pySAPC, a python package for sparse affinity propagation clustering: Application to odontogenesis whole genome time series gene-expression data

• Published in: Biochimica et biophysica acta Vol. 1860; no. 11; pp. 2613 - 2618
• Main Authors: Cao, Huojun, Amendt, Brad A.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.11.2016
• Subjects: Algorithms; Animals; Cluster Analysis; congenital abnormalities; data collection; Databases, Genetic; Dental anomalies; gene expression; Gene Expression - genetics; Gene Expression Profiling - methods; Genome - genetics; memory; Mice; microarray technology; multigene family; Multigene Family - genetics; Odontogenesis - genetics; Oligonucleotide Array Sequence Analysis - methods; pySAPC; Sparse affinity propagation clustering; time series analysis; Time series microarray; Tooth - growth & development; Dental anomalies; Time series microarray; Sparse affinity propagation clustering; pySAPC
• ISSN: 0304-4165; 0006-3002; 1872-8006
• DOI: 10.1016/j.bbagen.2016.06.008

Developmental dental anomalies are common forms of congenital defects. The molecular mechanisms of dental anomalies are poorly understood. Systematic approaches such as clustering genes based on similar expression patterns could identify novel genes involved in dental anomalies and provide a framework for understanding molecular regulatory mechanisms of these genes during tooth development (odontogenesis). A python package (pySAPC) of sparse affinity propagation clustering algorithm for large datasets was developed. Whole genome pair-wise similarity was calculated based on expression pattern similarity based on 45 microarrays of several stages during odontogenesis. pySAPC identified 743 gene clusters based on expression pattern similarity during mouse tooth development. Three clusters are significantly enriched for genes associated with dental anomalies (with FDR <0.1). The three clusters of genes have distinct expression patterns during odontogenesis. Clustering genes based on similar expression profiles recovered several known regulatory relationships for genes involved in odontogenesis, as well as many novel genes that may be involved with the same genetic pathways as genes that have already been shown to contribute to dental defects. By using sparse similarity matrix, pySAPC use much less memory and CPU time compared with the original affinity propagation program that uses a full similarity matrix. This python package will be useful for many applications where dataset(s) are too large to use full similarity matrix. This article is part of a Special Issue entitled “System Genetics” Guest Editor: Dr. Yudong Cai and Dr. Tao Huang. •Sparse similarity matrix could save lots of memory and CPU time in affinity propagation clustering•pySAPC is memory and computation efficient, could deal with large dataset•Gene clustering help us understanding molecular mechanisms of dental anomalies