Biology subjects

Garry, D.

Publications and source records attributed to Garry, D..

5 recordsLinked to original sources

bioRxiv2026

STEVE: Single-cell Transcriptomics Expression Visualization and Evaluation

Single-cell RNA sequencing (scRNA-seq) has become a key technology for characterizing cell-type heterogeneity in complex tissues. However, its utility depends on accurate and reproducible cell-type annotation, which remains a major analytical challenge. Although hundreds of computational tools have been developed for automated annotation, there is currently no systematic framework to evaluate annotation robustness in a dataset-specific manner or within the context of complete analytical pipelines. Here, we present STEVE (Single-cell Transcriptomics Expression Visualization and Evaluation), a quantitative framework designed to assess the accuracy, robustness, and reproducibility of cell-type annotation in scRNA-seq studies. STEVE implements three complementary in silico evaluation modules: (i) Subsampling Evaluation to quantify annotation stability under varying reference sizes and data partitions; (ii) Novel Cell Evaluation to assess the ability to detect previously unseen cell types; and (iii) Annotation Benchmarking to compare alternative annotation tools against ground-truth labels. In addition, STEVE includes a Reference Transfer Annotation module that enables cross-dataset cell-type mapping using external reference datasets. All modules are built upon a unified probabilistic framework that provides consistent confidence estimation across evaluation scenarios. We evaluated STEVE across four independent scRNA-seq datasets with experimentally defined or expert-curated cell-type labels. Our results show that annotation robustness is strongly influenced by the annotation method, biological separability, dataset complexity, and batch effects. STEVE provides a practical framework for quantifying annotation uncertainty and improving reproducibility in single-cell transcriptomic analyses. STEVE is freely available at GitHub (https://github.com/XiaoDongLab/STEVE).

bioinformatics↗

bioRxiv2025

Mouse Cardiac Regeneration Database (MCAREDB): a single-cell atlas of neonatal cardiac regeneration in mice

BackgroundThe adult mammalian heart exhibits minimal regenerative capacity, rendering most major cardiac injuries irreversible. In contrast, neonatal mice retain the ability to regenerate cardiac tissue for a brief period after birth. Although single-cell RNA sequencing (scRNA-seq) has deepened our understanding of this regenerative window, existing datasets remain dispersed across studies, hindering integrative and comparative analyses. Methods and ResultsTo address this gap, we developed the Mouse CArdiac REgeneration DataBase (MCAREDB), a freely accessible, web-based resource that consolidates scRNA-seq datasets from multiple mouse cardiac regeneration studies. MCAREDB enables cell type-specific exploration of differential gene expression and pathway enrichment across key biological contexts, including neonatal versus postnatal stages and sham versus myocardial infarction surgeries. Core features include an interactive genome browser with epigenetic tracks, comparative gene expression visualization, pathway enrichment analysis, and bulk data download options. ConclusionBy integrating diverse datasets into a unified and interactive framework, MCAREDB lowers technical barriers to data exploration and accelerates hypothesis generation in cardiac regeneration research. The resource is freely available at https://MCAREDB.org:3305.

bioinformatics↗

bioRxiv2022

SeATAC: a tool for exploring the chromatin landscape and the role of pioneer factors

The position of the nucleosome and chromatin packaging in eukaryotic genomes govern gene regulation and cellular functions. Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) is an efficient and precise method for revealing chromatin accessibility across the genome. However, there is no method that is specifically designed for detecting differential chromatin accessibility using ATAC-seq datasets. In this study, we developed a bioinformatics tool called SeATAC, that used a conditional variational autoencoder (CVAE) model to learn the latent representation of ATAC-seq V-plots, and to estimate the statistically differential chromatin accessibility. We demonstrated that SeATAC outperformed MACS2 and NucleoATAC on four separate tasks including: (1) detection of differential V-plots; (2) definition of nucleosome positions; (3) detection of nucleosome changes and (4) designation of transcriptional factor binding sites (TFBS) with differential chromatin accessibility. By applying SeATAC to several pioneer factor induced differentiation or reprogramming ATAC-seq datasets, we found that induction of these pioneer factors not only relaxed the closed chromatin but also decreased the chromatin accessibility of 20% - 30% of their target sites. These two groups of TF binding sites were characterized by different genomic distribution and histone marks. Here, we present SeATAC as a novel tool to accurately reveal the genomic regions with differential chromatin accessibility from ATAC-seq data.

bioinformatics↗

bioRxiv2019

Abcg2-Expressing Side Population Cells Contribute to Cardiomyocyte Renewal through Fusion

The adult mammalian heart has a limited regenerative capacity. Therefore, identification of endogenous cells and mechanisms that contribute to cardiac regeneration is essential for the development of targeted regenerative therapies. The side population (SP) phenotype enriches for stem cells throughout the body, and SP cells have been identified in the heart. We generated a novel Abcg2-driven, genetic lineage-tracing mouse model and show efficient labeling of SP cells. Labeled SP cells give rise to terminally differentiated cells in bone marrow and intestines. In the heart, we find that Abcg2-expressing SP cells contribute to cardiomyocyte labeling, which is further enhanced in response to different forms of cardiac injury. We find that cardiac SP cells fuse with preexisting cardiomyocytes, which stimulates cardiomyocyte cell cycle entry and likely proliferation, instead of directly differentiating into cardiomyocytes. Our findings provide evidence that cardiac SP cells contribute to endogenous cardiac regeneration through cardiomyocyte fusion.

cell biology↗

bioRxiv2017

DrImpute: Imputing dropout events in single cell RNA sequencing data

The single cell RNA sequencing (scRNA-seq) technique began a new era by allowing the observation of gene expression at the single cell level. However, there is also a large amount of technical and biological noise. Because of the low number of RNA transcriptomes and the stochastic nature of the gene expression pattern, there is a high chance of missing nonzero entries as zero, which are called dropout events. However, many statistical methods used for analyzing scRNA-seq data in cell type identification, visualization, and lineage reconstruction do not model for dropout events. We have developed DrImpute to impute dropout events, and it improves many of the statistical tools used for scRNA-seq analysis that do not account for dropout events. Our numerical studies with real data demonstrate the promising performance of the proposed method, which has been implemented in R.

bioinformatics↗