Biology subjects

Li, J. J.

Publications and source records attributed to Li, J. J..

At least 19 recordsLinked to original sources

bioRxiv2026

scStable: assessing scRNA-seq analysis stability under bulk-derived between-sample variation

Single-cell RNA sequencing (scRNA-seq) studies often profile only one or a few biological samples, limiting assessment of discovery stability under between-sample variation. We present scStable, a framework for assessing scRNA-seq analysis stability by generating synthetic samples that preserve cell-level heterogeneity from a single-sample reference while injecting bulk-derived between-sample variation. In gastric cancer data comprising 21 matched scRNA-seq and bulk RNA-seq profiles, scStable better recapitulates observed between-sample variation than ZINB-WaVE, subsampling, and Gaussian noise injection. Using synthetic sam-ples, scStable enables stability-aware analysis, including prioritization of stable differentially expressed genes and selection of clustering methods and hyperparameters.

bioinformatics↗

bioRxiv2026

IntegrateRigor: annotation-free integration optimization for cell identity recovery reveals cancer-immune interface niches

Integrating single-cell and spatial transcriptomics data across batches is essential for recovering comparable cell identities--including cell types, subtypes, and states--as a prerequisite for downstream analyses in multi-condition and large-scale studies. This task remains challenging because between-batch variation removal often conflicts with cell identity preservation, and current methods typically rely on generic highly variable gene selection and lack principled metrics for hyperparameter tuning when cell identity annotations are unavailable. Together, these limitations often lead to over-integration, which merges biologically distinct cell identities, or under-integration, which leaves cells separated by batch rather than identity. Here we introduce IntegrateRigor, a data-driven, annotation-free, method-agnostic framework that optimizes integration specifically for reliable cell identity recovery across batches. IntegrateRigor first selects genes whose expression patterns are stable across batches using a gene-wise likelihood-based batch stability score, excluding batch-sensitive genes that can bias cell identity alignment during integration. It then identifies the optimal integration configuration across methods and hyperparameters by defining a dataset-level integration score that explicitly balances between-batch variation removal against cell identity preservation, without requiring prior annotations. In a colorectal cancer single-cell and spatial transcriptomics dataset, IntegrateRigor revealed previously uncharacterized cancer-immune interface niches in the tumor microenvironment that were masked by under-integration under default settings and by over-integration in previous literature. Across diverse datasets spanning multiple sources of between-batch variation, IntegrateRigor consistently improved cell identity recovery by mitigating both over-integration and under-integration across five state-of-the-art integration methods. By transforming integration from a heuristic preprocessing step into a statistically principled, dataset-adaptive procedure for cell identity recovery, IntegrateRigor improves the reproducibility and biological discovery power of large-scale single-cell and spatial transcriptomics analyses.

bioinformatics↗

bioRxiv2026

Identifying Context-Specific Cell-Cell Interaction Genes Without Ligand-Receptor Databases from Spatial Transcriptomics

Current approaches to inferring cell-cell interactions (CCIs) are largely constrained by predefined ligand-receptor databases, particularly for low-resolution spatial transcriptomics (ST) platforms such as Visium. Due to the difficulties in accurately resolving interacting cells at coarse spatial resolution, other modes of interaction are often overlooked. Low-resolution ST data, however, can serve as an alternative to high-resolution ST, which suffers from low sensitivity, and to image-based ST, which is limited by restricted gene panels. Here, we present CellNeighborEX v2, a database-free framework that directly infers CCI-associated genes from ST data by detecting deviations between observed and expected gene expression at the spot-population level. These deviations are rigorously evaluated through a hybrid statistical framework involving permutation testing and are further refined by considering the abundance of interacting cell-type pairs. Compared with other conventional approaches relying on ligand-receptor databases, CellNeighborEX v2 can capture CCI genes from a broad spectrum of interactions, including both paracrine signaling and contact-dependent communication. Across datasets from hippocampus, liver cancer, colorectal cancer, ovarian cancer, and lymph node infection, CellNeighborEX v2 accurately recapitulated previously identified CCIs. Notably, it uniquely detected interactions absent from existing ligand-receptor databases, enabling detection of context-specific CCIs from Visium data. CellNeighborEX v2 is a tool that expands the analytical spectrum of Visium data and deepens our understanding of the molecular language of intercellular communication.

