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Related Concept Videos

Cluster Sampling Method01:20

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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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
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Model-based clustering on the unit sphere with an illustration using gene expression profiles.

Jean-Luc Dortet-Bernadet1, Nicolas Wicker

  • 1Institut de Recherche Mathématique Avancée (IRMA), UMR 7501 CNRS, Université Louis Pasteur, Strasbourg, France. dortet@math.u-strasbg.fr

Biostatistics (Oxford, England)
|May 1, 2007
PubMed
Summary

This study introduces a novel clustering method for spherical data, like standardized gene expression profiles. The approach uses projected normal distributions, offering improved analysis for complex biological datasets.

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Area of Science:

  • Computational Biology
  • Statistical Genomics
  • Machine Learning

Background:

  • Standardized gene expression data from microarray experiments often exhibit spherical distributions.
  • Existing clustering methods may not effectively handle the unique geometry of spherical data.
  • Accurate clustering is crucial for identifying patterns in gene expression profiles.

Purpose of the Study:

  • To develop a model-based clustering algorithm for data residing on a unit sphere.
  • To model spherical clusters using inverse stereographic projections of multivariate normal distributions.
  • To evaluate the algorithm's performance and compare it with existing methods.

Main Methods:

  • Utilized inverse stereographic projections of multivariate normal distributions to model clusters on the sphere.
  • Developed a corresponding model-based clustering algorithm.
  • Applied the algorithm to both simulated and real standardized gene expression profile datasets.

Main Results:

  • Assessed the performance of cluster number determination criteria using simulated data.
  • Compared the proposed method's performance against existing clustering techniques.
  • Demonstrated the algorithm's applicability on a real reference dataset of gene expression profiles.

Conclusions:

  • The proposed model-based clustering approach effectively handles spherical data.
  • The method provides a robust framework for analyzing standardized gene expression profiles.
  • This technique offers a valuable tool for uncovering biological insights from high-dimensional genomic data.