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

You might also read

Related Articles

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

Sort by
Same author

Enhanced positronium lifetime imaging through two-component reconstruction in time-of-flight positron emission tomography.

Frontiers in physics·2026
Same author

The properties of the positronium lifetime image reconstruction based on maximum likelihood estimation.

Bio-algorithms and med-systems·2026
Same author

Dissolution and stability of vigabatrin powder in water, fruit juice, milk, and infant formula.

Epilepsy & behavior reports·2025
Same author

PhyImpute and UniFracImpute: two imputation approaches incorporating phylogeny information for microbial count data.

Briefings in bioinformatics·2024
Same author

TimeNorm: a novel normalization method for time course microbiome data.

Frontiers in genetics·2024
Same author

Transcriptomic classification of diffuse large B-cell lymphoma identifies a high-risk activated B-cell-like subpopulation with targetable MYC dysregulation.

Nature communications·2024
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: May 29, 2025

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

4.8K

Flexible analysis of spatial transcriptomics data (FAST): a deconvolution approach.

Meng Zhang1, Joel Parker2, Lingling An3

  • 1Department of Mathematics, University of Arizona, 617 N. Santa Rita Ave., Tucson, AZ, 85721, USA.

BMC Bioinformatics
|January 31, 2025
PubMed
Summary
This summary is machine-generated.

We developed FAST, a novel reference-free method for spatial transcriptomics deconvolution. This flexible tool integrates gene expression, spatial, and histology data to accurately identify cell types without needing reference datasets.

Keywords:
DeconvolutionNon-negative matrix factorizationReference-freeSpatial transcriptomics

More Related Videos

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level
09:45

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

Published on: March 14, 2022

2.9K
Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
09:58

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

2.7K

Related Experiment Videos

Last Updated: May 29, 2025

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

4.8K
Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level
09:45

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

Published on: March 14, 2022

2.9K
Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
09:58

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

2.7K

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Spatial transcriptomics enables gene expression analysis within tissue spatial domains.
  • Technical limitations result in bulk data per sequencing spot, necessitating deconvolution for high-resolution data.
  • Existing deconvolution methods often require reference data or have limitations in reference-free approaches.

Purpose of the Study:

  • To develop a flexible, robust, and user-friendly reference-free deconvolution method for spatial transcriptomics data.
  • To address the limitations of current reference-free methods by integrating multiple data types and spatial information.

Main Methods:

  • Proposed a novel reference-free deconvolution method named Flexible Analysis of Spatial Transcriptomics (FAST).
  • Employed regularized non-negative matrix factorization (NMF) as the core analytical framework.
  • Unified gene expression, spatial, and histology information within a single deconvolution model.

Main Results:

  • FAST imposes fewer distribution assumptions and leverages tissue spatial structure for improved accuracy.
  • Simulation studies demonstrated that FAST outperforms existing reference-free deconvolution methods.
  • Real data applications successfully revealed underlying tissue structures and identified corresponding marker genes.

Conclusions:

  • FAST provides a flexible and accurate tool for deciphering complex cell-type composition in spatial transcriptomics.
  • The method enhances the understanding of biological processes and diseases by enabling detailed spatial analysis.
  • FAST offers a valuable alternative for spatial transcriptomics deconvolution when reference data is unavailable.