Biology subjects

Yang, J. Y. H.

Publications and source records attributed to Yang, J. Y. H..

At least 19 recordsLinked to original sources

bioRxiv2026

Generalisable tissue-wide molecular reconstruction from histology

Spatial transcriptomics technologies measure gene expression within intact tissues but remain difficult to scale across large tissue sections and patient cohorts. Consequently, many studies rely on tissue microarrays (TMAs) or sparse spatial profiling designs, where molecular measurements are available for only limited tissue regions and are often generated using heterogeneous gene panels. Existing H&E to spatial gene expression prediction methods remain challenged by sparse molecular measurements, partially overlapping gene panels and tissue-wide reconstruction across heterogeneous spatial datasets. Here, we present GHIST+, a framework for tissue-wide reconstruction of single-cell molecular states from H&E histology. GHIST+ integrates cellular morphology, local tissue context and shared tissue representations to extend sparse molecular measurements into tissue-wide molecular maps across heterogeneous spatial datasets. Across multiple cancer types and GTEx breast tissues, GHIST+ reconstructs biologically meaningful tissue-wide molecular organisation from sparse TMA-derived measurements while preserving spatial tissue structure, cell-type organisation and age-associated tissue states across cancer and non-cancer settings. GHIST+ establishes a scalable framework for transforming sparse spatial profiling experiments into tissue-wide molecular maps, enabling cohort-scale molecular reconstruction from routine histology under heterogeneous spatial transcriptomic settings.

bioinformatics↗

bioRxiv2026

CCIDeconv: Hierarchical model for deconvolution of subcellular cell-cell interactions in single-cell data

Cell-cell interaction (CCI) underlies several fundamental biological processes, including development, homeostasis and disease progression. Subcellular spatial transcriptomics (sST) provides an opportunity to examine whether CCI-associated signals show compartment-specific patterns within cells. Assessing CCI at subcellular level can help us gain insights into the distinct pathway activation and signalling patterns. We developed a novel approach that deconvolutes CCI into subcellular CCI (sCCI) information from non-spatial single-cell transcriptomics (scRNA- seq) based CCI using a modified CellChat-derived communication score. By estimating communication scores separately for cytoplasmic and nuclear compartments, we identified compartment-associated sCCI. We then deconvolved whole-cell communication scores into subcellular compartments using a hierarchical classification and regression framework, which we call CCIDeconv. To ensure biological fidelity, we integrated protein localization data from the Human Protein Atlas in our deconvolution model. Across nine publicly available human sST datasets, leave-one- dataset-out validation achieved a median composite score of 0.75, with mean R2 values of 0.87 and 0.80 for cytoplasmic- and nuclear-associated scores, respectively. Performance without spatial features approached that of spatial models as the number of training datasets increased, supporting application to non-spatial scRNA-seq data. This highlighted the potential for prediction of sCCI from scRNA-seq, given a sufficiently large number of training datasets. Overall, our method can attribute whole-cell CCI to its subcellular compartments, allowing researchers to dissect sCCI patterns and gain insights into the underlying biology of healthy and disease tissues. Keywords Cell-Cell Communication, Single Cell RNA-seq, Predictive Modeling, Bioinformatics, Transcriptomics, Machine Learning

bioinformatics↗

bioRxiv2025

Large Language Models for Accessible Reporting of Bioinformatics Analyses in Interdisciplinary Contexts

Health and life scientists frequently rely on quantitative experts to perform complex data analyses, yet interpretation of these results often becomes a bottleneck due to communication barriers across disciplines. Large Language Models (LLMs) have the potential to act as intermediaries that translate analytical outputs into accessible scientific narratives, but their ability to reliably interpret outputs from real-world biomedical data analyses across disciplines remains unclear. In this study, we investigate how state-of-the-art LLMs behave when integrated into real bioinformatics analysis workflows as report-generation assistants for interdisciplinary teams. We evaluated LLM-generated summaries using both automated and human evaluation frameworks to ensure holistic evaluation. Automated assessment employed multiple choice questions designed using Bloom's taxonomy to assess multiple levels of understanding, while human evaluation tasked scientists to score summaries for factual consistency, lack of harmfulness, comprehensiveness, and coherence. All generally produced readable and largely safe summaries, confirming their value for first-pass translation of technical analyses, however frequently misinterpreted visualisations, produced verbose summaries and rarely offered novel insights beyond what was already contained in the analytics. Our findings suggest that LLMs are best suited for easing interdisciplinary communication rather than replacing domain expertise and human oversight remains essential to guarantee accuracy, interpretative depth, and the generation of genuinely novel scientific insights.

