Unsupervised clustering in mRNA expression profiles

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Unsupervised clustering in mRNA expression
profiles

• D.K. Tasoulis, V.P. Plagianakos, and M.N.
  Vrahatis Computational Intelligence Laboratory
  (CILAB), Department of Mathematics, University of
  Patras, GR-26110 Patras, Greece
• University of Patras Artificial Intelligence
  Research Center (UPAIRC), University of Patras,
  GR-26110 Patras, Greece
• Computers in Biology and Medicine
• In Press, Corrected Proof, Available online 24
  October 2005

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K-Windows Clustering

• Adaptation of K-means, originally proposed in
  2002 by Vrahatis et. al.
• Windowing technique improves speed and accuracy
• Tries to place a d-dimensional window (box)
  containing all patterns that belong to a single
  cluster

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K-Windows Basic Concepts

• Move windows to find cluster centers (fig a)
• Select k points as centers of d-windows of size
  a.
• Window means becomes new center.
• Repeat until stopping criterion (movement of
  center).
• Enlarge windows to determine cluster edges (fig
  b)
• Enlarge one dimension by a specified percent.
• Relocate window as above.
• Keep only if increase in instances in window
  exceeds threshold

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Unsupervised K-Windows (UKW)

• Start with sufficiently large number of windows
• Merge to automatically determine the number of
  clusters
• For each pair of overlapping windows, calculate
  proportion of overlap for each window.
• Large overlap, considered same cluster, W1 is
  deleted.
• Many points in common, considered the same
  cluster.
• Low overlap, considered two different clusters.

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Experimental Setup

• Leukemia dataset well characterized
• Default UKW parameters used
• Supervised dimension reduction
• Two previously published gene subsets and their
  union
• Unsupervised dimension reduction
• Biclustering with UKW
• PCA
• PCA and UKW hybrid

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Supervised Feature Selection

• Use two gene subsets selected in previously
  published papers using supervised techniques.
• All algorithms did best on combined set, results
  below.

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Unsupervised Feature Selection(Biclustering
Technique)

• Apply UKW to cluster genes, select one gene,
  closest to cluster center, as representative from
  each cluster.
• Apply UKW to samples, using those genes (239).
• UKW accuracy 93.6 (ALL) and 76 (AML)
• No results reported for other algorithms

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Unsupervised Feature Selection(PCA Techniques)

• PCA and scree plot to reduce features
• Poor Performance
• Hybrid PCA and UKW method
• Partition genes using UKW
• Transform each partition using PCA
• Select representative factors from each cluster
• UKW accuracy 97.87 (ALL) and 88 (AML)

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UKW Results Summary
Dataset ALL Accuracy AML Accuracy
Published Gene Subsets (Supervised) 90 100
UKW Biclustering (Unsupervised) 93.6 76
PCA (Unsupervised) N/A N/A
PCA-UKW Hybrid (Unsupervised) 97.87 88
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• Default parameters
• initial window size a5
• enlargement threshold ?e0.8
• merging threshold ?m0.1
• coverage threshold ?c0.2
• variability threshold ?v0.02
• Link to article