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

Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure 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.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

You might also read

Related Articles

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

Sort by

Same author

M phase phosphorylation of the epigenetic regulator UHRF1 regulates its physical association with the deubiquitylase USP7 and stability.

Proceedings of the National Academy of Sciences of the United States of America·2012

Same author

Biosynthesis of ethyl oleate, a primer pheromone, in the honey bee (Apis mellifera L.).

Insect biochemistry and molecular biology·2012

Same author

Co-delivery strategies based on multifunctional nanocarriers for cancer therapy.

Current drug metabolism·2012

Same author

Efficacy of gemifloxacin for the treatment of experimental Staphylococcus aureus keratitis.

Journal of ocular pharmacology and therapeutics : the official journal of the Association for Ocular Pharmacology and Therapeutics·2012

Same author

Expression profile analysis of the polygalacturonase-inhibiting protein genes in rice and their responses to phytohormones and fungal infection.

Plant cell reports·2012

Same author

Characterizing natural dissolved organic matter in a freshly submerged catchment (Three Gorges Dam, China) using UV absorption, fluorescence spectroscopy and PARAFAC.

Water science and technology : a journal of the International Association on Water Pollution Research·2012

Same journal

Protocol for genomic mapping of chromatin targets using high-throughput CUT&RUN.

STAR protocols·2026

Same journal

A protocol for noninvasive quantification of dietary fat absorption in mice.

STAR protocols·2026

Same journal

Protocol for an AlCl<sub>3</sub>-induced Alzheimer's model in zebrafish larvae with optimized pH and behavioral assessment.

STAR protocols·2026

Same journal

Protocol for live cell barcoding and immunophenotyping of human hematological malignancies using cytometry by time of flight.

STAR protocols·2026

Same journal

Generating drug resistance models in human and murine cancer cell lines and assessing cross-resistance to chemotherapeutics and KRAS inhibitors.

STAR protocols·2026

Same journal

Protocol for simultaneous in vivo two-photon imaging and locomotion quantification during olfactory stimulation and pharmacology in walking Drosophila.

STAR protocols·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 7, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Protocol to recover single-cell gene expression profiles from spatial transcriptomics data using cluster computing.

Young Je Lee¹, Hao Chen², Jose Lugo-Martinez¹

¹Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

|February 13, 2025

Summary

This summary is machine-generated.

scResolve recovers single-cell gene expression profiles from low-resolution spatial transcriptomics data. This computational protocol enhances spatial transcriptomics analysis for single-cell resolution.

Keywords:

biotechnology and bioengineering computer sciences gene expression single cell

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

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

Related Experiment Videos

Last Updated: Jun 7, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

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

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Spatial transcriptomics technologies like Visium provide valuable gene expression data but often at a multicellular resolution.
This limitation hinders detailed single-cell level analysis, restricting insights into cellular heterogeneity and function within tissues.

Purpose of the Study:

To introduce scResolve, a novel computational protocol designed to computationally recover high-resolution, single-cell gene expression profiles from low-resolution spatial transcriptomics data.
To provide a detailed guide for setting up the computational environment, preparing data, and executing the super-resolution inference and cell segmentation modules of scResolve.

Main Methods:

scResolve employs super-resolution inference algorithms to deconvolve multicellular data.
Cell segmentation modules are utilized to assign expression profiles to individual cells.
The protocol is designed to run in a cluster environment, utilizing parallel computing for accelerated processing.

Main Results:

scResolve successfully recovers single-cell gene expression profiles from low-resolution spatial transcriptomics datasets.
The protocol enables the transition from multicellular to single-cell resolution analysis, unlocking new biological insights.
Parallel computing significantly speeds up data processing, making single-cell resolution analysis more accessible.

Conclusions:

scResolve offers a powerful computational solution for extracting single-cell resolution gene expression data from existing low-resolution spatial transcriptomics experiments.
This advancement expands the utility of spatial transcriptomics, enabling more granular investigations into tissue architecture and cellular interactions.
The protocol's efficiency and accessibility facilitate deeper understanding in fields like developmental biology and disease research.