bioinformatics↗

bioRxiv2025

BenchHub enables an inclusive and transparent ecosystem for community-focused benchmarking in computational biology

The rapid growth of computational methods for the computational biology field highlights the critical role of benchmarking in guiding method selection. However, there is no standardised data structure that effectively links and stores datasets, performance metrics and available ground truth. Without such a unified and shareable structure, it is difficult for the community to contribute, update and extend existing benchmarking studies to ensure long-term relevancy. To address this challenge, we present BenchHub, a community-oriented ecosystem with a modular R6-based structure that enables "living benchmarking." BenchHub comprises three key components: (i) a Trio database that links datasets, performance metrics, and supporting evidence (e.g. ground truth), (ii) a BenchmarkStudy structure that captures the different benchmark study designs, and (iii) a series of tools together with vignettes and interactive platform that allow users to gain insights from the benchmarking results. Together, these components streamline the benchmarking process for benchmark study developers, methods contributors, and benchmark consumers, promoting reproducibility, comparability, and long-term sustainability in computational biology.

bioinformatics↗

bioRxiv2025

dioscRi enables transferable prediction of clinical outcomes in multi-parameter cytometry data

Multi-parameter cytometry technologies enable high-dimensional analysis of immune cell populations at single-cell resolution. Deep learning has emerged as a transformative tool for analyzing these datasets, but existing methods often struggle with transferability across datasets due to technical variability, batch effects and identification of biologically relevant cell populations, limiting their utility in clinical research. We present dioscRi, a transferable deep learning framework that integrates a maximum mean discrepancy variational autoencoder for normalization and de-noising, enhancing cross-dataset compatibility. Changes in cell type proportions and marker expression are identified by structuring these features within biologically or empirically derived cell type hierarchies. These hierarchies are incorporated directly into an overlapping group LASSO model, improving the prediction of clinical outcomes. When applied to a coronary artery disease study, dioscRi recapitulated several known immune associations. Benchmarking across multiple datasets demonstrated dioscRis ability to generalize and outperform existing methods, establishing it as a versatile and interpretable tool for cytometry data analysis.

bioinformatics↗

bioRxiv2025

Multi-view gene panel characterization for spatially resolved omics

Spatially resolved transcriptomics has transformed our ability to study complex tissues at the cellular and subcellular resolution. However, targeted spatial technologies require pre-selected gene panels, which are typically curated based on existing biological knowledge and prior research hypotheses. While current methods often prioritize capturing cell type information, we argue that an effective gene panel should also capture cell type diversity, cell states, pathway-level information, and minimize redundancy. To address these broader requirements, we developed a gene panel characterization platform that characterizes panels across multiple perspectives, thus allowing us to compare panels comprehensively. Notably, computationally constructed gene panels performed competitively in capturing major cell types when compared to our in-house manually curated panel. However, refined manual curation offered distinct advantages, particularly in capturing minor and rare cell types and exhibited lower information redundancy comparatively. Building on this framework, we integrated these metrics into a deep learning platform, panelScope, leveraging them as a loss function to design holistic gene panels. Using an acute myeloid leukemia (AML) dataset with 42 well-defined cell types and the 5K Xenium panel from 10X Genomics, we demonstrate the utility of our framework in comprehensively characterizing gene panels, enabling the design of tailored panels that address diverse research needs.

bioinformatics↗

bioRxiv2024

CLUEY enables knowledge-guided clustering and cell type detection from single-cell omics data

Clustering is a fundamental task in single-cell omics data analysis and can significantly impact downstream analyses and biological interpretations. The standard approach involves grouping cells based on their gene expression profiles, followed by annotating each cluster to a cell type using marker genes. However, the number of cell types detected by different clustering methods can vary substantially due to several factors, including the dimension reduction method used and the choice of parameters of the chosen clustering algorithm. These discrepancies can lead to subjective interpretations in downstream analyses, particularly in manual cell type annotation. To address these challenges, we propose CLUEY, a knowledge-guided framework for cell type detection and clustering of single-cell omics data. CLUEY integrates prior biological knowledge into the clustering process, providing guidance on the optimal number of clusters and enhancing the interpretability of results. We apply CLUEY to both unimodal (e.g. scRNA-seq, scATAC-seq) and multimodal datasets (e.g. CITE-seq, SHARE-seq) and demonstrate its effectiveness in providing biologically meaningful clustering outcomes. These results highlight CLUEY on providing the much-needed guidance in clustering analyses of single-cell omics data. CLUEY package is available from https://github.com/SydneyBioX/CLUEY.

