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

Genomics

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

Proteomics

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...

You might also read

Related Articles

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

Sort by

Same author

Efficacy of psychological interventions for adolescents with borderline personality disorder: a systematic review and meta-analysis.

Frontiers in psychiatry·2026

Same author

A cross-sectional study of oxidative stress pathway genotypes and their interactions with environmental pollutant levels identifies associations with gene expression and lung function.

EBioMedicine·2026

Same author

A rhizobium-induced FT-FD module locally activates nodule stem cell gene to promote nodulation.

Nature communications·2026

Same author

Harnessing Artificial Intelligence for Hypothesis Generation in Childhood Asthma: Insights from NHANES.

Journal of respiratory biology and translational medicine·2026

Same author

eQTM (expression quantitative trait methylation) Atlas: a comprehensive resource of over 11 million DNA methylation-gene expression associations through across 11 tissues and 4 diseases.

bioRxiv : the preprint server for biology·2026

Same author

Depression and asthma across the lifespan.

American journal of respiratory and critical care medicine·2026

Same journal

Variable Selection with FDR Control for Noisy Data - An Application to Screening Metabolites that Are Associated with Breast Cancer and Colorectal Cancer.

Journal of data science : JDS·2026

Same journal

Quantifying Direct and Indirect Effects through Joint Modeling of Terminal Events and Gap Times between Recurrent Events.

Journal of data science : JDS·2026

Same journal

Neural Network for Correlated Survival Outcomes Using Frailty Model.

Journal of data science : JDS·2026

Same journal

Magnitude Pruning of Large Pretrained Transformer Models with a Mixture Gaussian Prior.

Journal of data science : JDS·2025

Same journal

A Meta-Learner Framework to Estimate Individualized Treatment Effects for Survival Outcomes.

Journal of data science : JDS·2025

Same journal

An Innovative Method of Singular Spectrum Analysis to Conduct Gap-filling and Denoising on Time Series Data.

Journal of data science : JDS·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 14, 2026

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

EMixed: Probabilistic Multi-Omics Cellular Deconvolution of Bulk Omics Data.

Manqi Cai¹, Kangyi Zhao², Penghui Huang¹

¹Department of Biostatistics and Health Data Science, University of Pittsburgh, USA.

Journal of Data Science : JDS

|October 20, 2025

Summary

This summary is machine-generated.

EMixed enhances cellular deconvolution by integrating multiple data types. This new framework improves cell type inference from bulk tissue data, offering deeper insights into cellular heterogeneity.

Keywords:

DNA methylation EM algorithm cellular deconvolution gene expression latent Dirichlet allocation multi-omics

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

A Multi-Omics Extraction Method for the In-Depth Analysis of Synchronized Cultures of the Green Alga Chlamydomonas reinhardtii

A Multi-Omics Extraction Method for the In-Depth Analysis of Synchronized Cultures of the Green Alga Chlamydomonas reinhardtii

Published on: August 8, 2019

Related Experiment Videos

Last Updated: Jan 14, 2026

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

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

A Multi-Omics Extraction Method for the In-Depth Analysis of Synchronized Cultures of the Green Alga Chlamydomonas reinhardtii

A Multi-Omics Extraction Method for the In-Depth Analysis of Synchronized Cultures of the Green Alga Chlamydomonas reinhardtii

Published on: August 8, 2019

Area of Science:

Computational biology
Genomics
Epigenetics

Background:

Cellular deconvolution infers cell type composition from bulk tissue data.
Traditional methods use single molecular modalities like RNA sequencing (RNA-seq) or DNA methylation (DNAm).
Single-modality approaches have limitations in fully understanding complex tissue cellularity.

Purpose of the Study:

Introduce EMixed, a versatile framework for single-modality and multi-omics cellular deconvolution.
Address limitations of existing methods for inferring cellular composition.
Enhance understanding of cellular heterogeneity in complex tissues.

Main Methods:

EMixed models raw RNA counts and DNA methylation (DNAm) counts or frequencies.
Utilizes allocation models to assign RNA transcripts and DNAm reads to specific cell types.
Employs an expectation-maximization (EM) algorithm for parameter estimation.

Main Results:

EMixed demonstrates superior performance in cellular deconvolution tasks.
Outperforms existing methods in both single-modality and multi-modality applications.
Validates the broad utility of multi-omics integration for cellular composition analysis.

Conclusions:

EMixed offers a robust framework for cellular deconvolution.
Multi-omics data integration significantly improves the accuracy of cell type inference.
The approach enhances the study of cellular heterogeneity in biological systems.