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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.
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Updated: Jun 24, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

An agglomerative hierarchical approach to visualization in Bayesian clustering problems.

K J Dawson1, K Belkhir

  • 1Centre for Mathematical and Computational Biology, Rothamsted Research, Harpenden, Hertfordshire, UK. kevin.dawson@bbsrc.ac.uk

Heredity
|April 2, 2009
PubMed
Summary
This summary is machine-generated.

We introduce the exact linkage algorithm for visualizing complex sample partitions in population genetics. This Bayesian clustering method generates hierarchical trees to represent uncertainty in individual assignments.

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Published on: February 15, 2017

Related Experiment Videos

Last Updated: Jun 24, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

  • Population genetics
  • Computational biology
  • Statistical genetics

Background:

  • Clustering individuals into groups is crucial in population genetics.
  • Bayesian approaches represent sample partitions as probability distributions.
  • Visualizing these distributions is challenging due to the rapid growth in possible partitions with sample size.

Purpose of the Study:

  • To address the visualization challenge of complex sample partitions in Bayesian clustering.
  • To introduce a novel algorithm for representing uncertainty in individual assignments.
  • To provide a tool for exploring posterior distributions in population genetics.

Main Methods:

  • Developed the exact linkage algorithm, a specialized maximin clustering method.
  • The algorithm utilizes posterior co-assignment probabilities as input.
  • Implemented the algorithm in the PartitionView software package.

Main Results:

  • The exact linkage algorithm generates rooted binary trees or forests.
  • Each node represents a set of individuals, with node height indicating posterior co-assignment probability.
  • This provides a visual representation of uncertainty in individual categorization.

Conclusions:

  • The exact linkage algorithm offers an effective solution for visualizing complex sample partitions.
  • It facilitates a better understanding of uncertainty in Bayesian clustering of individuals.
  • PartitionView provides a valuable tool for population geneticists to explore data.