bioinformatics↗

bioRxiv2024

Spatial gene expression at single-cell resolution from histology using deep learning with GHIST

The increased use of spatially resolved transcriptomics provides new biological insights into disease mechanisms. However, the high cost and complexity of these methods are barriers to broad clinical adoption. Consequently, methods have been created to predict spot-based gene expression from routinely-collected histology images. Recent benchmarking showed that current methodologies have limited accuracy and spatial resolution, constraining translational capacity. Here, we introduce GHIST, a deep learning-based framework that predicts spatial gene expression at single-cell resolution by leveraging subcellular spatial transcriptomics and synergistic relationships between multiple layers of biological information. We validated GHIST using public datasets and The Cancer Genome Atlas data, demonstrating its flexibility across different spatial resolutions and superior performance. Our results underscore the utility of in silico generation of single-cell spatial gene expression measurements and the capacity to enrich existing datasets with a spatially resolved omics modality, paving the way for scalable multi-omics analysis and new biomarker discoveries.

bioinformatics↗

bioRxiv2024

Multi-task benchmarking of spatially resolved gene expression simulation models

Computational methods for spatially resolved transcriptomics (SRT) are frequently developed and assessed through data simulation. The effectiveness of these evaluations relies on the simulation methods ability to accurately reflect experimental data. However, a systematic evaluation framework for spatial simulators is lacking. Here, we present SpatialSimBench, a comprehensive evaluation framework that assesses 13 simulation methods using 10 distinct STR datasets. We introduce simAdaptor, a tool that extends single-cell simulators to incorporate spatial variables, thus enabling them to simulate spatial data. SimAdaptor enables SpatialSimBench to be "back-wards" compatible. That is, it facilitates direct comparison between spatially aware simulators and existing non-spatial single-cell simulators through the adaption. Through SpatialSimBench, we demonstrate the feasibility of leveraging existing single-cell simulators for SRT data and highlight performance differences among methods. Additionally, we evaluate the simulation methods based on a total of 35 metrics across data property estimation, various downstream analysis and scalability. In total, we generated 4550 results from 13 simulation methods, 10 spatial datasets and 35 metrics. Our findings reveal that model estimation can be impacted by distribution assumptions and dataset characteristics. In summary, our evaluation and the evaluation framework will provide guidelines for selecting appropriate methods for specific scenarios and informing future method development.

bioinformatics↗

bioRxiv2024

Pathway metabolite ratios reveal distinctive glutamine metabolism in a subset of proliferating cells

Large-scale metabolomic analyses of pan-cancer cell line panels have provided significant insights into the relationships between metabolism and cancer cell biology. Here, we took a pathway-centric approach by transforming targeted metabolomic data into ratios to study associations between reactant and product metabolites in a panel of cancer and non-cancer cell lines. We identified five clusters of cells from various tissue origins. Of these, cells in Cluster 4 had high ratios of TCA cycle metabolites relative to pyruvate, produced more lactate yet consumed less glucose and glutamine, and greater OXPHOS activity compared to Cluster 3 cells with low TCA cycle metabolite ratios. This was due to more glutamine cataplerotic efflux and not glycolysis in cells of Cluster 4. In silico analyses of loss-of-function and drug sensitivity screens showed that Cluster 4 cells were more susceptible to gene deletion and drug targeting of lactate and glutamine metabolism, and OXPHOS than cells in Cluster 3. Our results highlight the potential of pathway-centric approaches to reveal new aspects of cellular metabolism from metabolomic data.

cancer biology↗

bioRxiv2024

Identification of SEC61B as a novel regulator of calcium flux and platelet hyperreactivity in diabetes mellitus

