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

Comparing Copy Number Variations and SNPs

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

You might also read

Related Articles

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

Sort by

Same author

Evaluation of triumeq treatment on a TDP-43 mouse model of amyotrophic Lateral sclerosis.

Scientific reports·2026

Same author

Spatial Mapping of the Precancer-to-Cancer Transition in Breast and Prostate.

Cancer discovery·2026

Same author

LAML-Pro: Joint Maximum Likelihood Inference of Cell Genotypes and Cell Lineage Trees.

bioRxiv : the preprint server for biology·2026

Same author

Multimodal spatial alignment and morphology mapping with MOSAICField.

bioRxiv : the preprint server for biology·2026

Same author

Motor Neuron Disease Mortality Trends in Australia From 1986 to 2023: A Population-Based Study.

The Medical journal of Australia·2026

Same author

Genomic evolution of pancreatic cancer at single-cell resolution.

Nature genetics·2026

Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026

Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026

Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026

Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026

Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026

Same journal

Primer Design through Submodular Function Estimation.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 15, 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

Anomaly detection in spatial transcriptomics via spatially localized density comparison.

Gary Hu¹, Julian Gold², Uthsav Chitra³

¹Department of Computer Science, Princeton University, Princeton, NJ, 08540, United States.

Bioinformatics (Oxford, England)

|July 15, 2025

Summary

This summary is machine-generated.

Sardine, a new method for spatial transcriptomics, accurately identifies localized tissue changes. It reveals biologically plausible regions of altered cell states in different conditions, outperforming existing approaches.

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics

Published on: May 3, 2024

Related Experiment Videos

Last Updated: Sep 15, 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

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics

Published on: May 3, 2024

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Biological tissue perturbations alter cell composition and states, often with spatial localization.
Spatial transcriptomics technologies enable measurement of these alterations.
Current analysis methods lack spatial information or use inaccurate heuristics.

Purpose of the Study:

To introduce Sardine (Spatial Anomaly Region Detection in Expression Manifolds), a novel method for analyzing spatial transcriptomics data.
To identify and estimate spatially localized changes in cell types and states between different conditions.
To improve the accuracy and biological plausibility of detecting spatial alterations in tissues.

Main Methods:

Sardine utilizes spatially localized density estimation to compare cell states across conditions.
It estimates the probability of cell states maintaining relative spatial locations between different tissue samples.
The method is implemented in Python 3 and available as open-source software.

Main Results:

Sardine accurately recapitulates spatial patterning of expression changes on simulated data.
It identifies biologically plausible regions of spatially localized expression changes in mouse cerebral cortex and spinal cord datasets.
The method demonstrates superior performance compared to existing approaches in detecting spatial anomalies.

Conclusions:

Sardine provides an accurate and biologically relevant approach for analyzing spatially localized changes in spatial transcriptomics data.
The method enhances the understanding of tissue alterations in response to various perturbations.
Sardine offers a valuable tool for researchers studying tissue heterogeneity and disease mechanisms.