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 Experiment Video

Updated: May 30, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Automatic annotation of spatial expression patterns via sparse Bayesian factor models.

Iulian Pruteanu-Malinici1, Daniel L Mace, Uwe Ohler

  • 1Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina, United States of America.

Plos Computational Biology
|August 5, 2011
PubMed
Summary
This summary is machine-generated.

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

Expanding the human proteome with microproteins and peptideins.

Nature·2026
Same author

Insights, opportunities, and challenges provided by large cell atlases.

Genome biology·2025
Same author

Sensitive dissection of a genomic regulatory landscape using bulk and targeted single-cell activation.

Cell genomics·2025
Same author

Author Correction: A critical period of translational control during brain development at codon resolution.

Nature structural & molecular biology·2025
Same author

Shortcomings of silhouette in single-cell integration benchmarking.

Nature biotechnology·2025
Same author

Transposon mediated functional genomic screening for BRAF inhibitor resistance reveals convergent Hippo and MAPK pathway activation events.

Scientific reports·2025
Same journal

Another 10 years of PLOS Computational Biology: A data-driven reflection on trends in genomics research.

PLoS computational biology·2026
Same journal

Mobility data resolution needed to inform predictive models of spatial epidemic spread from mobile phone data.

PLoS computational biology·2026
Same journal

DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.

PLoS computational biology·2026
Same journal

Redefining and estimating the early-phase reproduction ratio for epidemic outbreaks in spatially structured populations.

PLoS computational biology·2026
Same journal

Optimized phenotype definitions boost GWAS power.

PLoS computational biology·2026
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
See all related articles

We developed a new computational model to analyze gene expression images, revealing hidden biological functions. This method accurately annotates gene expression patterns, offering insights into developmental biology.

Area of Science:

  • Computational Biology
  • Developmental Biology
  • Genomics

Background:

  • Gene expression analysis has advanced with imaging techniques, providing high spatial resolution data.
  • Current computational methods struggle to efficiently analyze the rapidly growing volume of high-dimensional gene expression image data.
  • Image-based gene expression data offers superior spatial dynamics compared to lower-resolution microarray data.

Purpose of the Study:

  • To develop a computational framework for analyzing and annotating large-scale gene expression image datasets.
  • To infer hidden biological factors from diverse gene expression patterns.
  • To improve automated annotation of gene expression patterns using a novel modeling approach.

Main Methods:

  • Developed a sparse Bayesian factor analysis model to identify common factors in high-dimensional gene expression images.

More Related Videos

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
08:51

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Related Experiment Videos

Last Updated: May 30, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
08:51

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

  • Applied the model to a Drosophila embryonic expression pattern database.
  • Utilized factor mixing weights as features for a classification model to annotate expression patterns.

    • Main Results:

    • The inferred factors provide meaningful decomposition of gene expression data, representing co-regulation and biological functions.
    • The sparse Bayesian factor analysis model achieved comparable or superior classification accuracy for gene expression patterns.
    • The method effectively annotates expression patterns across different developmental stages using fewer features than existing methods.

    Conclusions:

    • The developed sparse Bayesian factor analysis model offers a generalizable framework for analyzing large microscopy datasets.
    • This approach provides biological insights through both generative modeling and automated annotation tasks.
    • The study highlights the potential of computational methods for deciphering complex gene expression patterns in developmental biology.