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CLIFF: clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts.

E P Xing1, R M Karp

  • 1Division of Computer Science, University of California, Berkeley, Berkeley, CA 94720, USA. epxing@cs.berkeley.edu

Bioinformatics (Oxford, England)
|July 27, 2001
PubMed
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CLIFF, a novel algorithm for clustering biological samples, effectively addresses challenges in gene expression data. It integrates feature selection with clustering to improve sample classification accuracy.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Clustering biological samples using gene expression microarray data presents challenges due to data sparsity, high dimensionality, and irrelevant/redundant features.
  • Existing clustering approaches often do not adequately address feature selection, potentially impacting accuracy.

Purpose of the Study:

  • To introduce CLIFF, an iterative algorithm designed for robust clustering of biological samples from gene expression data.
  • To enhance clustering performance by integrating a sophisticated feature filtering process.

Main Methods:

  • CLIFF employs an iterative approach, alternating between feature filtering and clustering.
  • Feature filtering ranks genes based on discriminability, relevance to a reference partition, and irredundancy.

Related Experiment Videos

  • Clustering is performed using a normalized cut method on the selected features.

    • Main Results:

    • CLIFF demonstrated superior performance compared to standard clustering methods lacking feature selection on a leukemia sample dataset.
    • The algorithm achieved a clustering result closely matching expert labeling for 72 leukemia samples across 7130 genes.

    Conclusions:

    • CLIFF offers an effective solution for clustering biological samples, particularly when dealing with high-dimensional and sparse gene expression data.
    • The integration of iterative feature selection significantly improves clustering accuracy and reliability.