Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 4, 2026

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
06:24

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

scMEDAL: interpretable single-cell transcriptomics analysis with batch effect visualization via deep mixed-effects

Aixa X Andrade1, Son N Nguyen1, Austin Marckx1

  • 1Lyda Hill Department of Bioinformatics University of Texas Southwestern Medical Center, Dallas, TX, USA.

Nature Communications
|June 2, 2026
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Self-nanonizing gelatin oleyl conjugate solid dispersions for enhanced solubility and permeability of tetrabenazine.

International journal of pharmaceutics·2026
Same author

Activation of the ciliary kinase CDKL5 is mediated by the cyclin-dependent kinase CDK20/LF2 to control flagellar length.

PLoS biology·2025
Same author

The Role of Long-Range Non-Specific Electrostatic Interactions in Inhibiting the Pre-Fusion Proteolytic Processing of the SARS-CoV-2 S Glycoprotein by Heparin.

Biomolecules·2025
Same author

<i>Xist</i> Repeat A coordinates an assembly of SR proteins to recruit SPEN and induce gene silencing.

bioRxiv : the preprint server for biology·2025
Same author

Rapid and high-yield recovery of plasma-derived extracellular vesicles using modified chromatography with soluble protein depletion for biomarker discovery.

Cell communication and signaling : CCS·2025
Same author

Author Correction: Mitochondrial NADPH fuels mitochondrial fatty acid synthesis and lipoylation to power oxidative metabolism.

Nature cell biology·2025
Same journal

Demonstration of a quantum C-NOT gate in a time-multiplexed fully reconfigurable photonic processor.

Nature communications·2026
Same journal

Nonlinear quantum light source with van der Waals ferroelectric NbOX<sub>2</sub> (X = Br, I).

Nature communications·2026
Same journal

Antagonistic histone H2A variants and autonomous heterochromatin formation shape epigenomic patterns in Arabidopsis.

Nature communications·2026
Same journal

The long tail of nitrate pollution in groundwater challenges governance of global water quality.

Nature communications·2026
Same journal

Select microbial metabolites promote tau aggregation in a murine tauopathy model.

Nature communications·2026
Same journal

Warming climate has lengthened global intense tropical cyclone seasons.

Nature communications·2026
See all related articles
This summary is machine-generated.

scMEDAL, a new framework, models batch effects in single-cell RNA sequencing data. It separates batch-invariant and batch-specific variations, preserving biological signals often lost in standard correction methods.

Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNA-seq) offers high-resolution cellular heterogeneity analysis.
  • Disentangling biological signals from technical batch effects remains a significant challenge in scRNA-seq data analysis.
  • Current batch-correction algorithms often suppress or discard batch-related variation, potentially losing valuable biological information.

Purpose of the Study:

  • To introduce scMEDAL (single-cell Mixed Effects Deep Autoencoder Learning), a novel framework for modeling batch effects in scRNA-seq data.
  • To develop a method that separately models batch-invariant and batch-specific effects, preserving biologically meaningful signals.
  • To provide interpretable, batch-specific representations and generative visualizations for enhanced data analysis.

More Related Videos

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Related Experiment Videos

Last Updated: Jun 4, 2026

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
06:24

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Main Methods:

  • Proposed scMEDAL, a framework utilizing a random-effects Bayesian autoencoder (scMEDAL-RE).
  • scMEDAL-RE independently models batch-invariant and batch-specific effects.
  • The framework generates interpretable, batch-specific embeddings and counterfactual reconstructions.

Main Results:

  • scMEDAL-RE successfully learns batch-specific representations while preserving biologically meaningful information.
  • The method demonstrated improved prediction of disease status, donor group, and tissue across diverse datasets (autism, leukemia, cardiovascular).
  • Generative visualizations, including counterfactual reconstructions, offer novel insights into cellular expression patterns across batches.

Conclusions:

  • scMEDAL is a versatile and interpretable framework that complements existing batch correction methods.
  • The approach provides deeper insights into cellular heterogeneity and data acquisition effects.
  • scMEDAL enhances the analysis of scRNA-seq data by effectively modeling and visualizing batch-specific variations.