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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...
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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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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Speeding up the Consensus Clustering methodology for microarray data analysis.

Raffaele Giancarlo1, Filippo Utro

  • 1Dipartimento di Matematica ed Informatica, Universitá di Palermo, Via Archirafi 34, 90123 Palermo, Italy. raffaele@math.unipa.it.

Algorithms for Molecular Biology : AMB
|January 18, 2011
PubMed
Summary
This summary is machine-generated.

We developed Fast Consensus (FC), a faster version of Consensus Clustering, to accurately predict the number of clusters in datasets. FC offers excellent precision and speed, making it ideal for analyzing complex biological data like microarrays.

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Area of Science:

  • Statistics
  • Data Analysis
  • Bioinformatics
  • Machine Learning

Background:

  • Internal validation measures are crucial for determining the number of clusters in datasets, particularly for complex biological data like microarrays.
  • Existing methods often struggle to balance speed and precision, hindering effective analysis of inherent biological structures.
  • Consensus Clustering (Consensus) is a robust methodology but computationally intensive.

Purpose of the Study:

  • To develop a faster approximation algorithm for Consensus Clustering.
  • To maintain the high precision of Consensus while significantly improving time performance.
  • To provide a reliable internal validation measure for cluster number prediction in datasets.

Main Methods:

  • Proposed a speed-up of Consensus Clustering, named Fast Consensus (FC).
  • Evaluated FC's performance on twelve benchmark microarray datasets with varying dimensionality.
  • Compared FC's speed and precision against existing internal validation methods and Consensus.

Main Results:

  • FC achieves comparable precision to Consensus with substantially better time performance.
  • FC is among the fastest internal validation methods, retaining Consensus's outstanding precision.
  • FC generates a consensus matrix suitable as a dissimilarity matrix, ensuring consistent clustering performance.

Conclusions:

  • Fast Consensus (FC) is a robust and efficient internal validation measure for cluster number prediction, especially for datasets with hundreds of items and up to a thousand conditions.
  • The developed technique can be applied to accelerate other stability-based validation measures.
  • FC offers a practical solution for analyzing complex biological data where speed and accuracy are critical.