Biology subjects

Zhou, X. M.

Publications and source records attributed to Zhou, X. M..

At least 19 recordsLinked to original sources

bioRxiv2026

OncoSpot enables benchmarking of cancer spatial transcriptomics methods across tumor evolutionary modes

Spatial transcriptomics (ST) is increasingly used to map clonal architecture within tumors, yet methods for analyzing cancer ST data are difficult to evaluate because real tumors lack ground truth for copy-number alterations (CNAs), subclonal identity, and malignant cell fraction. Existing ST simulators model normal tissue and do not represent tumor evolution, CNA-driven expression, or the spatial organization of malignant subclones. We present OncoSpot, a cancer-aware simulator of Visium-like spot-based ST data. OncoSpot combines a spatial evolutionary scaffold from DEMON, CNAs generated along the resulting clonal phylogeny with CNAsim, cancer-type-specific malignant transcriptional programs, and spatially clustered nonmalignant cell types, and aggregates cells into spots with known malignant fraction, subclone, and copy-number profiles. We demonstrate OncoSpot on high-grade serous ovarian carcinoma, colorectal cancer, and hepatocellular carcinoma, which represent three distinct modes of tumor evolution. Simulated subclones were spatially clustered and showed stronger phylogeny-space concordance than those generated by scCube under matched subclone populations. Benchmarking CopyKAT, CancerFinder, Clonalscope, and RCTD revealed cancer-dependent performance: Clonalscope recovered copy-number signals more accurately in ovarian and colorectal cancer, whereas CopyKAT performed better in hepatocellular carcinoma, reflecting both the assumptions of each method and the copy-number landscapes of the three cancers. OncoSpot's ground truth also exposed a failure mode in Clonalscope's malignant spot identification, in which tumor and normal labels were inverted, and allowed us to trace its cause. These method-dependent behaviors would be difficult to detect in real data. OncoSpot provides a framework for benchmarking CNA inference, subclone detection, malignant spot identification, and normal spot deconvolution, and its modular design and guidelines allow extension to additional cancer types. OncoSpot is freely available at https://github.com/compbio-mallory/OncoSpot.

bioinformatics↗

bioRxiv2026

A Latent Inflammatory Tissue-State Variable Mechanistically Links Radiotherapy-Induced Immune Remodeling to Recurrent Tumor Permissiveness

Triple-negative breast cancer recurrence following radiotherapy is associated with a microenvironment characterized by immune dysfunction and persistent inflammation. We developed an experimentally constrained agent-based model to investigate how transient immune remodeling becomes a persistent recurrence-permissive tissue state. The model reproduced experimentally observed macrophage recruitment and phenotype dynamics but demonstrated that recurrent recruitment, impaired inflammatory resolution, and adaptive immune bias were insufficient to reproduce the recurrent macrophage ecology. We therefore introduced recurrence-associated microenvironmental inflammation (RAMI), a latent tissue-state variable representing accumulated unresolved inflammatory remodeling. Coupling RAMI to the emergence of experimentally constrained interleukin-6 signaling generated tissue-to-cell feedback that reinforced recurrence-associated macrophage phenotypes and increased tumor establishment, supporting inflammatory tissue memory as a mechanistic intermediary between transient immune perturbation and persistent recurrent tumor permissiveness.

systems biology↗

bioRxiv2026

CNVeil resolves haplotype-specific copy number and uncovers subclonal architecture hidden from total copy number profiling in single-cell cancergenomes

Single-cell DNA sequencing (scDNA-seq) resolves copy number variation (CNV) at single-cell resolution, revealing tumor heterogeneity and subclonal structure. Most existing methods, however, infer only total copy number. Haplotype-resolved copy number, which captures allelic imbalance and clonal evolution, remains far less developed, largely because low coverage, allelic dropout, and technical noise in scDNA-seq make phased allelic inference substantially harder than total copy number estimation. We present CNVeil, a haplotype-aware framework that infers total, allele-specific, and chromosome-scale haplotype-resolved copy number from scDNA-seq data. CNVeil first builds robust total copy number profiles through highly variable bin selection, hierarchical clustering, subclone-aware ploidy estimation, and cross-cell consensus segmentation. Using this profile as a stable scaffold, it infers allele-specific copy number with an expectation-maximization algorithm applied to heterozygous SNP allele counts, then reconstructs haplotype-specific copy number by enforcing coherent haplotype orientation across adjacent segments via dynamic programming. We benchmarked CNVeil against 12 state-of-the-art methods, including eight total copy number callers, two allele-specific callers, and two haplotype-resolved callers, across 20 simulated and real datasets spanning six experimental settings, including high-multiplexed single-nucleus sequencing, Acoustic Cell Tagmentation (ACT), and 10x Chromium. This constitutes the largest comparative evaluation of single-cell copy number inference methods to date. CNVeil consistently outperformed existing tools in segmentation accuracy, ploidy inference, subclone identification, and allele-specific copy number estimation. In a breast cancer multi-omics (wellDR-seq) cohort, CNVeil uncovered haplotype-specific subclonal diversification invisible to total copy number analysis alone and linked allele-specific copy number states to transcriptional variation. By transforming sparse single-cell allelic signals into chromosome-scale haplotype-resolved profiles, CNVeil closes a major methodological gap and provides a scalable framework for studying tumor evolution and functional genomic heterogeneity at single-cell resolution.

