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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...

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Related Experiment Video

Updated: Oct 17, 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

ClusterMap for multi-scale clustering analysis of spatial gene expression.

Yichun He^1,2, Xin Tang^1,2, Jiahao Huang²

¹John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.

Nature Communications

|October 9, 2021

Summary

This summary is machine-generated.

A new computational framework, ClusterMap, analyzes RNA spatial data without prior annotation. It identifies subcellular structures, cells, and tissue regions, advancing in situ transcriptomic analysis.

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Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Accurate quantification of RNA in spatial context is vital for understanding gene expression and regulation in complex tissues.
In situ transcriptomic methods provide spatially resolved RNA profiles in intact tissues.
A unified computational framework for integrative analysis of in situ transcriptomic data is currently lacking.

Purpose of the Study:

To introduce ClusterMap, an unsupervised and annotation-free computational framework for integrative analysis of in situ transcriptomic data.
To enable precise clustering of RNAs into subcellular structures, cell bodies, and tissue regions in 2D and 3D space.
To demonstrate the broad applicability of ClusterMap across diverse tissue types and in situ transcriptomic measurements.

Main Methods:

Developed an unsupervised and annotation-free framework named ClusterMap.
Formulated the analysis as a point pattern problem incorporating RNA physical location and gene identity.
Utilized density peak clustering (DPC) to identify biologically meaningful structures.

Main Results:

ClusterMap precisely clusters RNAs into subcellular structures, cell bodies, and tissue regions in both 2D and 3D.
The framework demonstrated consistent performance across diverse tissue types, including mouse brain, placenta, gut, and human cardiac organoids.
ClusterMap effectively uncovers gene expression patterns, cell niches, and tissue organization principles.

Conclusions:

ClusterMap provides a unified computational framework for the integrative analysis of in situ transcriptomic data.
The framework is broadly applicable to various in situ transcriptomic measurements.
ClusterMap facilitates the discovery of gene expression patterns, cell niche, and tissue organization principles from complex spatial transcriptomic data.