bioinformatics↗

bioRxiv2026

Information Geometry Reconciles Discrete and Continuous Variation in Single-Cell and Spatial Transcriptomic Analysis

Single-cell and spatial transcriptomics provide high-resolution cellular characterization, yet standard analytical approaches remain theoretically misaligned with the probabilistic nature of the data. After UMI normalization, current pipelines rely on Euclidean or log-transformed Euclidean distance for similarity measurement. Both are fundamentally ill-suited to model the multinomial count data. Euclidean distance in normalized space overemphasizes high-variance genes, while log-transformation inverts this bias but at the cost of distorting subtle, continuous expression modulations. Neither approach naturally captures the dual nature of gene expression: both discrete presence/absence transitions and continuous quantitative variation. To overcome these limitations, we introduce GAIA (Geometric Analysis from an Information Aspect), an information-geometric framework for cell representation learning and inter-cell similarity measurement. By anchoring analysis in the true probabilistic model, treating cells as multinomial distributions over genes and projecting cells to a statistical manifold, GAIA organically reconciles both the presence/absence effect and the more continuous expression modulations. Mathematically, GAIA exploits the equivalence between Fisher-Rao distance in multinomial space and geodesic distance on the unit hypersphere, a property that enables both theoretical guarantees and computational efficiency. Experiments in synthetic and real scRNA-seq and spatial transcriptomic datasets demonstrate that GAIA preserves robust and consistent cell-to-cell relationships, delineates biologically nuanced sub-types, mitigates batch effects arising from sequencing depth variation, and eliminates the dependence on knowledge-restricted gene selection for learning meaningful cell representations. Overall, GAIA offers a knowledge-lean, variance-stabilizing framework for analyzing single-cell and spatial transcriptomic data, enhancing discrimination between nuanced cell sub-type and -states.

bioinformatics↗

bioRxiv2026

scDesignPop generates realistic population-scale single-cell RNA-seq for power analysis, benchmarking, and privacy protection

Single-cell RNA sequencing (scRNA-seq) combined with genotyping in large cohorts has enabled the discovery of genetic associations with molecular traits (e.g., eQTLs) at cell-type resolution. However, generating population-scale data remains cost-prohibitive, selecting appropriate analysis methods lacks consensus, and sharing eQTL results alongside scRNA-seq data raises privacy risks. To address these challenges, we introduce scDesignPop, a flexible statistical simulator for generating realistic population-scale scRNA-seq data with genetic effects. scDesignPop models cell- and individual-level covariates, putative cell-type-specific eQTLs (cts-eQTLs), and either real or synthetic genotypes. We validated scDesignPop using the OneK1K and CLUES cohorts across 4 qualitative and 16 quantitative metrics. Unlike splatPop, the only existing population-scale simulator, scDesignPop better preserves eQTL effects and gene-gene dependencies within cell types, closely recapitulating characteristics of the reference data. Leveraging its generative framework, scDesignPop enables power analysis in cell types under multiple eQTL model specifications to guide experimental design; facilitates benchmarking of single-cell eQTL mapping methods through user-defined ground truths; and mitigates re-identification risk using synthetic data while retaining cts-eQTL effects.

bioinformatics↗

bioRxiv2026

Systematic mapping of emergent transcriptional states in interacting single-cell dyads by Cell-Cell-seq

Cell-cell interactions drive rapid and heterogeneous changes in gene expression, yet most transcriptomic methods either dissociate cells, losing pair identity and interaction timing, or infer communication indirectly from ligand-receptor co-expression. Here we present Cell-Cell-seq, a scalable workflow for profiling defined cell pairs ("dyads") with single-cell transcriptomic resolution. Cell-Cell-seq uses cavity-containing hydrogel microparticles (Nanovials) to confine two cells, synchronize contact onset, protect fragile conjugates during handling and sorting, and interface directly with droplet-based RNA sequencing. Using antigen-matched prostate tumor cells and engineered T cells as a model system, Cell-Cell-seq captured thousands of tumor-T cell dyads and revealed broad functional and transcriptional heterogeneity across interactions. Dyads unmasked transient activation programs that were obscured in standard well-plate co-culture, consistent with asynchronous contact in bulk assays. To distinguish interaction-induced programs from the composite nature of dyad transcriptomes, we developed a pseudo-mixing framework that generates in silico pseudo-dyads to construct an empirical null distribution under "no interaction," enabling statistically robust identification of emergent genes and partner-resolved attribution of responses. Dyad-resolved analysis further revealed coordinated cross-cell programs, including coupled chemokine expression consistent with bidirectional paracrine signaling and inverse coupling between tumor immunoregulatory programs and T cell activation. Finally, we introduce ccRepair to correct compositional dilution in mixed transcriptomes, improving interpretability while preserving genuine cross-cell coordination. Together, Cell-Cell-seq provides a generalizable platform for dissecting immune synapse biology and mapping interaction-dependent programs across heterogeneous cell populations, with applications in profiling tumor-immune communication and functionally screening immunotherapies.

