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 Concept Videos

Deconvolution01:20

Deconvolution

715
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...
715

You might also read

Related Articles

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

Sort by
Same author

Mavacamten shows broad benefit in human and mouse models of MYBPC3-related hypertrophic cardiomyopathy.

Nature cardiovascular research·2026
Same author

Comparison of cardiac regeneration capacity between zebrafish and medaka reveals a regenerative response in both teleost species.

NPJ Regenerative medicine·2026
Same author

Metabolic support of trained immune responses in myeloid cells.

eLife·2026
Same author

Mitochondria directly interact with the nuclear pore complex.

Nature·2026
Same author

Activation of HIF2 in Cardiac Vasculature Leads to Arterial Remodeling, Dilation, Thrombosis, and Inflammation, Recapitulating Cardiac Involvement in Kawasaki Disease.

Circulation·2026
Same author

A signal-responsive cooperative transcription factor network determines alveolar macrophage identity.

The Journal of experimental medicine·2026

Related Experiment Video

Updated: Apr 12, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K

SpatialDDLS: an R package to deconvolute spatial transcriptomics data using neural networks.

Diego Mañanes1, Inés Rivero-García1,2, Carlos Relaño1

  • 1Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), 28029 Madrid, Spain.

Bioinformatics (Oxford, England)
|February 17, 2024
PubMed
Summary
This summary is machine-generated.

SpatialDDLS is a new algorithm that uses neural networks to analyze spatial transcriptomics data, improving cell type deconvolution for better tissue analysis. This fast and accurate tool enhances understanding of cellular organization in tissues.

More Related Videos

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.5K
Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

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

Published on: July 6, 2022

4.9K

Related Experiment Videos

Last Updated: Apr 12, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K
Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.5K
Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

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

Published on: July 6, 2022

4.9K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Spatial transcriptomics offers insights into tissue structure and cellular organization.
  • Current platforms often lack single-cell resolution, limiting detailed analysis.
  • Accurate cell type deconvolution is crucial for interpreting spatial transcriptomics data.

Purpose of the Study:

  • To introduce SpatialDDLS, a novel algorithm for cell type deconvolution in spatial transcriptomics.
  • To develop a fast and accurate method that overcomes resolution limitations of existing platforms.
  • To enable a deeper understanding of cellular diversity within tissue microenvironments.

Main Methods:

  • Developed SpatialDDLS, a neural network-based algorithm for spatial transcriptomics deconvolution.
  • Utilized single-cell RNA sequencing data to simulate mixed transcriptional profiles.
  • Trained a fully connected neural network to identify cell types within spatial 'spots'.

Main Results:

  • SpatialDDLS demonstrates high accuracy in cell type deconvolution.
  • The algorithm is significantly faster than existing state-of-the-art methods.
  • Comparative analysis validates SpatialDDLS as a robust alternative for spatial transcriptomics data.

Conclusions:

  • SpatialDDLS provides an accurate and efficient solution for cell type deconvolution in spatial transcriptomics.
  • The tool addresses the need for higher resolution analysis in studying tissue organization.
  • SpatialDDLS is a valuable addition to the computational biology toolkit for spatial omics research.