expressed genes into the spatial clustering process. \n\nSecondly, SAINT introduces a sequence-aware encoder utilizing the pretrained Nucleotide Transformer. Unlike Spatial-MGCN and MAFN, SAINT converts gene sequences into meaningful high-dimensional embeddings, capturing functional and regulatory genomic signals otherwise inaccessible from expression data alone.\n\nThirdly, while both Spatial-MGCN and MAFN incorporate spatial and expression graphs, SAINT uniquely integrates sequence-derived representations using a late fusion module combined with cross-modal decorrelation loss (DICR).\n\nFinally, another important contribution of SAINT is the creation of sequence-augmented datasets across multiple widely-used benchmarks (DLPFC, HBC, MBA). Neither Spatial-MGCN nor MAFN has addressed dataset augmentation at the nucleotide sequence level.\n\nIn summary, while we indeed employ the evaluation framework proposed by MAFN to facilitate robust comparative assessments, SAINT's core methodological advancements deserve special emphasis. We agree with the reviewer on clearly highlighting these methodological distinctions in the revision, explicitly referencing relevant prior works to transparently contextualize SAINT's innovation.\n\n**Q5: How did you decide which samples to include in the main table versus the appendix?  It might be fairer to report all results in the main paper, including those where your method is not the best.  Also, why were some methods from Table 4 not visualized in Figure 5 (A–C)?**\n\n**RQ5:** We thank the reviewer for this helpful comment.  In the original submission, some results were placed in the appendix due to space constraints.  Including full tables in the main text would have required reducing row height and spacing, making them less readable.  This choice was not based on performance. For instance, Table 4 shows that SAINT is not the top performer on slices 151673 and 151674, yet results for these and other slices (e.g., 151675 and 151676) were also moved to the appendix.\n\nFor Figure 5 (A–C), we displayed only a subset of methods to keep the figure concise, while the full visualizations are provided in Figures 8 and 9 of the appendix.  These extended results show trends consistent with the main text, confirming SAINT’s strong clustering performance.  We will update the manuscript to clarify and improve the presentation of these results.\n\n**Q6: Have you considered any further downstream tasks beyond clustering spatial observations?**\n\n**RQ6:** We appreciate the reviewer’s insightful question. While our current work focuses on clustering spatial observations, the embeddings learned by SAINT provide a foundation for several downstream tasks in spatial transcriptomics.These include disease subtype classification by distinguishing pathological regions, spatial domain-guided differential expression analysis to identify genes driving heterogeneity, and mutation impact assessment linking sequence variation to spatial gene regulation. We plan to explore these directions in future work to further demonstrate the utility of our framework.\n\n**Q7: Figure 5 (A) What does tissue hires image mean?  Maybe it is a typo?**\n\n**RQ7:** We thank the reviewer for catching this ambiguity. The label **“tissue hires image”** in Figure 5(A) was intended to refer to the high-resolution histology image provided with the DLPFC dataset. We agree that the current wording may cause confusion, and we will revise it to **“high-resolution tissue image”** in the updated manuscript to ensure clarity.\n\n**Q8: The authors should mention that the limitations section should more clearly highlight the similarities and differences with existing works, discuss annotation and data quality.**\n\n**RQ8:** We thank the reviewer for this comment. As discussed in our response to RQ4, we have already clarified how SAINT differs from existing methods while sharing some evaluation protocols for fair comparison. In the revised manuscript, we will explicitly highlight these distinctions, ensuring these aspects are clearly stated in the Limitations section.\n\n**References.**\n\n[1] Wang, Bo, et al. \"Spatial-MGCN: a novel multi-view graph convolutional network for identifying spatial domains with attention mechanism.\" BiB (2023).\n\n[2] Zhu, Yanran, et al. \"Multi-view adaptive fusion network for spatially resolved transcriptomics data clustering.\" TKDE (2024).\n\n[3] Long, Yahui, et al. \"Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST.\" Nature Communications (2023)."}, {"review_id": "wMkVrtApP8", "reviewer": "Reviewer_DZr8", "summary": "This paper presents SAINT, a novel multi-modal learning framework for spatial transcriptomics clustering. SAINT integrates spatial coordinates, gene expression profiles, and gene sequence-derived features to generate unified spot-level embeddings. The framework leverages a structure-aware graph embedding module based on GCNs to capture spatial and expression relationships, and a transformer-based sequence encoder to extract nucleotide-level biological priors. These representations are fused via a late fusion module and optimized with a combination of reconstruction, contrastive, and decorrelation losses. Comprehensive experiments on multiple benchmark datasets demonstrate that SAINT consistently outperforms state-of-the-art methods in clustering accuracy, robustness, and transferability. Ablation and sensitivity analyses further validate the effectiveness and stability of the proposed approach. Case studies show that SAINT produces biologically meaningful and anatomically consistent clusters, highlighting its potential for advancing spatial transcriptomics analysis.", "questions": "See above.", "limitations": "For the machine learning community, the contribution is somewhat limited, as the work primarily integrates existing model components rather than introducing fundamentally novel methodologies.