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

Benchmarking sketching methods on spatial transcriptomics data.

Nucleic acids research·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 author

A scalable, low-cost, sample hashing workflow for multiomic single-cell analysis using the Seq-Well S<sup>3</sup> platform.

Nature protocols·2026

Same author

A streamlined and comprehensive protocol for the generation and multi-omic analysis of human monocyte-derived macrophages.

BMC biotechnology·2025

Same author

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

PLoS computational biology·2025

Same author

Multiomic analysis reveals that polyamines alter G. vaginalis-induced cervicovaginal epithelial cell dysfunction.

bioRxiv : the preprint server for biology·2025

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

Search research articles

Related Experiment Video

Updated: Jun 16, 2026

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

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, NH, USA.

Biorxiv : the Preprint Server for Biology

|January 7, 2025

Summary

This summary is machine-generated.

Researchers developed Randomized Spatial PCA (RASP), a fast new method for analyzing spatial transcriptomics (ST) data. RASP efficiently identifies tissue domains and improves gene expression analysis, aiding biological discovery.

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

The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers

The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers

Published on: January 22, 2013

Related Experiment Videos

Last Updated: Jun 16, 2026

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

The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers

The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers

Published on: January 22, 2013

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Spatial transcriptomics (ST) reveals gene expression patterns within tissue architecture.
Understanding spatial domains is crucial for development and disease research.
Existing ST analysis methods can be computationally intensive.

Purpose of the Study:

Introduce Randomized Spatial PCA (RASP), a novel, fast, and scalable dimensionality reduction method for ST data.
Enable flexible integration of non-transcriptomic data and de-noising of gene expression.
Improve the efficiency of spatial domain identification and analysis in ST.

Main Methods:

RASP employs a randomized two-stage principal component analysis (PCA) framework.
Utilizes sparse matrix operations and configurable spatial smoothing for efficiency.
Compares RASP against five existing methods on diverse ST datasets (10x Visium, Stereo-Seq, MERFISH, 10x Xenium).

Main Results:

RASP demonstrates computational speeds orders-of-magnitude faster than existing techniques.
Achieves comparable or superior performance in tissue domain detection.
Effectively reconstructs de-noised and spatially smoothed gene expression values.

Conclusions:

RASP offers a significant computational advantage for analyzing large-scale ST datasets.
Facilitates exploration of high-resolution subcellular ST data.
Enhances the study of tissue organization and biological processes through efficient spatial gene expression analysis.