2016/06/09 by Patrick McSharry, Patrick E. McSharry, McSharry, Patrick E. +2
Biochemistry, Genetics and Molecular Biology · Computer Science · #Algorithms and Data Compression #Evolutionary Algorithms and Applications #FOS: Biological sciences #Gene expression and cancer classification #Quantitative Methods (q-bio.QM) #q-bio.QM
paper · pdf · doi:10.48550/arxiv.1606.02801
arxiv created 2016/06/09 · openalex publication_date 2016/06/09 · arxiv updated 2016/06/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Motivation: Clustering techniques are routinely applied to identify patterns of co-expression in gene expression data. Co-regulation, and involvement of genes in similar cellular function, is subsequently inferred from the clusters which are obtained. Increasingly sophisticated algorithms have been applied to microarray data, however, less attention has been given to the statistical significance of the results of clustering studies. We present a technique for the analysis of commonly used hierarchical linkage-based clustering called Significance Analysis of Linkage Trees (SALT). Results: The statistical significance of pairwise similarity levels between gene expression profiles, a measure of co-expression, is established using a surrogate data analysis method. We find that a modified version of the standard linkage technique, complete-linkage, must be used to generate hierarchical linkage trees with the appropriate properties. The approach is illustrated using synthetic data generated from a novel model of gene expression profiles and is then applied to previously analysed microarray data on the transcriptional response of human fibroblasts to serum stimulation.