A two-phase clustering procedure based on allele specific expression

BACKGROUND: Allele Specific Expression analysis is an important tool for integrating genome and transcriptome data. It quantifies expression variation between the two haplotypes of a diploid individual distinguished by heterozygous sites, and is a powerful tool to estimate cis-regulatory diversity of alleles. Clustering algorithms can be used to identify patterns or groups of genes/samples based on their expression profiles. Depending on the structure of the data, different existing clustering algorithm can be adapted to allele specific expression data. However, no ad-hoc procedure has been developed. RESULTS: In this work, we begin defining an expression matrix capturing allele expressions from an RNA-sequencing experiment. On this matrix, we develop a novel two-phase unsupervised clustering procedure, built on top of a spectral clustering algorithm, whose aim is to partition the population into groups of similar individuals, according to their allelic expression. As case-studies, the approach is used to cluster 98 cultivars representative of the variability observed in Vitis vinifera, starting from read counts of genes of chromosome 1 of leaves, and to analyze allele-specific count data from a CASTxMRL F1 hybrid mice dataset. CONCLUSION: Using the above mentioned real case-studies as well as generated synthetic data, we see that our algorithm shows significant robustness and outperforms other standard clustering techniques.