bioengineering↗

bioRxiv2026

Hypatia: a set of quantitative methods for profiling isoform across cell populations

High-throughput long-read single-cell RNA-sequencing enables isoform-level study across single cells, yet methods for systematically assessing cell-to-cell variations remain limited. Here, we develop Hypatia, a comprehensive platform for dissecting isoform complexities across cell populations, devising Tsallis entropy and Cramers V to facilitate robust comparative profiling. Hypatia revealed prominent isoform species variations and usage shifts across cell-types in glioblastoma, renal cell carcinoma, and heart, highlighting clinically relevant applications for studying isoform-derived, cell-specific functions.

bioinformatics↗

bioRxiv2026

Evolutionary dynamics of temporal transcription factor series in the insect optic lobe

The nervous system consists of a wide variety of neuronal cell types arranged into complex circuits that support a broad range of behaviors. Patterning of neural stem cells in time through the sequential expression of series of temporal transcription factors is a key contributor to the generation of neuronal diversity. How do temporal series arise and diversify across species to support the evolution of neuronal type? Here, we reconstruct the evolutionary history of the temporal series in the visual brain of insects, spanning 400 million years of evolution; we identify a conserved temporal ground plan, as well as species-specific variations. We find that temporal programs evolve through recurrent modifications of a shared scaffold. Finally, we show that such evolutionary changes in temporal patterning can result in different neuronal type identities. Together, our results reveal both the deep conservation and evolutionary plasticity of temporal patterning programs, and establish temporal transcription factor series as a tractable substrate for the evolution of neuronal diversity.

developmental biology↗

bioRxiv2026

CIPHER: An end-to-end framework for designing optimized aggregated spatial transcriptomics experiments

MotivationMost imaging-based spatial transcriptomics methods measure individual genes, which limits scalability and typically requires integration with scRNA-seq to recover full cellular states. Recent approaches such as CISI, FISHnCHIPs, and ATLAS address this limitation by measuring aggregate transcriptional signatures, where multiple genes are pooled into each channel to increase throughput. While aggregate measurements improve scalability, they shift the problem from gene selection to feature design. For effective integration with scRNA-seq, these signatures must be not only discriminative in transcriptional space but also straightforward to measure, with balanced signal, sufficient dynamic range, and robustness to experimental noise. By optimizing decoding accuracy in isolation, existing methods leave substantial performance on the table. ResultsWe present CIPHER (Cell Identity Projection using Hybridization Encoding Rules), a neural-network framework that jointly optimizes the experimental encoding matrix, i.e., the way that genes are aggregated to signatures, and the downstream cell embedding. CIPHER integrates the physical limits of imaging assays directly into its loss function, shaping the latent space to maximize discriminability while maintaining robustness to measurement noise and signal constraints. Using a large-scale mouse brain scRNA-seq reference, we show that CIPHER-designed encodings yield latent spaces with improved cell-type separability, uniform signal utilization, and greater resilience to hybridization variability, resulting in higher decoding accuracy from both simulated and experimental data. ConclusionCIPHER formulates aggregate signature design as a joint optimization problem over decoding accuracy and experimental measurability. This enables systematic, scRNA-seq-aligned feature design for scalable spatial transcriptomics based on aggregate measurements. AvailabilityCode and documentation are available at https://github.com/wollmanlab/Design/. Author summarySpatial transcriptomics reveals how cells are organized within tissues by mapping where genes are expressed. To achieve both scale and resolution, many approaches now combine spatial imaging with single-cell RNA-seq references to reconstruct complete transcriptomes in situ. New methods such as CISI, FISHnCHIPs, and ATLAS accelerate this process by measuring combinations of co-expressed genes rather than each gene individually. These aggregate measurements simplify experiments but introduce a new challenge: deciding which genes to combine so that the resulting features are both experimentally reliable and computationally informative for integration with scRNA-seq data. We developed CIPHER, a computational framework that learns how to design and decode these aggregate measurements optimally. By integrating experimental constraints and decoding accuracy into a unified neural-network model, CIPHER provides a principled approach for designing signature-based spatial transcriptomics experiments that enable efficient and accurate reconstruction of cellular transcriptomes.