bioinformatics↗

bioRxiv2026

Multi-modality Graph Representation Learning for Malignant Cell Identification from scRNA-seq using DeepMalignant

Distinguishing malignant from normal cells in single-cell RNA sequencing data remains a critical yet challenging task in cancer genomics. Existing methods often suffer from poor precision, limited generalizability across cancer types, and reduced robustness across different sequencing platforms. We developed DeepMalignant, an unsupervised multimodal graph attention autoencoder for malignant cell identification that jointly integrates gene expression and copy number alteration (CNA) information. We applied DeepMalignant to five datasets covering 26 samples and four cancer types (breast, colorectal, pancreatic, and ovarian cancers), generated by three platforms (10x Genomics, inDrop, and Drop-seq) for benchmarking and compared it with existing state-of-the-art methods including scMalignantFinder, PreCanCell, CopyKAT, ikarus, and Cancer-Finder. DeepMalignant achieved the best overall balance of precision and recall and consistently outperformed the existing methods that used either gene expression or CNA in F1 scores. Ablation studies showed that both CNA-based edge weighting and graph attention aggregation contribute independently to performance, and attribution analysis further indicated that the learned embeddings capture biologically meaningful malignant programs. We further applied DeepMalignant to two ductal carcinoma in situ (DCIS) samples, DCIS2 and DCIS1, that have matched spatial transcriptomics and scRNA-seq data. DeepMalignant identified tumor-enriched regions that were highly consistent with the matched histological image. The downstream cellcell communications analysis revealed that fibroblast-derived C3 and MIF both directed signaling more toward normal epithelial cells than tumor epithelial cells, demonstrating that accurate tumor-normal cell classification by DeepMalignant enables biologically meaningful interrogation of the tumor microenvironment and revealing how stromal cells differentially communicate with malignant versus normal epithelial populations.

bioinformatics↗

bioRxiv2026

Benchmarking cell type annotation in spatial transcriptomics: resolving cellular hierarchies, biological fidelity, and dynamic cell states

Spatial transcriptomics enables the quantification of gene expression within its native tissue context, providing unprecedented insight into tissue architecture, cellular ecosystems, and local cell-cell interactions at regional and single-cell resolution. Accurate cell type annotation is a critical prerequisite for interpreting these data and is often the first and most essential step in downstream analysis. Despite rapid advances in computational methods, cell type annotation remains challenging and frequently requires extensive expert-driven manual curation based on marker-gene expression, spatial context, and prior biological knowledge. While early approaches relied primarily on transcriptional similarity, newer methods increasingly incorporate spatial information, histological features, and multimodal data to improve annotation accuracy. Nevertheless, reliable annotation remains difficult when biological interpretation requires fine-grained subtype resolution, particularly for platforms with limited gene panels, tissues undergoing dynamic cellular state transitions, and studies in which reference and query datasets differ substantially in biological context or technical modality. Here, we present a systematic benchmark of 20 state-of-the-art cell type annotation methods across four spatial transcriptomics datasets spanning diverse technologies, experimental conditions, cell numbers, and gene panel sizes. Importantly, all benchmark datasets contain expert-curated cell type labels, including wellresolved cell populations and subtype annotations, providing high-quality biological ground truth for evaluation. The benchmark encompasses both reference-based and reference-free methods representing a broad range of computational frameworks. Performance was assessed using conventional classification metrics, including accuracy and F1-based measures, together with structure-aware metrics that evaluate both cell-level annotation accuracy and preservation of higher-order biological organization. Across datasets, annotation performance varied substantially according to tissue context, reference-query similarity, and annotation granularity. Fine-grained subtype annotation and recovery of rare cell populations remained challenging for many methods, particularly in datasets capturing injury, repair, developmental, and regenerative processes characterized by continuous cellular state transitions. Notably, high classification accuracy did not necessarily correspond to preservation of global cellular relationships or biologically coherent downstream pathway and gene-set enrichment analyses. Overall, scANVI, Seurat, and TACCO consistently ranked among the top-performing methods, although their relative advantages were context dependent. Together, our results provide a comprehensive assessment of current annotation strategies for spatial transcriptomics and offer practical guidance for selecting methods that best align with specific biological questions, dataset characteristics, and analytical priorities.