High platelet reactivity is associated with adverse clinical events and is more frequent in people with diabetes mellitus (DM). To better understand platelet dysfunction in DM, we performed a proteomic analysis in platelets from a matched cohort of 34 people without, and 42 people with type 2 DM. The cohorts were matched by clinical characteristics including age, sex, and coronary artery disease burden. Using high sensitivity unbiased proteomics, we consistently identified over 2,400 intracellular proteins, and detected proteins that are differentially released by platelets from people with diabetes in response to low dose thrombin. Importantly, we identified the endoplasmic reticulum (ER) protein SEC61 translocon subunit beta (SEC61B) was increased in platelets from humans and mice with in vivo hyperglycemia. SEC61B was increased in megakaryocytes in mouse models of diabetes, in association with megakaryocyte ER stress. A rise in cytosolic calcium is a key aspect in platelet activation, and the SEC61 translocon is known to act as a channel for ER calcium leak. We demonstrate that cultured cells overexpressing SEC61B have increased calcium flux and decreased protein synthesis. In accordance, hyperglycemic mouse platelets mobilized more calcium to the cytosol and had lower protein synthesis compared with normoglycemic platelets. Independently, in vitro induction of ER stress increased platelet SEC61B expression and markers of platelet activation. We propose a mechanism whereby ER stress-induced upregulation of platelet SEC61B leads to increased cytosolic calcium, potentially contributing to platelet hyperactivity in people with diabetes. Key PointsO_LIPlatelet SEC61B is increased in hyperglycemia and contributes to increased endoplasmic reticulum (ER) calcium leak C_LIO_LIIncreased ER calcium leak is associated with ER stress and platelet hyperactivity C_LI

cell biology↗

bioRxiv2023

The current landscape and emerging challenges of benchmarking single-cell methods

With the rapid development of computational methods for single-cell sequencing data, benchmarking serves as a valuable resource. As the number of benchmarking studies surges, it is timely to assess the current state of the field. We conducted a systematic literature search and assessed 282 papers, including all 130 benchmark-only papers from the search and an additional 152 method development papers containing benchmarking. This collective effort provides the most comprehensive quantitative summary of the current landscape of single-cell benchmarking studies. We examine performances across nine broad categories, including often ignored aspects such as role of datasets, robustness of methods and downstream evaluation. Our analysis highlights challenges such as how to effectively combine knowledge across multiple benchmarking studies and in what ways can the community recognise the risk and prevent benchmarking fatigue. This paper highlights the importance of adopting a community-led research paradigm to tackle these challenges and establish best practice standards.

bioinformatics↗

bioRxiv2023

eNODAL: an experimentally guided nutriomics data clustering method to unravel complex drug-diet interactions

Unraveling the complex interplay between nutrients and drugs via their effects on omics features could revolutionize our fundamental understanding of nutritional physiology, personalized nutrition and ultimately human health-span. Experimental studies in nutrition are starting to use large-scale omics experiments to pick apart the effects of such interacting factors. However, the high dimensionality of the omics features, coupled with complex fully-factorial experimental designs together pose a challenge to the analysis. Current strategies for analyzing such types of data are based on between-feature correlations. However, these techniques risk overlooking important signals that arise from the experimental design and produce clusters that are hard to interpret. We present a novel approach for analyzing high-dimensional outcomes in nutriomics experiments, termed experiment-guided NutriOmics DatA cLustering (eNODAL). This three-step hybrid framework takes advantage of both ANOVA-type analyses and unsupervised learning methods to extract maximum information from experimental nutriomics studies. First, eNODAL categorizes the omics features into interpretable groups based on the significance of response to the different experimental variables using an ANOVA-like test. Such groups may include the main effects of a nutritional intervention, and drug exposure, or their interaction. Second, consensus clustering is performed within each interpretable group to further identify subclusters of features with similar response profiles to these experimental factors. Third, eNODAL annotates these subclusters based on their experimental responses and biological pathways enriched within the subcluster. We validate eNODAL using data from a mouse experiment to test for the interaction effects of macronutrient intake and drugs that target aging mechanisms in mice.

bioinformatics↗

bioRxiv2023

Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data

Recent advances in subcellular imaging transcriptomics platforms have enabled high-resolution spatial mapping of gene expression, while also introducing significant analytical challenges in accurately identifying cells and assigning transcripts. Existing methods grapple with cell segmentation, frequently leading to fragmented cells or oversized cells that capture contaminated expression. To this end, we present BIDCell, a self-supervised deep learning-based framework with biologically-informed loss functions that learn relationships between spatially resolved gene expression and cell morphology. BIDCell incorporates cell-type data, including single-cell transcriptomics data from public repositories, with cell morphology information. Using a comprehensive evaluation framework consisting of metrics in five complementary categories for cell segmentation performance, we demonstrate that BIDCell outperforms other state-of-the-art methods according to many metrics across a variety of tissue types and technology platforms. Our findings underscore the potential of BIDCell to significantly enhance single-cell spatial expression analyses, including cell-cell interactions, enabling great potential in biological discovery.

