Biology subjects

Honcharuk, V.

Publications and source records attributed to Honcharuk, V..

3 recordsLinked to original sources

bioRxiv2026

DeepSpaceDB 2.0: an interactive spatial transcriptomics database for large-scale Xenium data exploration

The 10x Genomics Xenium platform enables high-resolution spatial transcriptomics at single-cell and subcellular scales, but effective reuse of public Xenium datasets is hindered by large data sizes and heterogeneous file formats. We previously developed DeepSpaceDB, a spatial transcriptomics database designed for interactive, in-depth analysis of tissues and tissue microenvironments. Here, we present a major expansion of DeepSpaceDB that integrates large-scale single-cell spatial transcriptomics data generated by the Xenium platform. In this update, we systematically collected 1,539 public Xenium datasets from multiple repositories and processed them through a robust, standardized pipeline that validates, repairs, and harmonizes heterogeneous inputs into a unified representation. To support efficient exploration of these data, we introduced a redesigned DeepSpaceDB interface and complementary Zarr-based storage formats optimized for gene-centric visualization and spatially localized queries, enabling sub-second response times for common interactive operations. The updated platform supports real-time visualization of spatial data and analysis of regions of interest directly in the web browser. Together, this expansion establishes DeepSpaceDB as a unified resource for single-cell spatial transcriptomics, substantially lowering the barrier to accessing, exploring, and reusing large-scale public Xenium datasets.

bioinformatics↗

bioRxiv2025

oCELLoc: Automated Cell Type Assignment in Transcriptomics Data Using Reference Filtering

Interpreting single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data requires accurate cell-type prediction, which strongly depends on the quality of the reference used. However, prediction accuracy is highly dependent on reference quality; missing relevant cell types or including irrelevant ones can substantially impair performance. To address this challenge, we developed oCELLoc, a regression-based method that selects the most appropriate reference cell types from a large atlas and tailors them to each new sample. oCELLoc takes pseudobulk gene expression from ST or scRNA-seq data together with a broad reference matrix and uses regularized regression with cross-validation to identify a limited number of essential cell types. We applied oCELLoc to toy datasets, scRNA-seq data, and 2,144 Visium samples across diverse tissues and conditions, demonstrating that using the filtered cell types leads to more biologically meaningful downstream predictions. oCELLoc is available as an R package on GitHub and CRAN.

bioinformatics↗

bioRxiv2025

DeepSpaceDB: a spatial transcriptomics atlas for interactive in-depth analysis of tissues and tissue microenvironments

Spatial transcriptomics provides a revolutionary approach to mapping gene expression within tissues, offering critical insights into the spatial organization of cellular and molecular processes. However, generating new spatial transcriptomics data is expensive and technically demanding, and analyzing such data requires advanced bioinformatics expertise. While publicly available datasets are growing rapidly, existing databases offer limited tools for interactive exploration and cross-sample comparisons. Here, we introduce DeepSpaceDB, a next-generation spatial transcriptomics database designed to address these issues. DeepSpaceDB focuses on interactivity and advanced analytical functionality, enabling users to explore spatial transcriptomics data with unprecedented flexibility. DeepSpaceDB allows for interactive selection and comparison of gene expression across regions within a single tissue slice or between slices, such as comparing hippocampal regions of an Alzheimers model mouse and a control. It also includes quality indicators, database-wide trends, and advanced visualizations that provide real-time interactivity, such as zoomable plots and hover-based information display. Moreover, these functions are not restricted to the samples collected in our database but can also be applied to samples uploaded by users. The current version of DeepSpaceDB focuses explicitly on samples of the 10X Genomics Visium platform, ensuring higher-quality analyses and enhanced exploration tools, including comparison between interactively selected regions of tissue sections. This tradeoff enables unique features like similarity-based sample embeddings and database-wide comparisons, setting it apart from other databases prioritizing broad platform coverage over functionality. With its combination of advanced tools and interactive capabilities, DeepSpaceDB represents a transformative resource for spatial transcriptomics research, paving the way for deeper insights into tissue organization and disease biology. Availability: DeepSpaceDB is available at www.deepspacedb.com.

bioinformatics↗