bioinformatics↗

bioRxiv2026

Trajectories of Response Inhibition Development in Adolescence

Adolescence is a pivotal developmental period during which cognitive control and the ability to suppress maladaptive impulses undergo profound refinement. Despite extensive behavioral and imaging studies in humans, the neural circuit mechanisms that drive the maturation of inhibitory control remain poorly understood. To address this gap, we performed a longitudinal multimodal study in macaque monkeys spanning the transition from puberty to adulthood, combining behavioral measurements, prefrontal neurophysiology, and structural neuroimaging within the same individuals. Behavioral performance in multiple variants of the antisaccade task improved dramatically across adolescence. In parallel, prefrontal cortical neurons exhibited robust developmental increases in task-related firing, particularly around the generation of goal-directed saccades, indicating maturation of neural representations supporting inhibitory control. Strikingly, trajectories of behavioral improvement and neural activity maturation were tightly predicted by the development of long-range white matter pathways linking frontal cortex with distributed brain networks. These findings identify white matter development as a key systems-level mechanism underlying the emergence of mature inhibitory control and prefrontal neural dynamics and establish a primate framework for understanding developmental vulnerabilities associated with neuropsychiatric disorders that emerge in adolescence and early adulthood.

neuroscience↗

bioRxiv2026

Spatial transcriptomics reveals organizational properties of mouse spinal cord and alterations in neuropathic pain

The spinal cord integrates diverse somatosensory inputs and executes motor outputs through anatomically and functionally distinct circuits. Here, we employed spatially resolved single-cell transcriptomic profiling of the adult mouse spinal cord to gain insights into the organizational logic of the spinal cord. Our results reveal distinct spatial and laminar distributions of neuronal subtypes, including axial level and sex-specific differences. Many neuronal subtypes exhibit close spatial proximity, implicating regionally specific patterns of connectivity and circuit functions. Additionally, neuronal subtypes within the dorsal horn exhibit a wide range of predicted cell-cell communication motifs, as assessed by the spatial distribution of neuropeptide-and other ligand-receptor pairs. Finally, we identified several neuronal subtypes with altered transcriptomic and predicted cell-cell communications in a model of neuropathic pain. This spatially resolved cellular and molecular map of the spinal cord will facilitate the decoding of circuit mechanisms underlying somatosensory and motor functions in health and disease.

neuroscience↗

bioRxiv2025

scCotag: Diagonal integration of single-cell multi-omics data via prior-informed co-optimal transport and regularized barycentric mapping

Recent advances in high-throughput single-cell technologies have enabled characterization of cellular states across distinct omics layers, yielding complementary insights into the organization of biological systems. To jointly leverage these disparate modalities, computational methods have been developed to align non-overlapping cell populations profiled in different omics layers, a task known as diagonal integration, thereby facilitating more fine-grained biological interpretations. However, existing integration approaches unrealistically assume that all cells are alignable and treat prior-knowledge-derived feature correspondences as fully reliable, retaining them unrefined throughout the alignment process. In this work, we introduce scCotag, a co-optimal transport (COOT)-based deep learning frame-work for diagonal integration of single-cell multi-omics data, to address these limitations. scCotag first infers cell alignment and feature correspondence with prior-informed COOT in an iterative manner, and then leverages the resulting transport plans to jointly learn cell and feature embeddings via regularized barycentric mapping and graph reweighting. Systematic benchmarking on human brain, bone marrow, and blood single-cell RNA-seq and ATAC-seq datasets demonstrates the overall superior performance of scCotag over state-of-the-art methods in both cell alignment accuracy and embedding accuracy. In particular, scCotag excels in simulated imbalance scenarios where not all cells are alignable. Applying scCotag to data in postmortem brains of Alzheimers disease (AD) and non-AD (NoAD) patients further yields more fine-grained biological interpretation of regulatory mechanisms. Together, these results demonstrate that scCotag provides a robust framework for single-cell diagonal integration and regulatory inference.

