Download consensus clusters

Generating Robust and Consensus
Clusters from Gene Expression Data
Allan Tuckera, Stephen Swifta, Xiaohui Liua,
Nigel Martinb, Christine Orengoc, Paul Kellamc
a
b
c
Introduction
• Many different clustering algorithms used
for gene expression analysis
• Little work on inter-method consistency or
cross-comparison
• Important due to differing results (each
algorithm implicitly forces a structure on
data)
• Obtaining a consensus across methods
should improve confidence
The Talk
• Compare a number of existing methods for
clustering gene expression data
• Algorithms for generating robust clusters and
consensus clusters
• Tested on a set of Amersham Scorecard data with
known structure and experimentally obtained virus
B-Cell data
• Provides specific advantages in the analysis of
array based gene expression data
Clustering Methods
•
•
•
•
Hierarchical Clustering (R)
PAM (R)
CAST (C++)
Simulated Annealing (C++)
Datasets
• Amersham Scorecard
– 597 genes, 24 blocks with 32 columns and 12
rows under 30 experimental conditions
– Repeated experiments which we assume should
cluster together
• B Cell Data
– 1987 genes
Comparison of Methods
The Agreement Matrix
Robust Clustering
• Takes agreement matrix as input
• Place all genes into robust clusters that have
full agreement
• Deterministic algorithm
• Should give higher degree of confidence in
clusters
• Not all genes will be assigned
Robust Clustering
Dataset
ASC
Bcell
No. of Robust
Clusters
24
154
% of variables
assigned
79%
25%
Max. Robust
Cluster size
44
14
Min. Robust
Cluster size
2
2
Mean Robust
Cluster size
10.2
3.2
Consensus Clustering
• “Full agreement” requirement for robust
clusters can be too restrictive
• Algorithm for generating consensus clusters
given minimum agreement parameter
• Approximate stochastic algorithm
Consensus Clustering
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Agreement Matrix
Consensus Clusters
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Input Cluster Results
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Consensus Clustering
B-Cell Dataset
ASC Dataset
Consensus Clustering
Consensus Clustering
Summary
• Clustering biological data is very useful
• Biases in clustering algorithms can mean
success in identification of patterns vary
• Consensus algorithms used in protein
secondary structure prediction
• We apply similar strategy with robust and
consensus clustering
Conclusions
• Robust clusters good for identifying
common transcriptional modules
• Also for identifying genes with common
functional pathway
• Useful for creating clusters of genes with
high confidence
• Can be restrictive in discarding genes that
do not have full agreement.
Conclusions
• Consensus clustering relaxes full agreement
requirement
• Resembles defined clusters in synthetic data
very well
• Reliably picks out features in the virus gene
expression data
• Fulfils desire not to rely on one clustering
algorithm during gene expression analysis
Acknowledgements
• The Biotechnology and Biological Sciences
Research Council (BBSRC), UK
• The Engineering and Physical Sciences
Research Council (EPSRC), UK