Paper Info
Title
Consensus Clustering For Cancer Gene Expression Data Large-Scale Analysis Using Evidence Accumulation Approach
Abstract
Clustering algorithms are extensively used on patient tissue samples in order to group and visualize the microarray data. The high dimensionality and probe specific noise make the selection of the appropriate clustering algorithm an uneasy task. This study presents a large-scale analysis of three clustering algorithms: k-means, hierarchical clustering (HC) and evidence accumulation clustering (EAC) on thirty-five cancer gene expression data sets selected to benchmark the performance of the clustering algorithms. Separated performance analysis was done on data sets from Affymetrix and cDNA chip platforms to examine the possible influence of the microarray technology. The study revealed no consistent algorithm ranking can be inferred, though in general EAC presented the best compromise of adjusted rand index (ARI) and variance. However, the results indicated that ARI variance under repeated k-means initializations offers useful information on the need to implement more complex clustering techniques. If repeated K-means converges to the same partition, also confirmed by the HC clustering, there is no need to run EAC. However, under moderate or highly variable ARI in repeated K-means, EAC should be used to reduce the uncertainty of clustering and unveil the data structure.
Year
DOI
Venue
2017
10.5220/0006174501760183
PROCEEDINGS OF THE 10TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES, VOL 3: BIOINFORMATICS
Keywords
Field
DocType
Clustering, Consensus Clustering, Cancer Gene Expression
Scale analysis (statistics),Data mining,Computer science,Gene expression,Consensus clustering,Artificial intelligence,Bioinformatics,Machine learning,Cancer
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Isidora Sasic100.34
Sanja Brdar212.71
Tatjana Loncar-Turukalo3135.37
Helena Aidos416013.15
Ana L. N. Fred51317195.30