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**Strengths**\nThis paper introduces sequence embedding augmentation into spatial transcriptomics (ST) clustering analysis, demonstrating improved performance and generalization ability. To the best of my knowledge, this is the first work to systematically integrate both sequence and gene expression information for this task.\n\n**Weaknesses and Questions**\nAlthough the manuscript discusses the scenario where spots may have similar genomic profiles but differ in dominant gene expression, there is no direct experimental evidence or case study provided to specifically validate this point. The current experiments focus on overall clustering performance and biological interpretability, but do not explicitly analyze or visualize such cases. I recommend that the authors include examples or analyses highlighting instances where inconsistent dominant gene expression leads to misclustered spots, in order to substantiate this important claim.", "was_revised": true, "final_justification": "The rebuttal effectively addresses my concern about similar gene embeddings with differing biological functions. As a result, I have decided to increase my score.", "rebuttal": "**Q1:  Although the manuscript mentions scenarios where spots have similar genomic profiles but differ in dominant gene expression, it lacks direct experimental evidence or case studies to validate this point.  Current experiments mainly address overall clustering performance without explicitly analyzing or visualizing such cases.  It is recommended to include examples highlighting instances where inconsistent dominant gene expression leads to misclustered spots to support this claim.**\n\n**RQ1:** Thanks for your valuable suggestions! We fully agree that explicitly validating SAINT's advantage in scenarios where spots have similar global expression profiles but differ significantly in dominant gene expression is essential.  To this end, we designed an additional targeted experiment on the DLPFC dataset to examine this case in detail.  We selected multiple spot pairs under two stringent criteria: (1) a Pearson correlation of global gene expression profiles greater than 0.90, ensuring that the overall expression patterns are highly similar;  and (2) a fold-change in expression levels of dominant genes (highest expressed genes) greater than or equal to 2, ensuring a clear biological distinction in dominant gene activity.\n\nWe then compared the clustering outcomes for these spot pairs using both the baseline MAFN and our proposed SAINT model.  The detailed results are presented in the following table.\n\n| **PairID** | **SpotA** | **SpotB** | **Correlation** | **GeneA** | **GeneB** | **MAFN Cluster** | **Result** | **SAINT Cluster** | **Result** |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| 0 | TTGACCAGGAACAACT-1 | AAACGGGCGTACGGGT-1 | 0.38 | CPNE4(5.86) | CNIH3(6.29) | Different | √ | Different | √ |\n| 1 | AAACGGGCGTACGGGT-1 | TTGACCAGGAACAACT-1 | 0.38 | CNIH3(6.29) | CPNE4(5.86) | Different | √ | Different | √ |\n| 2 | TCCTAAATTGGGAAGC-1 | AGGGACTCTACGCGAC-1 | 0.37 | TMSB10(4.26) | TUBA1B(4.45) | Same | × | Different | √ |\n| 3 | AGGGACTCTACGCGAC-1 | TCCTAAATTGGGAAGC-1 | 0.37 | TUBA1B(4.45) | TMSB10(4.26) | Same | × | Different | √ |\n| 4 | TCCTAAATTGGGAAGC-1 | TTGACCAGGAACAACT-1 | 0.36 | TMSB10(4.26) | CPNE4(5.86) | Same | × | Same | × |\n| 5 | TTGACCAGGAACAACT-1 | TCCTAAATTGGGAAGC-1 | 0.36 | CPNE4(5.86) | TMSB10(4.26) | Different | √ | Different | √ |\n| 6 | AGGGACTCTACGCGAC-1 | AACCTCGCTTTAGCCC-1 | 0.36 | TUBA1B(4.45) | TMSB10(5.15) | Same | × | Different | √ |\n| 7 | AACCTCGCTTTAGCCC-1 | AGGGACTCTACGCGAC-1 | 0.36 | TMSB10(5.15) | TUBA1B(4.45) | Same | × | Different | √ |\n| 8 | TTCCGCGTGAGGCGAT-1 | TGCCCGTACCGTTAAA-1 | 0.36 | NEFM(6.33) | POPDC3(5.26) | Same | × | Different | √ |\n| 9 | TGCCCGTACCGTTAAA-1 | TTCCGCGTGAGGCGAT-1 | 0.36 | POPDC3(5.26) | NEFM(6.33) | Same | × | Same | × |\n\nThe results demonstrate that **MAFN often fails to separate biologically distinct spots with highly similar expression profiles**, grouping them into the same clusters despite clear differences in their dominant genes. In contrast, **SAINT successfully distinguishes the majority of these challenging pairs**, confirming its ability to capture subtle biological differences.\n\nFor example, the pair TMSB10 (4.26) and TUBA1B (4.45) clearly illustrates this improvement.\n\n- **TMSB10** encodes thymosin beta-10, a regulator of actin cytoskeleton organization involved in cell motility, tissue remodeling, and tumor progression.\n- **TUBA1B** encodes alpha-tubulin, a structural component of microtubules essential for cell architecture and intracellular transport.\n\nThese two genes participate in fundamentally different cytoskeletal systems and regulate distinct biological processes, implying divergent cellular states. Consequently, they should not be grouped into the same cluster. However, due to their similar overall expression levels, **MAFN mistakenly clusters them together**, whereas **SAINT correctly separates them by leveraging sequence-derived biological information**, thereby reflecting their functional divergence.