Identification of co-regulation patterns by unsupervised cluster analysis of gene expression data

Identification of co-regulation patterns by unsupervised cluster analysis of gene expression data

A method is provided for unsupervised clustering of gene expression data to identify co-regulation patterns. A clustering algorithm randomly divides the data into k different subsets and measures the similarity between pairs of datapoints within the subsets, assigning a score to the pairs based on s...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: BEN HUR ASA, GUYON ISABELLE, ELISSEEFF ANDRE
Format: Patent
Sprache:eng
Schlagworte:
ANALOGUE COMPUTERS
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
HANDLING RECORD CARRIERS
INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
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Beschreibung
Zusammenfassung:A method is provided for unsupervised clustering of gene expression data to identify co-regulation patterns. A clustering algorithm randomly divides the data into k different subsets and measures the similarity between pairs of datapoints within the subsets, assigning a score to the pairs based on similarity, with the greatest similarity giving the highest correlation score. A distribution of the scores is plotted for each k. The highest value of k that has a distribution that remains concentrated near the highest correlation score corresponds to the number of co-regulation patterns.