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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...
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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.
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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Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
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Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

Inferential clustering approach for microarray experiments with replicated measurements.

Miquel Salicrú1, Sergi Vives, Tian Zheng

  • 1Statistics Department, Barcelona University, Avda Diagonal 645, 08028 BCN, Spain. msalicru@ub.edu

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|October 31, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel clustering method for gene expression data, enhancing analysis of microarray experiments with limited replicates. The approach offers a user-friendly bioinformatics solution, avoiding subjective dissimilarity measures for more efficient data utilization.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Cluster analysis is vital for understanding gene associations in microarray data.
  • Existing methods often rely on subjective dissimilarity measures.
  • Low replication in microarray experiments presents analytical challenges.

Purpose of the Study:

  • Introduce a general clustering approach for gene expression data.
  • Address challenges in microarray analysis with low replication.
  • Provide an objective and efficient clustering methodology.

Main Methods:

  • Developed a general clustering algorithm based on confidence interval inferential methodology.
  • Applied the method to gene expression data from microarray experiments.
  • Focused on datasets with low replication (3-5 replicates).

Main Results:

  • The proposed method efficiently utilizes measured data.
  • It avoids the subjective selection of dissimilarity measures.
  • Demonstrated effectiveness on simulated and real microarray datasets.

Conclusions:

  • The new methodology offers an easy-to-use bioinformatics solution for clustering microarray data with replicates.
  • It is a general algorithm applicable beyond microarray experiments.
  • Outperforms conventional correlation or Euclidean distance-based clustering methods.