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

Tau368 improves p-tau diagnostic accuracy for FTLD-tau from FTLD-TDP.

Acta neuropathologica·2026

Same author

Characterizing the MRI signature of hippocampal sclerosis of aging.

Alzheimer's research & therapy·2026

Same author

Contributions of the Alzheimer's Disease Neuroimaging Initiative to advancing AD research: a targeted review of recent publications.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026

Same author

Clinicoanatomic localization of iron-rich gliosis in aphasic presentations of globular glial tauopathy.

Brain communications·2026

Same author

Pan-cancer spatial atlas of tertiary lymphoid structures.

Science (New York, N.Y.)·2026

Same author

Neocortical tau burden determines the degree of cognitive impairment in individuals with Braak stage V neurofibrillary degeneration.

Acta neuropathologica·2026

Same journal

Ciliary flow and morphology shape mass transport at the surface and within gastrovascular cavities of black corals.

Communications biology·2026

Same journal

Virus-mediated prokaryotic community adaptation dynamics under thermal stress in municipal organic solid waste microbiomes.

Communications biology·2026

Same journal

Multi-omics insights into the woolly trait of Saussurea medusa and the plant's coordinated regulation of flavonoid biosynthesis.

Communications biology·2026

Same journal

Loss contexts enhance dorsolateral prefrontal interpersonal neural synchrony during successful human deceptive recommendations.

Communications biology·2026

Same journal

Neuro-regulator role of H<sub>2</sub>S in astrocyte activation and its effects on neurological damage and behavior of VPA-exposed rats.

Communications biology·2026

Same journal

Temporal orchestration of transcriptional and epigenomic programming underlying maternal embryonic diapause in a cricket model.

Communications biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 3, 2025

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

SpaDecon: cell-type deconvolution in spatial transcriptomics with semi-supervised learning.

Kyle Coleman¹, Jian Hu², Amelia Schroeder²

¹Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA. kylecole@pennmedicine.upenn.edu.

Communications Biology

|April 7, 2023

Summary

This summary is machine-generated.

SpaDecon, a new semi-supervised method, accurately deconvolutes cell types in spatially resolved transcriptomics (SRT) data. It integrates gene expression, spatial, and histology information for improved spatial transcriptomics analysis.

More Related Videos

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

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: Aug 3, 2025

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

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

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:

Spatially resolved transcriptomics (SRT) reveals gene expression patterns in tissue context.
Current SRT methods lack single-cell resolution, limiting precise cell-type localization.
Accurate cell-type deconvolution is crucial for understanding tissue architecture.

Purpose of the Study:

To develop and validate SpaDecon, a novel semi-supervised method for cell-type deconvolution in SRT data.
To enhance the inference of individual cell locations and distributions within spatial transcriptomics datasets.
To improve the integration of genomics and digital pathology through accurate spatial cell-type mapping.

Main Methods:

SpaDecon employs a semi-supervised learning approach.
It integrates gene expression, spatial location, and histology data for deconvolution.
The method was validated on real and pseudo-SRT datasets with known cell-type distributions.

Main Results:

SpaDecon accurately deconvolutes cell types in SRT data, outperforming existing methods.
Quantitative evaluations using mean squared error and Jensen-Shannon divergence demonstrated superior performance.
The method shows high accuracy and computational efficiency.

Conclusions:

SpaDecon provides accurate cell-type deconvolution for SRT data, overcoming single-cell resolution limitations.
Its performance and speed make it a valuable tool for spatial transcriptomics analysis.
SpaDecon facilitates the integration of genomics with digital pathology for deeper biological insights.