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

Vector Algebra: Method of Components

Vector Algebra: Method of Components

It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...

Deconvolution

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

You might also read

Related Articles

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

Sort by

Same author

Evaluating Cross-Platform Batch Correction Methods for Integrated Microarray and RNA-seq Data Analysis.

bioRxiv : the preprint server for biology·2026

Same author

Genetic diversity of collaborative cross mice enables the establishment of a novel <i>Chlamydia muridarum</i> female genital tract infection model.

Infection and immunity·2026

Same author

Quantitative Lesion-Level Modeling Reveals Organ-Dependent Therapeutic Response.

Clinical pharmacology and therapeutics·2026

Same author

Assessing Potential Benefit of Stroke Prophylaxis Therapy in Atrial Fibrillation and Atrial Flutter Using a Novel Competing Risk Scoring Tool.

Journal of the American Heart Association·2026

Same author

In Situ Tandem Synthesis of Charged Boron-Oxo Clusters into Highly Ordered Architectures.

Angewandte Chemie (International ed. in English)·2025

Same author

CT-based radiomics deep learning signatures for noninvasive prediction of early recurrence after radical surgery in locally advanced colorectal cancer: A multicenter study.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology·2025

Same journal

An epigenetic clock for chronological age estimation in East Asian populations.

NAR genomics and bioinformatics·2026

Same journal

The role of ATF4 in neurons under mitochondrial stress.

NAR genomics and bioinformatics·2026

Same journal

Distinct repeat architecture landscapes in the proteomes of protozoan parasites.

NAR genomics and bioinformatics·2026

Same journal

Long non-coding RNA triplex-dependent regulation of melanoma gene networks.

NAR genomics and bioinformatics·2026

Same journal

Challenges in predicting chromatin accessibility differences between species.

NAR genomics and bioinformatics·2026

Same journal

Power-law penalties correct distance bias in single-cell co-accessibility and deep-learning chromatin interaction predictions.

NAR genomics and bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 12, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

ARTdeConv: adaptive regularized tri-factor non-negative matrix factorization for cell type deconvolution.

Tianyi Liu¹, Chuwen Liu¹, Quefeng Li¹

¹Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.

NAR Genomics and Bioinformatics

|April 28, 2025

Summary

This summary is machine-generated.

This study introduces ARTdeConv, a novel method for cell type deconvolution from gene expression data. ARTdeConv accurately estimates cell proportions, outperforming existing methods and aiding disease research.

More Related Videos

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Published on: March 1, 2017

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

Related Experiment Videos

Last Updated: May 12, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Published on: March 1, 2017

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

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Accurate cell type deconvolution from bulk gene expression is vital for disease research.
Existing methods struggle with incomplete signatures, partial information, and varying mRNA amounts, biasing results.
Limited use of external reference data (e.g., population cell proportions) hinders accuracy.

Purpose of the Study:

To develop an advanced deconvolution method addressing limitations of current approaches.
To introduce ARTdeConv (adaptive regularized tri-factor non-negative matrix factorization) for robust cell type deconvolution.
To validate ARTdeConv's performance against state-of-the-art methods and in real-world applications.

Main Methods:

Developed an adaptive regularized tri-factor non-negative matrix factorization algorithm (ARTdeConv).
Established rigorous numerical convergence for the ARTdeConv algorithm.
Validated performance through benchmark simulations and real-world datasets (influenza vaccine, COVID-19).

Main Results:

ARTdeConv demonstrated superior performance over existing semi-reference-based and reference-free deconvolution methods.
The method showed robustness even when its core assumptions were challenged.
ARTdeConv estimates strongly correlated with flow cytometry measurements in a vaccine study.
Analysis of COVID-19 patient data revealed immunologically relevant patterns.

Conclusions:

ARTdeConv offers a significant advancement in cell type deconvolution from gene expression data.
The R package implementation facilitates its adoption by researchers and practitioners.
Accurate deconvolution enhances understanding of cellular dynamics in health and disease.