bioinformatics↗

bioRxiv2025

From features to slice: parameter-cloud modeling of spatial transcriptomics for simulation and 3D interpolatory augmentation

Computational simulation and data augmentation of spatial transcriptomics (ST) are essential for quantitative benchmarking, reproducibility, and methodological innovation. Yet current models often lack flexibility in controlling spatial and transcriptional heterogeneity, fail to capture higher-order gene dependencies, and rarely extend to three-dimensional or alignment-aware contexts. Here we present FEAST, a computational infrastructure that models ST data within a parameter cloud - a latent manifold encoding gene-level mean, variance, and sparsity. By sampling and perturbing this manifold, FEAST generates high-fidelity synthetic slices with tunable spatial and transcriptional variation, enabling systematic evaluation of clustering, deconvolution, and spatial alignment algorithms. Beyond two dimensions, FEAST performs 3D parameter-cloud interpolation guided by optimal transport and benchmark alignment, reconstructing continuous tissue architectures while preserving molecular coherence. Together, these capabilities establish FEAST as a foundational platform for standardized benchmarking, data augmentation, and 3D reconstruction in spatial transcriptomics. The source code and tutorials for FEAST are publicly available at https://github.com/maiziezhoulab/FEAST, and can be installed via Pypi at https://pypi.org/project/FEAST-py/.

bioinformatics↗

bioRxiv2025

BioLACE: unifying spatial geometry and marker priors for cohesive cell-type clustering in spatial transcriptomics

Spatial transcriptomics (ST) provides high-dimensional gene expression profiles together with spatial coordinates, enabling the reconstruction of tissue architecture at cellular resolution. While recent graph-based deep learning methods have advanced spatial clustering in ST, many rely on complex architectures that obscure interpretability and rarely integrate biological priors such as marker gene information. We introduce BioLACE, a scalable framework that unifies spatial structure, transcriptomic variation, and curated marker gene profiles within a shared Variational Autoencoder (VAE) latent space. BioLACEjointly optimizes three complementary objectives: (1) a VAE reconstruction loss to preserve transcriptional structure, (2) a graph Laplacian regularizer to enforce spatial smoothness, and (3) a temperature-scaled InfoNCE contrastive loss guided by marker-informed similarities. Applied to MERFISH hypothalamus, mouse spinal cord, and Slide-seq mouse cerebellum datasets, BioLACE achieves superior cell type clustering accuracy, well-defined biologically consistent boundaries, and interpretable latent representations, highlighting its generality and scalability for modern ST analysis. The source code and tutorials for BioLACE are publicly available at https://github.com/maiziezhoulab/BioLACE.

genomics↗

bioRxiv2025

Interpretable spatial multi-omics data integration and dimension reduction with SpaMV

Spatial multi-omics technologies have revolutionized our understanding of biological systems by providing spatially resolved molecular profiles from multiple perspectives. Existing spatial multi-omics integration methods often assume that data from different modalities share a common underlying distribution, aiming to project them into a single unified latent space. This assumption, however, can obscure the unique insights offered by each modality, thereby limiting the full potential of multi-omics analyses. To address this limitation, we present the Spatial Multi-View (SpaMV) representation learning algorithm, which captures both the shared information across modalities and the distinct, modality-specific information, enabling a more comprehensive and interpretable representation of spatial multi-omics data. Through extensive evaluation on both simulated and real-world datasets, SpaMV demonstrates superior spatial domain clustering performance and provides users with more interpretable dimension reduction for downstream analysis. Moreover, SpaMV effectively annotates cell types within clusters of a mouse thymus dataset, highlighting its effectiveness in interpretable dimensionality reduction.

bioinformatics↗

bioRxiv2025

Enhancing variant detection in complex genomes: leveraging linked reads for robust SNP, Indel, and structural variant analysis

