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

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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

Detection of non-invasive sexing of early chick embryos in intact eggs using laser speckle contrast imaging and deep neural networks.

PloS one·2026

Same author

Perturbation of genes linked to common schizophrenia risk variants identifies cilia programs.

bioRxiv : the preprint server for biology·2026

Same author

SpatialArtifacts: a computational framework for tissue artifact detection in spatial transcriptomics data.

bioRxiv : the preprint server for biology·2026

Same author

Spatio-molecular gene expression reflects dorsal anterior cingulate cortex structure and function in the human brain.

Cell reports·2026

Same author

Generation of spinal cord organoids from human induced pluripotent stem cells caudalised to a lumbar fate.

Scientific reports·2026

Same author

Recover Biological Structure from Sparse-View Diffraction Images with Neural Volumetric Prior.

Proceedings. IEEE International Conference on Computer Vision·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 13, 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

Spatial mutual nearest neighbors for spatial transcriptomics data.

Haowen Zhou¹, Pratibha Panwar^2,3,4, Boyi Guo⁵

¹Bioinformatics and Systems Biology Graduate Program, University of California San Diego, Gilman, CA 92093, United States.

Bioinformatics (Oxford, England)

|July 27, 2025

Summary

This summary is machine-generated.

SpatialMNN integrates spatial transcriptomic data by leveraging spatial coordinates to identify similar niches across samples. This method enhances batch correction and spatial domain prediction in tissues.

More Related Videos

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

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Published on: May 16, 2025

Related Experiment Videos

Last Updated: Sep 13, 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

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

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Published on: May 16, 2025

Area of Science:

Computational biology
Bioinformatics
Genomics

Background:

Mutual nearest neighbors (MNN) is a standard computational tool for single-cell RNA-sequencing batch correction.
Traditional MNN methods do not incorporate spatial information, limiting their application in spatial transcriptomics.

Purpose of the Study:

To develop a novel algorithm, spatialMNN, for integrating multiple spatial transcriptomic samples.
To identify and analyze spatial domains within tissues by accounting for 2D spatial information.

Main Methods:

Constructs a k-nearest neighbors (kNN) graph using spatial coordinates.
Identifies 'niches' as anchor points by pruning noisy edges.
Builds a MNN graph across samples to find similar niches.
Partitions the spatialMNN graph using algorithms like Louvain for spatial domain prediction.

Main Results:

spatialMNN successfully integrates multiple spatial transcriptomic samples.
The algorithm identifies spatial domains across tissue samples.
Demonstrated performance on large datasets, including 31 10x Genomics Visium samples.
Evaluated computing performance against other spatial clustering methods.

Conclusions:

spatialMNN provides an effective approach for batch correction and spatial domain identification in spatial transcriptomics.
The method accounts for crucial 2D spatial information, outperforming traditional MNN approaches in this context.