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

Random Sampling Method

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...

You might also read

Related Articles

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

Sort by

Same author

Autoencoder Denoising for Network-Based Spatial Transcriptomics Data with Applications for Cell Signaling Estimation.

Complex networks & their applications. International Conference on Complex Networks and Their Applications·2026

Same author

Biological foundation models illuminate annotation blind spots in evolutionarily divergent genomes.

bioRxiv : the preprint server for biology·2026

Same author

Eigenvector centrality for multilayer networks with dependent node importance.

Complex networks & their applications. International Conference on Complex Networks and Their Applications·2026

Same author

Benchmarking sketching methods on spatial transcriptomics data.

Nucleic acids research·2026

Same author

Analysis of biological networks using Krylov subspace trajectories.

bioRxiv : the preprint server for biology·2026

Same author

A Composite interaction Score: Prioritizing cell-cell interactions from single-cell RNA-seq with application to pre-menopausal epithelial barriers.

Journal of advanced research·2026

Same journal

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

PLoS computational biology·2026

Same journal

Exploring the structural lexicon of the Proteome via Metric Geometry.

PLoS computational biology·2026

Same journal

Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.

PLoS computational biology·2026

Same journal

CAdir: Joint clustering of cells and genes for single-cell transcriptomics with visualization-driven cluster quality assessment.

PLoS computational biology·2026

Same journal

Systematic design of auxotrophic strains and media conditions to probe metabolic functions in E. coli.

PLoS computational biology·2026

Same journal

Neuronal excitability and parameter variability in the Hodgkin-Huxley model.

PLoS computational biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 28, 2026

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

Randomized Spatial PCA (RASP): A computationally efficient method for dimensionality reduction of high-resolution

Ian K Gingerich^1,2, Brittany A Goods², H Robert Frost¹

¹Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, United States of America.

Plos Computational Biology

|December 10, 2025

Summary

This summary is machine-generated.

Randomized Spatial PCA (RASP) is a new, fast method for analyzing spatial transcriptomics data. It accurately identifies tissue domains and improves gene expression smoothing, making complex spatial biology research more accessible.

More Related Videos

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

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Related Experiment Videos

Last Updated: Jun 28, 2026

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

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

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Spatial transcriptomics (ST) reveals gene expression in tissue context.
Understanding spatial domains is crucial for tissue architecture and disease research.
Existing methods can be computationally intensive for large ST datasets.

Purpose of the Study:

Introduce Randomized Spatial PCA (RASP), a novel dimensionality reduction technique for ST data.
Enhance computational speed and scalability for analyzing large-scale ST datasets.
Provide a flexible method for de-noising and spatially smoothing gene expression.

Main Methods:

RASP utilizes a randomized two-stage PCA framework.
Configurable spatial smoothing is integrated into the method.
Performance was benchmarked against existing ST analysis tools using diverse datasets.

Main Results:

RASP achieves comparable or superior accuracy in tissue-domain detection compared to existing methods.
RASP offers significant improvements in computational speed and scalability.
The method enables efficient exploration of spatial-smoothing parameters for optimal results.

Conclusions:

RASP provides a computationally efficient and accurate approach for spatial transcriptomics data analysis.
Its speed and scalability make it suitable for large, high-resolution datasets.
RASP empowers researchers to better investigate complex tissue architecture and spatial gene expression patterns.