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

Search research articles

Related Experiment Video

Updated: Nov 19, 2025

Reusable Single Cell for Iterative Epigenomic Analyses

Reusable Single Cell for Iterative Epigenomic Analyses

Published on: February 11, 2022

A practical solution to pseudoreplication bias in single-cell studies.

Kip D Zimmerman^1,2, Mark A Espeland³, Carl D Langefeld^4,5,6

¹Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA. kdzimmer@wakehealth.edu.

Nature Communications

|February 3, 2021

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

Association between renin angiotensin system and cognitive outcomes over 15 years: The Look AHEAD study.

Journal of Alzheimer's disease : JAD·2026

Same author

Air pollution is linked to divergent cortical thickness patterns in brain regions vulnerable to Alzheimer's disease.

Neurotoxicology·2026

Same author

Identification of Frailty Subtypes in African American Adults With and Without Type 2 Diabetes: A Latent Class Analysis From the Jackson Heart Study.

Diabetes, obesity & metabolism·2026

Same author

Brain Imaging Biomarkers and Cognitive Outcomes in a Multidomain Lifestyle Intervention: The POINTER Imaging Ancillary Study.

JAMA neurology·2026

Same author

Association of epigenetic age acceleration with MRI biomarkers of aging and Alzheimer's disease neurodegeneration.

Aging·2026

Same author

Relative impact of multidomain lifestyle interventions on deficit accumulation frailty over 24 months in the U.S. POINTER trial.

The journals of gerontology. Series A, Biological sciences and medical sciences·2026

Same journal

Chlorinated VSLSs Surpass HCFCs in CFC-11-Equivalent Emissions for Ozone Layer Depletion in China.

Nature communications·2026

Same journal

Author Correction: Charge transfer in triphenylamine-tetrazine covalent organic frameworks for solar-driven hydrogen peroxide production.

Nature communications·2026

Same journal

Vegetation browning patterns under compound soil and atmospheric dryness in northern permafrost ecosystems.

Nature communications·2026

Same journal

Voltage imaging of CA1 pyramidal cells and SST+ interneurons reveals stability and plasticity mechanisms of spatial firing.

Nature communications·2026

Same journal

Radical-omics reveals the hydrogen-abstraction pathway of isoprene oxidation.

Nature communications·2026

Same journal

Toughening elastomer via sequentially activated multi-pathway energy dissipation.

Nature communications·2026

See all related articles

This summary is machine-generated.

Single-cell data analysis requires accounting for non-independent cells within individuals. Generalized linear mixed models (GLMMs) offer a robust solution, improving statistical power and reproducibility in cell type differential expression studies.

Area of Science:

Genomics
Bioinformatics
Statistical Biology

Background:

Single-cell data exhibits a hierarchical structure due to cells from the same individual being non-independent.
Current single-cell analysis methods often fail to address this hierarchical structure, leading to biased inference and inflated error rates.
Existing methods like batch effect correction for individuals do not adequately account for within-sample correlation.

Purpose of the Study:

To document the dependence within single-cell data across various cell types.
To compare the performance of pseudo-bulk aggregation methods with mixed models.
To propose a robust statistical framework for differential expression analysis in single-cell studies.

Main Methods:

Documented cell dependence across diverse cell types.

More Related Videos

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Profiling Individual Human Embryonic Stem Cells by Quantitative RT-PCR

Profiling Individual Human Embryonic Stem Cells by Quantitative RT-PCR

Published on: May 29, 2014

Related Experiment Videos

Last Updated: Nov 19, 2025

Reusable Single Cell for Iterative Epigenomic Analyses

Reusable Single Cell for Iterative Epigenomic Analyses

Published on: February 11, 2022

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Profiling Individual Human Embryonic Stem Cells by Quantitative RT-PCR

Profiling Individual Human Embryonic Stem Cells by Quantitative RT-PCR

Published on: May 29, 2014

Compared pseudo-bulk aggregation with generalized linear mixed models (GLMMs).

Proposed applying GLMMs with a random effect for individual to account for zero inflation and within-individual correlation.

Main Results:

Pseudo-bulk aggregation methods are conservative and underpowered compared to mixed models.
GLMMs properly account for the hierarchical structure of single-cell data.
The proposed method improves robustness and reproducibility in differential expression analysis.

Conclusions:

Generalized linear mixed models are essential for accurate single-cell data analysis.
Accounting for within-individual correlation is critical for robust differential expression inference.
The study provides power estimates to aid in designing future single-cell experiments.