Accurate detection of genetic variants, including single nucleotide polymorphisms (SNPs), small insertions and deletions (INDELs), and structural variants (SVs), is essential for comprehensive genomic analysis. While short-read sequencing performs well for SNP and INDEL detection, it remains limited in resolving SVs, particularly in complex genomic regions, due to its short read length. Linked-read sequencing technologies, such as single-tube Long Fragment Read (stLFR), partially address this limitation by incorporating molecular barcodes to provide long-range information. In this study, we evaluate conventional paired-end linked reads (PE100_stLFR) and explore a conceptual extension: long single-end barcoded reads of 500 bp (SE500_stLFR) and 1000 bp (SE1000_stLFR). We developed_stLFR-sim, a Python-based simulator that reproduces the_stLFR workflow and enables realistic benchmarking. Using a high-quality T2T assembly of HG002, we generated multiple datasets across 12 sequencing configurations. SVs were called using Aquila_stLFR (v2) and benchmarked against the Genome in a Bottle (GIAB) HG002 SV truth set with Truvari. We show that simulated PE100_stLFR closely matches real data, validating the simulation framework. Increasing read length consistently improves SV detection accuracy, with SE1000_stLFR achieving the best performance and approaching long-read methods while outperforming short-read and pangenome-based approaches. Collectively, our results highlight the strong potential of long single-end barcoded reads for improving SV detection, and suggest that even modest increases in read length, when combined with barcode information, can provide a cost-effective and practical strategy for enhancing future sequencing technologies and SV discovery.

bioinformatics↗

bioRxiv2025

stDyer-image improves clustering analysis of spatially resolved transcriptomics and proteomics with morphological images

Spatially resolved transcriptomics (SRT) and spatially resolved proteomics (SRP) data enable the study of gene expression and protein abundances within their precise spatial and cellular contexts in tissues. Certain SRT and SRP tech-nologies also capture corresponding morphology images, adding another layer of valuable information. However, few existing methods developed for SRT data effectively leverage these supplementary images to enhance clustering performance. Here, we introduce stDyer-image, an end-to-end deep learning framework designed for clustering for SRT and SRP datasets with images. Unlike existing methods that utilize images to complement gene expression data, stDyer-image directly links image features to cluster labels. This approach draws inspiration from pathologists, who can visually identify specific cell types or tumor regions from morphological images without relying on gene expression or protein abundances. Benchmarks against state-of-the-art tools demonstrate that stDyer-image achieves superior performance in clustering. Moreover, it is capable of handling large-scale datasets across diverse technologies, making it a versatile and powerful tool for spatial omics analysis.

bioinformatics↗

bioRxiv2025

SCGclust: Single Cell Graph clustering using graphautoencoders integrating SNVs and CNAs

Intra-tumor heterogeneity (ITH) is a compounding factor for cancer prognosis and treatment. Single-cell DNA sequencing (scDNA-seq) provides cellular resolution of the variations in a cell and has been widely used to study cancer progression and responses to drug and treatment. While the low coverage scDNA-seq technologies typically provides a large number of cells, accurate cell clustering is essential for effectively characterizing ITH. Existing cell clustering methods typically are based on either single nucleotide variations (SNV) or copy number alterations (CNA), without leveraging both signals together. Since both SNVs and CNAs are indicative of the cell subclonality, in this paper, we designed a robust cell clustering tool that integrates both signals using a graph autoencoder. Our model co-trains the graph autoencoder and a graph convolutional network (GCN) to guanrantee meaningful clustering results and to prevent all cells from collapsing into a single cluster. Given the low dimensional embedding generated by the autoencoder, we adopted a Gaussian Mixture Model to further cluster cells. We evaluated our method on eight simulated datasets and a real cancer sample. Our results demonstrate that our method consistently achieves higher V-measure scores compared to SBMClone, a SNV-based method, and a K-means method, which relies solely on CNA signals. These findings highlight the advantage of integrating both SNV and CNA signals within a graph autoencoder framework for accurate cell clustering. SCGclust is publicly available at https://github.com/compbio-mallory/cellClustering_GNN.

bioinformatics↗

bioRxiv2024

FocalSV: target region-based structural variant assembly and refinement using single-molecule long read sequencing data

Structural variants (SVs) play a critical role in shaping the diversity of the human genome and their detection holds significant potential for advancing precision medicine. Despite notable progress in single-molecule long-read sequencing technologies, accurately identifying SV breakpoints and resolving their sequence remains a major challenge. Current alignment-based tools often struggle with precise breakpoint detection and sequence characterization, while whole genome assembly-based methods are computationally demanding and less practical for targeted analyses. Neither approach is ideally suited for scenarios where regions of interest are predefined and require precise SV characterization. To address this gap, we introduce FocalSV, a target region assembly-based SV detection tool that combines the precision of assembly-based methods with the efficiency of region-specific approaches. FocalSV was evaluated on nine germline datasets and two paired normal-tumor cancer datasets, demonstrating superior performance in both precision and efficiency.