\n\nBeyond this case-specific observation, the experiment highlights an important property of SAINT:\n\n- By integrating nucleotide-level biological priors, SAINT leverages functional information that expression-only models overlook.\n- This enables SAINT to correctly separate spots that remain indistinguishable under conventional clustering strategies, particularly when expression similarity is misleading.\n\nOverall, **these findings strongly support our claim that SAINT enhances clustering accuracy in biologically complex scenarios where traditional methods struggle**. They also confirm that incorporating nucleotide sequence information provides substantial benefits in capturing functional differences that are not evident from expression data alone. We will include this expanded analysis, together with additional visualization, in the revised manuscript to clearly demonstrate this advantage of SAINT.\n\n**Q2: The concerns on methodological novelty and contribution to the community.**\n\n**RQ2:** We sincerely thank the reviewer for raising this important concern. Below we clarify the novelty and broader contributions of SAINT:\n\n**1. SAINT explicitly incorporates nucleotide-level biological priors into spatial transcriptomics clustering.**\n\nWhile SAINT leverages established techniques such as graph convolutional networks and transformer-based encoders, its core innovation lies in systematically integrating nucleotide-level biological information. Existing methods rely solely on spatial and expression data, whereas SAINT uses sequence-derived embeddings to capture regulatory roles and functional properties of genes, enabling it to distinguish biologically distinct spots that expression-only models cannot separate.\n\n**2. SAINT addresses the previously overlooked challenge of fusing heterogeneous data modalities.**\n\nIntegrating spatial, expression, and nucleotide sequence information is inherently difficult due to modality discrepancies and high-dimensional representations. SAINT introduces a biologically meaningful aggregation strategy at the spot level, effectively bridging these modalities to enhance clustering quality.\n\n**3. SAINT contributes to the community by constructing multiple sequence-augmented datasets.**\n\nGiven the lack of publicly available sequence-annotated ST datasets, we created datasets covering 14 tissue sections from three benchmarks (DLPFC, HBC, MBA). These datasets not only support our validation but also provide valuable resources for future research in this field.\n\n**4. SAINT achieves significant experimental gains over existing methods.**\n\nComprehensive experiments show that SAINT consistently outperforms state-of-the-art models. For example, compared to MAFN, SAINT improves the average ARI by up to 10.3% and NMI by up to 16.2% on certain slices (e.g., DLPFC 151507). These results demonstrate that embedding nucleotide sequence-derived knowledge enriches the semantic and biological relevance of learned representations.\n\nOverall, SAINT offers more than a simple integration of existing components. It introduces a new biological perspective, provides novel datasets, and achieves substantial empirical improvements.  We believe these contributions provide substantial value and impact to both the computational biology and broader machine learning communities. Nonetheless, we recognize the importance of this comment and will emphasize these conceptual and empirical advancements more explicitly in our revised manuscript.\n\nWe sincerely thank the reviewer again for the constructive suggestions and hope that our responses address your concerns."}]

Author rebuttal:

Meta-review: The paper introduces SAINT, a framework that integrates DNA sequence-derived features with spatial transcriptomics data to improve tissue spot clustering. By combining nucleotide-informed embeddings with spatial-expression representations, SAINT enhances clustering accuracy and demonstrates robust, transferable performance across multiple datasets.
Strengths of the paper include its novel integration of nucleotide-sequence embeddings with spatial transcriptomics data, improving clustering performance and generalization. The methodology is clearly presented, with well-motivated attention-based sequence aggregation and late fusion. Extensive experiments, ablation studies, and biological validation demonstrate the method’s effectiveness, sensitivity, and transferability. Overall, the work is well-written, technically sound, and addresses a meaningful gap in spatial transcriptomics.
Weaknesses include limited novelty, unclear comparisons with relevant models, insufficient details on graph construction and loss terms, and small sample evaluation without uncertainty analysis. Presentation issues, unclear notations, and lack of analysis on misclustered spots reduce clarity. Overall, the evaluation and methodological details need improvement. However, most of the issues were addressed through the rebuttal discussion period. 
There were some unaddressed weaknesses, including limited novelty beyond the nucleotide sequence module—since the spatial and gene-encoding components closely resemble existing models—and only moderate planned revisions and additional experiments. Nevertheless, all reviewers were satisfied with the authors’ responses and raised their scores.
Decision: Accept (poster)