Biology subjects

Li, H.-D.

Publications and source records attributed to Li, H.-D..

6 recordsLinked to original sources

bioRxiv2026

stEDGE enables edge-guided multiscale reconstruction of hierarchical spatial domains and transition interfaces in spatial transcriptomics

Spatial transcriptomic technologies enable high-resolution mapping of tissue architecture, yet most computational methods still represent tissues as flat spatial partitions, limiting their ability to resolve spatial domain boundaries, gradual transition regions and nested hierarchies. Here we present stEDGE, an edge-guided and interpretable framework for multiscale reconstruction of spatial domain hierarchies and transition-associated states in spatial transcriptomics. Unlike conventional domain-first approaches, stEDGE first estimates local boundary structure and uses it to guide the reconstruction of fine-grained spatial domains. We further introduce a domain transition index (DTI) to quantify domain-level transition propensity and integrate DTI with inter-domain similarity and boundary strength to organize fine domains into a coherent multilevel hierarchy. A tree-guided gene attribution strategy then distinguishes shared parent-level programs from branch-specific specialization across hierarchical spatial states. Across 15 benchmarked tissue sections spanning diverse tissues, technologies and spatial resolutions, stEDGE reconstructs developmental, inflammatory, tumor and brain architectures beyond conventional domain partitioning. Applied to cell-resolved Xenium profiling of fibrotic human lung, stEDGE further reveals lesion-associated remodeling as a coherent multiscale hierarchy in which remodeling interfaces coexist with a stable macrophage-associated airway/lumen state and spatially organized TLS-like lymphoid niches. Together, stEDGE provides a unified framework for modeling how stable compartments, domain boundaries, remodeling interfaces, localized niches and multiscale gene programs are jointly organized across spatial scales.

bioinformatics↗

bioRxiv2025

Inference and visualization of multi-scale cell tree for decoding functional diversity with scMustree

AbstractDeciphering cellular heterogeneity and functional diversity is crucial to understanding biological systems and disease mechanisms. Although single-cell RNA sequencing (scRNA-seq) has revolutionized this field, existing methods often fail to characterize the functional associations between cell populations due to the discrete nature of clustering results. We introduce scMustree, a tree- construction algorithm that enables in-depth exploration of functional associations and transitional states among cellular populations. scMustree combines top-down iterative decomposition for high- purity leaf nodes with bottom-up merging based on quantitative functional distances, preserving local continuity and global structural clarity. Benchmarking on real datasets shows that scMustree not only outperforms existing methods in clustering accuracy but also reveals functionally coherent subtypes, anatomically related cell types, and developmentally connected transitions. In case studies, it identifies Alzheimers disease-associated microglial subtypes and captures spatial cell distribution patterns of cell types in developed embryos, demonstrating its utility as a powerful tool for multi-scale exploration of cellular heterogeneity.

bioinformatics↗

bioRxiv2024

BiMultiNetPlot: An R package for visualizing ecological bipartite multilayer networks

O_LIWith the increasing study of ecological multilayer bipartite networks, the visualization of these networks has become more important. However, tools for visualizing multilayer networks are still lacking. C_LIO_LII present BiMultiNetPlot, an R package designed for visualizing ecological bipartite multilayer networks. C_LIO_LII demonstrate how to use BiMultiNetPlot through a series of examples that represent the most common types of ecological multilayer networks. C_LIO_LIBiMultiNetPlot is an open-source, flexible package within the ggplot2 environment, helping ecologists better understand the multilayer nature of ecological networks. C_LI

ecology↗

bioRxiv2024

Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression data

Single-cell RNA sequencing (scRNA-seq) technologies have been widely used to characterize cellular landscapes in complex tissues. Large-scale single-cell transcriptomics holds great potential for identifying rare cell types critical to the pathogenesis of diseases and biological processes. Existing methods for identifying rare cell types often rely on one-time clustering using partial or global gene expression. However, these rare cell types may be overlooked in the initial clustering step, making them difficult to distinguish. In this paper, we propose a Cluster decomposition-based Anomaly Detection method (scCAD), which iteratively decomposes clusters based on the most differential signals in each cluster to effectively separate rare cell types and achieve accurate identification. We benchmark scCAD on 25 real-world scRNA-seq datasets, demonstrating its superior performance compared to 10 state-of-the-art methods. In-depth case studies across diverse datasets, including mouse airway, brain, intestine, human pancreas, immunology data, and clear cell renal cell carcinoma, showcase scCADs efficiency in identifying rare cell types in complex biological scenarios. Furthermore, scCAD can correct the annotation of rare cell types and identify immune cell subtypes associated with disease, providing new insights into disease progression.

bioinformatics↗

bioRxiv2021

Genome-wide prediction and integrative functional characterization of Alzheimer's disease-associated genes

The mechanism of Alzheimers disease (AD) remains elusive, partly due to the incomplete identification of risk genes. We developed an approach to predict AD-associated genes by learning the functional pattern of curated AD-associated genes from brain gene networks. We created a pipeline to evaluate disease-gene association by interrogating heterogeneous biological networks at different molecular levels. Our analysis showed that top-ranked genes were functionally related to AD. We identified gene modules associated with AD pathways, and found that top-ranked genes were correlated with both neuropathological and clinical phenotypes of AD on independent datasets. We also identified potential causal variants for genes such as FYN and PRKAR1A by integrating brain eQTL and ATAC-seq data. Lastly, we created the ALZLINK web interface, enabling users to exploit the functional relevance of predicted genes to AD. The predictions and pipeline could become a valuable resource to advance the identification of therapeutic targets for AD.

bioinformatics↗

bioRxiv2017

iREAD: A Tool For Intron Retention Detection From RNA-seq Data

SummaryDetecting intron retention (IR) events is emerging as a specialized need for RNA-seq data analysis. Here we present iREAD (intron REtention Analysis and Detector), a tool to detect IR events genome-wide from high-throughput RNA-seq data. The command line interface for iREAD is implemented in Python. iREAD takes as input an existing BAM file, representing the transcriptome, and a text file containing the intron coordinates of a genome. It then 1) counts all reads that overlap intron regions, 2) detects IR vents by analyzing features of reads such as depth and distribution patterns, and 3) outputs a list of retained introns into a tab-delimited text file. The output can be directly used for further exploratory analysis such as differential intron expression and functional enrichment. iREAD provides a new and generic tool to interrogate poly-A enriched transcriptomic data of intron regions.\n\nAvailabilitywww.libpls.net/iread\n\nContactNathan.Price@systemsbiology.org

bioinformatics↗