bioinformatics↗

bioRxiv2025

spCorr: flexible and scalable inference of spatially varying correlation in spatial transcriptomics

Spatial transcriptomics has transformed our ability to explore gene expression within its tissue context, enabling us to dissect subtle yet biologically significant variations in situ. While numerous computational methods have been proposed for detecting Spatially Varying Genes (SVGs) expression by modeling each individual gene separately, much less effort has been devoted to understanding how correlations between genes change across space. Such Spatially Varying Correlations (SVCs) are critical for understanding biological processes such as gene regulatory mechanisms shaped by local tissue environments, yet existing tools remain limited for this task. To address this gap, we present spCorr, a flexible and scalable regression framework for studying SVCs. spCorr provides interpretable, spot-level estimates of gene correlation and detects gene pairs whose correlations vary across locations or between tissue domains. Through extensive simulations and real-data analyses, we show that spCorr achieves high detection power, reliably controls the False Discovery Rate (FDR), and is computationally efficient. Importantly, spCorr reveals biologically meaningful correlation patterns that highlight fine-scale tissue structures, gene module functions, and region-specific interactions, offering new opportunities to study coordinated gene regulation in spatial transcriptomics.

bioinformatics↗

bioRxiv2025

Lipid nanoparticle co-delivery of mRNA and a small molecule drug for oral cancer chemoimmunotherapy

Oral squamous cell carcinoma (OSCC) represents 90% of all head and neck cancers. Despite decades of research, the 5-year survival rate is 50%, and strikingly, the overall incidence rate is projected to increase by 30% in the next ten years, which will result in a sharp increase in mortality. Two fundamental aspects of OSCC are that it progresses via the inactivation and mutation of tumor suppressor (TS) genes and has a "cold" tumor immune microenvironment (TIME). A major barrier in the treatment of OSCC is the lack of novel therapies clinicians have at their disposal that are designed to disrupt tumor progression by reshaping the cold tumors into inflammatory "hot" tumors. To overcome these obstacles, we employed a lipid nanoparticle (LNP) that co-encapsulates p53 mRNA and the small molecule ciclopirox (CPX). We demonstrate that both drugs have innate chemotherapeutic properties by facilitating caspase activation. Moreover, these therapies can create a less immunosuppressive TIME in part by repolarizing tumor-associated macrophages (TAMs) to M1-like phenotypes. When formulated together, our platform provides an all-in-one approach for OSCC, effective in both p53-therapy-susceptible and p53-therapy-resistant models. Additionally, this work provides a template for a delivery platform capable of tackling multiple mechanisms of OSCC progression and survival.

bioengineering↗

bioRxiv2025

16S rRNA Survey Reveals the Potential of Oral Microbiota in Distinguishing Patients with Chronic Heart Failure from Healthy Controls

