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 8, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

UNLOCKING MULTI-SAMPLE DIFFERENTIAL EXPRESSION FOR SPATIAL TRANSCRIPTOMICS DATA WITH TESSERA.

Florica Constantine1, Zoltan Laszik2, Sandrine Dudoit3

  • 1Department of Statistics, University of California, Berkeley.

Biorxiv : the Preprint Server for Biology
|May 7, 2026
PubMed
Summary

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

Molecular Reactivity in Maternal Pregnancy Blood and Neonatal Dried Blood Spots Is Associated with the Risk of Pediatric Acute Lymphoblastic Leukemia.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2025
Same author

Holo-omics disentangle drought response and biotic interactions among plant, endophyte and pathogen.

The New phytologist·2025
Same author

HSA Adductomics in the Shanghai Women's Health Study Links Lung Cancer in Never-Smokers with Air Pollution, Redox Biology, and One-Carbon Metabolism.

Antioxidants (Basel, Switzerland)·2025
Same author

Visualizing scRNA-Seq data at population scale with GloScope.

Genome biology·2024
Same author

Tissue-based T cell activation and viral RNA persist for up to 2 years after SARS-CoV-2 infection.

Science translational medicine·2024
Same author

Trajectory inference across multiple conditions with condiments.

Nature communications·2024
Same journal

Layered social competition coordinates reproductive hierarchy formation in ants.

bioRxiv : the preprint server for biology·2026
Same journal

Combination epigenetic-targeted therapy increases the immunogenicity of poorly immunogenic sarcomas.

bioRxiv : the preprint server for biology·2026
Same journal

Loss of LanC-like proteins delays post-injury regeneration of aging skeletal muscles.

bioRxiv : the preprint server for biology·2026
Same journal

Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets.

bioRxiv : the preprint server for biology·2026
Same journal

Confidence-supported label-free metabolic imaging with FPhaS phase autofluorescence microscopy.

bioRxiv : the preprint server for biology·2026
Same journal

Sequence-encoded autoinhibition couples mRNA decapping activity to phase separation.

bioRxiv : the preprint server for biology·2026
See all related articles
This summary is machine-generated.

We developed TESSERA, a new spatial generalized linear model for analyzing multi-sample spatial transcriptomics data. This method efficiently handles variations across samples, enabling robust differential gene expression analysis.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Spatial transcriptomics offers high-resolution gene expression analysis.
  • Multi-sample studies present challenges for traditional spatial statistics due to sample variability.
  • Existing methods struggle with diverse coordinate systems, cell types, and tissue architectures across samples.

Purpose of the Study:

  • To introduce TESSERA, a novel method for analyzing multi-sample spatial transcriptomics count data.
  • To provide a scalable computational framework for model fitting and statistical inference.
  • To enable differential gene expression analysis across multiple samples while accounting for spatial correlation.

Main Methods:

  • A spatial generalized linear model framework is proposed.
Keywords:
Differential expressionGeneralized linear mixed modelGeneralized linear spatial modelMulti-sample analysisScalable inferenceSpatial statisticsSpatial transcriptomics

More Related Videos

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

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
10:22

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

Related Experiment Videos

Last Updated: May 8, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 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

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
10:22

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

  • A mathematical and computational framework for efficient model fitting and inference is developed.
  • The method estimates common fixed effects across samples for robust analysis.

    • Main Results:

    • TESSERA successfully extends analysis to the multi-sample setting.
    • The method demonstrates competitive or superior performance compared to existing algorithms in single-sample scenarios.
    • Benchmarking on simulated data and application to human kidney samples validate the approach.

    Conclusions:

    • TESSERA provides a crucial advancement for multi-sample spatial transcriptomics research.
    • The method facilitates robust identification of differentially expressed genes across conditions.
    • This work offers a scalable and efficient solution for complex spatial transcriptomics data analysis.