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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

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...
Factorial Design02:01

Factorial Design

Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
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...
Compacting Factor test01:22

Compacting Factor test

The compacting factor test is a method used to assess the workability of concrete. It is  especially suitable for concrete mixes containing aggregates up to one and a half inches in size. This test involves specialized equipment consisting of two truncated cone-shaped hoppers and a cylinder, all with polished interior surfaces to minimize friction.
The procedure begins by placing concrete into the upper hopper without any compaction. Once filled, the bottom door of this hopper is opened,...
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an organic...

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Related Experiment Video

Updated: Jun 17, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Penalized mixtures of factor analyzers with application to clustering high-dimensional microarray data.

Benhuai Xie1, Wei Pan, Xiaotong Shen

  • 1Division of Biostatistics, School of Public Health and School of Statistics, University of Minnesota, Minneapolis, MN, USA.

Bioinformatics (Oxford, England)
|December 25, 2009
PubMed
Summary

This study introduces a penalized mixture of factor analyzers to handle correlated variables in high-dimensional data, enabling simultaneous clustering and variable selection for improved analysis of complex datasets like microarrays.

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Area of Science:

  • Bioinformatics
  • Statistical Learning
  • Computational Biology

Background:

  • Model-based clustering is crucial for analyzing high-dimensional data, such as microarrays.
  • Existing penalized methods perform variable selection but assume variable independence.
  • This independence assumption limits their applicability to datasets with correlated variables.

Purpose of the Study:

  • To develop a novel penalized model-based clustering method that accounts for variable non-independence.
  • To enable simultaneous variable selection and clustering in high-dimensional data with correlated variables.
  • To address the challenge of parameter explosion in general covariance matrices.

Main Methods:

  • Generalizing the mixture of factor analyzers model.
  • Incorporating penalization for variable selection.
  • Developing a method to model non-diagonal covariance matrices effectively.

Main Results:

  • The proposed method successfully models non-independent variables.
  • Effective simultaneous clustering and variable selection were achieved.
  • Demonstrated advantages over existing methods using simulated and real microarray data.

Conclusions:

  • The penalized mixture of factor analyzers is a powerful tool for high-dimensional data analysis.
  • This method enhances clustering by accounting for variable correlations.
  • It offers a robust approach for gene expression and similar complex biological data.