bioinformatics↗

bioRxiv2023

Construction and optimization of multi-platform precision pathways for precision medicine

In the enduring challenge against disease, advancements in medical technology have empowered clinicians with novel diagnostic platforms. Whilst in some cases, a single test may provide a confident diagnosis, often additional tests are required. However, to strike a balance between diagnostic accuracy and cost-effectiveness, one must rigorously construct the clinical pathways. Here, we developed a framework to build multi-platform precision pathways in an automated, unbiased way, recommending the key steps a clinician would take to reach a diagnosis. We achieve this by developing a confidence score, used to simulate a clinical scenario, where at each stage, either a confident diagnosis is made, or another test is performed. Our framework provides a range of tools to interpret, visualize and compare the pathways, improving communication and enabling their evaluation on accuracy and cost, specific to different contexts. This framework will guide the development of novel diagnostic pathways for different diseases, accelerating the implementation of precision medicine into clinical practice.

bioinformatics↗

bioRxiv2023

scTIE: data integration and inference of gene regulation using single-cell temporal multimodal data

Single-cell technologies offer unprecedented opportunities to dissect gene regulatory mecha-nisms in context-specific ways. Although there are computational methods for extracting gene regulatory relationships from scRNA-seq and scATAC-seq data, the data integration problem, essential for accurate cell type identification, has been mostly treated as a standalone challenge. Here we present scTIE, a unified method that integrates temporal multimodal data and infers regulatory relationships predictive of cellular state changes. scTIE uses an autoencoder to embed cells from all time points into a common space using iterative optimal transport, followed by extracting interpretable information to predict cell trajectories. Using a variety of synthetic and real temporal multimodal datasets, we demonstrate scTIE achieves effective data integration while preserving more biological signals than existing methods, particularly in the presence of batch effects and noise. Furthermore, on the exemplar multiome dataset we generated from differentiating mouse embryonic stem cells over time, we demonstrate scTIE captures regulatory elements highly predictive of cell transition probabilities, providing new potentials to understand the regulatory landscape driving developmental processes.

bioinformatics↗

bioRxiv2023

Ensemble deep learning of embeddings for clustering multimodal single-cell omics data

MotivationRecent advances in multimodal single-cell omics technologies enable multiple modalities of molecular attributes, such as gene expression, chromatin accessibility, and protein abundance, to be profiled simultaneously at a global level in individual cells. While the increasing availability of multiple data modalities is expected to provide a more accurate clustering and characterisation of cells, the development of computational methods that are capable of extracting information embedded across data modalities is still in its infancy. ResultsWe propose SnapCCESS for clustering cells by integrating data modalities in multimodal singlecell omics data using an unsupervised ensemble deep learning framework. By creating snapshots of embeddings of multimodality using variational autoencoders, SnapCCESS can be coupled with various clustering algorithms for generating consensus clustering of cells. We applied SnapCCESS with several clustering algorithms to various datasets generated from popular multimodal single-cell omics technologies. Our results demonstrate that SnapCCESS is effective and more efficient than conventional ensemble deep learning-based clustering methods and outperforms other state-of-the-art multimodal embedding generation methods in integrating data modalities for clustering cells. The improved clustering of cells from SnapCCESS will pave the way for more accurate characterisation of cell identity and types, an essential step for various downstream analyses of multimodal single-cell omics data. Availability and implementationSnapCCESS is implemented as a Python package and is freely available from https://github.com/yulijia/SnapCCESS.

bioinformatics↗

bioRxiv2022

Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2

The recent emergence of multi-sample multi-condition single-cell multi-cohort studies allow researchers to investigate different cell states. The effective integration of multiple large-cohort studies promises biological insights into cells under different conditions that individual studies cannot provide. Here, we present scMerge2, a scalable algorithm that allows data integration of atlas-scale multi-sample multi-condition single-cell studies. We have generalised scMerge2 to enable the merging of millions of cells from single-cell studies generated by various single-cell technologies. Using a large COVID-19 data collection with over five million cells from 1000+ individuals, we demonstrate that scMerge2 enables multi-sample multi-condition scRNA-seq data integration from multiple cohorts and reveals signatures derived from cell-type expression that are more accurate in discriminating disease progression. Further, we demonstrate that scMerge2 can remove dataset variability in CyTOF, imaging mass cytometry and CITE-seq experiments, demonstrating its applicability to a broad spectrum of single-cell profiling technologies.

bioinformatics↗