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

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

Construction of a public health emergency information system framework: A case study of Zhuhai city, China.

PloS one·2026

Same author

Differential nucleosome organization in human interphase and metaphase chromosomes.

Molecular systems biology·2026

Same author

Robust Detection of Watermarks for Large Language Models Under Human Edits.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026

Same author

Differential nucleosome organization in human interphase and metaphase chromosomes.

bioRxiv : the preprint server for biology·2025

Same author

A Statistical Framework of Watermarks for Large Language Models: Pivot, Detection Efficiency and Optimal Rules.

Annals of statistics·2025

Same author

DDX6 interacts with DDX3X to repress translation in microRNA-mediated silencing.

Nucleic acids research·2025

Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026

Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026

Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026

Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026

Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026

Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 2, 2026

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

NLSDeconv: an efficient cell-type deconvolution method for spatial transcriptomics data.

Yunlu Chen¹, Feng Ruan¹, Ji-Ping Wang¹

¹Department of Statistics and Data Science, Northwestern University, Evanston, IL 60208, United States.

Bioinformatics (Oxford, England)

|December 20, 2024

Summary

This summary is machine-generated.

A new method called NLSDeconv enhances spatial transcriptomics (ST) by accurately estimating cell types. This computational tool offers superior performance and efficiency compared to existing deconvolution methods.

More Related Videos

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

Related Experiment Videos

Last Updated: May 2, 2026

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

Area of Science:

Computational biology
Genomics
Bioinformatics

Background:

Spatial transcriptomics (ST) provides gene expression data in intact tissues.
ST lacks single-cell resolution, requiring computational deconvolution.
Accurate cell-type deconvolution is crucial for interpreting ST data.

Purpose of the Study:

Introduce NLSDeconv, a novel non-negative least squares-based deconvolution method.
Develop an accompanying Python package for NLSDeconv.
Evaluate NLSDeconv's performance against existing methods.

Main Methods:

Developed NLSDeconv using non-negative least squares.
Implemented NLSDeconv as a Python package.
Benchmarked NLSDeconv against 18 other deconvolution methods on diverse ST datasets.

Main Results:

NLSDeconv demonstrated competitive statistical performance.
NLSDeconv exhibited superior computational efficiency.
The method proved effective across various spatial transcriptomics datasets.

Conclusions:

NLSDeconv is a highly efficient and accurate tool for cell-type deconvolution in spatial transcriptomics.
The NLSDeconv Python package provides a valuable resource for researchers.
This method advances the analysis of spatial transcriptomics data.