BackgroundOral microbiota can reflect physiological functions and pathological conditions in human body. Patients with chronic heart failure (CHF) exhibit distinct oral health status compared to healthy controls (HCs), which is attributed to the differences in dominant microbial communities present in the oral cavity. Up to date, there are few studies examined the association between CHF and dominant oral microbiota. To fill in this research gap, this study aimed to investigate the differences of oral microbiota between CHF patients and HCs, to identify valuable novel biomarkers for CHF. MethodsChronic heart failure patients and healthy volunteers were recruited. Oral microbiota samples were then collected using oral swabs, and 16S rRNA sequencing was employed to analyze the microbiota. Statistical analysis was conducted to identify key bacteria at multiple taxonomic levels in the oral microbiota samples from both the CHF patient and healthy control groups, with a focus on core genera to identify potential biomarkers and evaluate their diagnostic efficacy. ResultsThere were 60 CHF patients and 30 HCs were recruited, with 42 CHF patients with New York Heart Association (NYHA) functional class II-IV and 28 HCs were included in the final analysis. The alpha diversity was higher in HCs, while beta diversity was higher in CHF patients. The CHF patients showed significant differences from HCs at five gene (phylum, class, order, family and genus) levels by analyzing the relative richness of microbiota at different taxomal levels. Altogether 14 microbes could distinguish CHF patients from HCs, i.e., Abiotrophia, Butyrivibrio, Lactobacillus, Capnocytophaga and Neisseria which are more abundant in CHF patients, and Actinomyces, Anaerovorax, Eubacterium, Kingella, Mogibacterium, Peptococcus, Peptostreptococcus, Solobacterium and TM7_genus_incertae_sedis which are more abundant in HCs. Furthermore, the AUC of their combined diagnosis was 83.7% (95% confidential interval 74.1%-93.3%), which have high reliability for the diagnostic significance. In accordance to Spearmans correlation, Eubacterium, Solobacterium and Rhizobium were core genera and the abundance of Eubacterium and Solobacterium exhibited downward trends as NYHA class increases. ConclusionThis study revealed the dysbiosis of the oral microbiota in CHF patients and identified potential biomarkers for CHF diagnosis and management. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/669863v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@d60f6eorg.highwire.dtl.DTLVardef@18f17ceorg.highwire.dtl.DTLVardef@163f0daorg.highwire.dtl.DTLVardef@599bcd_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2025

CellScope: High-Performance Cell Atlas Workflow with Tree-Structured Representation

Single-cell sequencing enables comprehensive profiling of individual cells, revealing cellular heterogeneity and function with unprecedented resolution. However, current analysis frameworks lack the ability to simultaneously explore and visualize cellular hierarchies at multiple biological levels. To address these limitations, we present CellScope, an innovative framework for constructing high-resolution cell atlases at multiple clustering levels. CellScope employs a two-step manifold fitting process for gene selection and noise reduction, followed by agglomerative clustering, and uniquely integrates UMAP visualization with hierarchical clustering to intuitively represent cellular relationships simultaneously at multiple levels--such as cell lineage, cell type, and cell subtype levels. Compared to established pipelines such as Seurat and Scanpy, CellScope comprehensively improves clustering performance, visualization clarity, computational efficiency, and algorithm interpretability, while reducing dependence on hyperparameters across a multitude of single-cell datasets. Most importantly, it can reveal new biological insights that other contemporary methods are unable to detect, thereby deepening our understanding of cellular heterogeneity and function, and potentially informing disease research.

bioinformatics↗

bioRxiv2025

Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq

The transcriptome provides a highly informative molecular phenotype to connect genotype to phenotype and is most frequently measured by RNA-sequencing (RNA-seq). Therefore, an ultimate goal is to perturb every gene and measure changes in the transcriptome. However, this remains challenging, especially in intact organisms due to different experimental and computational challenges. Here, we present Worm Perturb-Seq (WPS), which provides high-resolution RNA-seq profiles for hundreds of replicate perturbations at a time in a living animal. WPS introduces multiple experimental advances that combine strengths of bulk and single cell RNA-seq, and that further provides an analytical framework, EmpirDE, that leverages the unique power of the large WPS datasets. EmpirDE identifies differentially expressed genes (DEGs) by using gene-specific empirical null distributions, rather than control conditions alone, thereby systematically removing technical biases and improving statistical rigor. We applied WPS to 103 Caenhorhabditis elegans nuclear hormone receptors (NHRs) to delineate a Gene Regulatory Network (GRN) and found that this GRN presents a striking pairwise modularity where pairs of NHRs regulate shared target genes. We envision that the experimental and analytical advances of WPS should be useful not only for C. elegans, but will be broadly applicable to other models, including human cells.

genomics↗

bioRxiv2024

Systematic dissection of epigenetic age acceleration in normal breast tissue reveals its link to estrogen signaling and cancer risk

