Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Multicompartment Models: Overview

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Semi-supervised disentangled representation learning for single-cell RNA sequencing data.

Briefings in bioinformatics·2026

Same author

Differential gene regulatory network analysis reveals transcriptional disruption in opioid.

NAR genomics and bioinformatics·2026

Same author

Integrating feature selection with unsupervised deep embedding for clustering single-cell RNA-seq data.

Briefings in bioinformatics·2026

Same author

Trust in Information Sources and COVID-19 Vaccine Uptake.

Public health challenges·2025

Same author

Single-Cell Transcriptomic Landscape Deciphers Intratumoral Heterogeneity and Subtypes of Acral and Mucosal Melanomas.

Clinical cancer research : an official journal of the American Association for Cancer Research·2025

Same author

Patients with Cervical Cancer with and without HIV Infection Have Unique T-cell Activation Profiles despite Similar Survival Outcomes after Chemoradiation.

Cancer research communications·2025

Same journal

RNAbpFlow: base pair-augmented SE(3) flow matching for conditional RNA 3D structure generation.

Nature methods·2026

Same journal

Spatio-DARLIN enables robust and efficient in situ lineage tracing in mice at single-cell resolution.

Nature methods·2026

Same journal

EasyGrid: a versatile platform for automated cryo-EM sample preparation and quality control.

Nature methods·2026

Same journal

Cloud-based microscope enables live neuroimaging for 24 h and beyond with worldwide access.

Nature methods·2026

Same journal

Deep molecular profiling in three dimensions.

Nature methods·2026

Same journal

3D pathology-guided microdissection.

Nature methods·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 13, 2026

Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

Published on: April 21, 2023

Dependency-aware deep generative models for multitasking analysis of spatial omics data.

Tian Tian^1,2, Jie Zhang³, Xiang Lin⁴

¹School of Computer Science, National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, and Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, Hubei, China.

|May 23, 2024

Summary

This summary is machine-generated.

We developed spaVAE, a novel deep learning model for analyzing spatial transcriptomics data. This method effectively captures spatial correlations and noise, improving various downstream analyses for biomedical research.

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Related Experiment Videos

Last Updated: Apr 13, 2026

Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

Published on: April 21, 2023

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Spatially resolved transcriptomics (SRT) advances biomedical research but faces analysis challenges due to data noise and spatial dependencies.
Existing methods struggle with the discrete nature of SRT count data and complex spatial relationships.

Purpose of the Study:

To introduce spaVAE, a deep generative spatial variational autoencoder model for robust SRT data analysis.
To address challenges in characterizing count data and capturing spatial correlations in SRT.

Main Methods:

Developed spaVAE, a dependency-aware variational autoencoder model.
Implemented a hybrid embedding combining Gaussian process and Gaussian priors for spatial correlations.
Optimized deep neural networks to approximate SRT data distributions.

Main Results:

spaVAE probabilistically characterizes count data while capturing spatial correlations.
The model supports diverse SRT analytical tasks: dimensionality reduction, visualization, clustering, batch integration, denoising, differential expression, spatial interpolation, resolution enhancement, and spatially variable gene identification.
Extended spaVAE to spaPeakVAE for spatial ATAC-seq and spaMultiVAE for spatial multi-omics data.

Conclusions:

spaVAE offers a powerful, flexible framework for analyzing complex spatial transcriptomics data.
The model and its extensions enhance the utility of SRT technologies in biomedical research.