bioinformatics↗

bioRxiv2024

Brain structure and activity predicting cognitive maturation in adolescence

Cognitive abilities of primates, including humans, continue to improve through adolescence 1,2. While a range of changes in brain structure and connectivity have been documented 3,4, how they affect neuronal activity that ultimately determines performance of cognitive functions remains unknown. Here, we conducted a multilevel longitudinal study of monkey adolescent neurocognitive development. The developmental trajectory of neural activity in the prefrontal cortex accounted remarkably well for working memory improvements. While complex aspects of activity changed progressively during adolescence, such as the rotation of stimulus representation in multidimensional neuronal space, which has been implicated in cognitive flexibility, even simpler attributes, such as the baseline firing rate in the period preceding a stimulus appearance had predictive power over behavior. Unexpectedly, decreases in brain volume and thickness, which are widely thought to underlie cognitive changes in humans 5 did not predict well the trajectory of neural activity or cognitive performance changes. Whole brain cortical volume in particular, exhibited an increase and reached a local maximum in late adolescence, at a time of rapid behavioral improvement. Maturation of long-distance white matter tracts linking the frontal lobe with areas of the association cortex and subcortical regions best predicted changes in neuronal activity and behavior. Our results provide evidence that optimization of neural activity depending on widely distributed circuitry effects cognitive development in adolescence.

neuroscience↗

bioRxiv2024

Leveraging cross-source heterogeneity to improve the performance of bulk gene expression deconvolution

We introduce CSsingle, a novel method that enhances the decomposition of bulk and spatial transcriptomic (ST) data by addressing key challenges in cellular heterogeneity. CSsingle applies cell size correction using ERCC spike-in controls, enabling it to account for variations in RNA content between cell types and achieve accurate bulk data deconvolution. In addition, it enables fine-scale analysis for ST data, advancing our understanding of tissue architecture and cellular interactions, particularly in complex microenvironments. We provide a unified tool for integrating bulk and ST with scRNA-seq data, advancing the study of complex biological systems and disease processes. The benchmark results demonstrate that CSsingle outperforms existing methods in accuracy and robustness. Validation using more than 700 normal and diseased samples from gastroesophageal tissue reveals the predominant presence of mosaic columnar cells (MCCs), which exhibit a gastric and intestinal mosaic phenotype in Barretts esophagus and esophageal adenocarcinoma (EAC), in contrast to their very low detectable levels in esophageal squamous cell carcinoma and normal gastroesophageal tissue. We revealed a dynamic relationship between MCCs and squamous cells during immune checkpoint inhibitors (ICI)-based treatment in EAC patients, suggesting MCC expression signatures as predictive and prognostic markers of immunochemotherapy outcomes. Our findings reveal the critical role of MCC in the treatment of EAC and its potential as a biomarker to predict outcomes of immunochemotherapy, providing insight into tumor epithelial plasticity to guide personalized immunotherapeutic strategies.

bioinformatics↗

bioRxiv2024

Benchmarking clustering, alignment, and integration methods for spatial transcriptomics

Spatial transcriptomics (ST) is advancing our understanding of complex tissues and organisms. However, building a robust clustering algorithm to define spatially coherent regions in a single tissue slice, and aligning or integrating multiple tissue slices originating from diverse sources for essential downstream analyses remain challenging. Numerous clustering, alignment, and integration methods have been specifically designed for ST data by leveraging its spatial information. The absence of benchmark studies complicates the selection of methods and future method development. Here we systematically benchmark a variety of state-of-the-art algorithms with a wide range of real and simulated datasets of varying sizes, technologies, species, and complexity. Different experimental metrics and analyses, like adjusted rand index (ARI), uniform manifold approximation and projection (UMAP) visualization, layer-wise and spot-to-spot alignment accuracy, spatial coherence score (SCS), and 3D reconstruction, are meticulously designed to assess method performance as well as data quality. We analyze the strengths and weaknesses of each method using diverse quantitative and qualitative metrics. This analysis leads to a comprehensive recommendation that covers multiple aspects for users. The code used for evaluation is available on GitHub. Additionally, we provide jupyter notebook tutorials and documentation to facilitate the reproduction of all benchmarking results and to support the study of new methods and new datasets (https://benchmarkst-reproducibility.readthedocs.io/en/latest/).

bioinformatics↗