Breast aging encompasses intricate molecular and cellular changes that elevate cancer risk. Our study profiled DNA methylation and gene expression of 181 normal breast samples and systematically evaluated eight epigenetic clocks. We found that clocks trained using breast tissues demonstrate improved age prediction in normal breast tissue, and bias universally exists in epigenetic clocks, necessitating a proper definition of age acceleration. Cell composition analysis revealed significant age-related alterations and highlighted its distinct associations with age acceleration, including increased luminal epithelial and myoepithelial cells and reduced adipocytes and immune cells, connecting age acceleration to carcinogenesis from a cell compositional perspective. Additionally, CpG sites associated with age acceleration were enriched for estrogen receptor binding sites, providing a mechanistic link between estrogen exposure, accelerated aging, and cancer. These findings highlight the importance of cellular heterogeneity in epigenetic age estimates and the potential of age acceleration to guide for risk stratification and prevention strategies.

bioinformatics↗

bioRxiv2024

mcRigor: a statistical method to enhance the rigor of metacell partitioning in single-cell data analysis

In single-cell data analysis, addressing sparsity often involves aggregating the profiles of homogeneous single cells into metacells. However, existing metacell partitioning methods lack checks on the homogeneity assumption and may aggregate heterogeneous single cells, potentially biasing downstream analysis and leading to spurious discoveries. To fill this gap, we introduce mcRigor, a statistical method to detect dubious metacells, which are composed of heterogeneous single cells, and optimize the hyperparameter of a metacell partitioning method. The core of mcRigor is a feature-correlation-based statistic that measures the heterogeneity of a metacell, with its null distribution derived from a double permutation scheme. As an optimizer for existing metacell partitioning methods, mcRigor has been shown to improve the reliability of discoveries in single-cell RNA-seq and multiome (RNA+ATAC) data analyses, such as uncovering differential gene co-expression modules, enhancer-gene associations, and gene temporal expression. Moreover, mcRigor enables benchmarking and selection of the most suitable metacell partitioning method with optimized hyperparameters tailored to specific datasets, ensuring reliable downstream analysis. Our results indicate that among existing metacell partitioning methods, MetaCell and SEACells consistently outperform MetaCell2 and SuperCell, albeit with the trade-off of longer runtimes.

bioinformatics↗

bioRxiv2024

Semisynthetic Simulation for Microbiome Data Analysis

High-throughput sequencing data lie at the heart of modern microbiome research. Effective analysis of these data requires careful preprocessing, modeling, and interpretation to detect subtle signals and avoid spurious associations. In this review, we discuss how simulation can serve as a sandbox to test candidate approaches, creating a setting that mimics real data while providing ground truth. This is particularly valuable for power analysis, methods benchmarking, and reliability analysis. We explain the probability, multivariate analysis, and regression concepts behind modern simulators and how different implementations make trade-offs between generality, faithfulness, and controllability. Recognizing that all simulators only approximate reality, we review methods to evaluate how accurately they reflect key properties. We also present case studies demonstrating the value of simulation in differential abundance testing, dimensionality reduction, network analysis, and data integration. Code for these examples is available in an online tutorial (https://go.wisc.edu/8994yz) that can be easily adapted to new problem settings.

bioinformatics↗

bioRxiv2024

Spatial Single-Cell Mapping of Transcriptional Differences Across Genetic Backgrounds in Mouse Brains

Genetic variation can alter organ structure and, in turn, function. Comparative statistical analysis of organs across genetic backgrounds requires spatial, single-cell, atlas-scale data in replicates, which current technologies do not provide at scale. We introduce Atlas-scale Transcriptome Localization using Aggregate Signatures (ATLAS), a scalable tissue mapping method. ATLAS learns transcriptional signatures from scRNAseq data, encodes them in situ with tens of thousands of oligonucleotide probes, and decodes them to infer cell types and imputed transcriptomes. We validated ATLAS in the mouse brain by comparing its cell type inferences with direct MERFISH measurements of marker genes and quantitative comparisons to four other technologies. Using ATLAS, we mapped the central brains of five male and five female C57BL/6J (B6) mice and five male BTBR T+ tf/J (BTBR) mice, an idiopathic model of autism, collectively profiling over 40 million cells across over 400 coronal sections. Our analysis revealed over 40 significant differences in cell type distributions and identified 16 regional composition changes across male-female and B6-BTBR comparisons. ATLAS thus enables systematic comparative studies, facilitating organ-level structure-function analysis of disease models.

genomics↗