2018/08/18 by Yang Li, Shaoyang Ning, Li, Yang +7
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Applications (stat.AP) #Bayesian Methods and Mixture Models #Bioinformatics and Genomic Networks #FOS: Computer and information sciences #Gene expression and cancer classification #stat.AP
paper · pdf · doi:10.48550/arxiv.1808.06109
34 pages, 8 figures
arxiv created 2018/08/18 · openalex publication_date 2018/08/18 · arxiv updated 2018/08/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Determination of functions for poorly characterized genes is crucial for understanding biological processes and studying human diseases. Functionally associated genes are often gained and lost together through evolution. Therefore identifying co-evolution of genes can predict functional gene-gene associations. We describe here the full statistical model and computational strategies underlying the original algorithm, CLustering by Inferred Models of Evolution (CLIME 1.0) recently reported by us [Li et al., 2014]. CLIME 1.0 employs a mixture of tree-structured hidden Markov models for gene evolution process, and a Bayesian model-based clustering algorithm to detect gene modules with shared evolutionary histories (termed evolutionary conserved modules, or ECMs). A Dirichlet process prior was adopted for estimating the number of gene clusters and a Gibbs sampler was developed for posterior sampling. We further developed an extended version, CLIME 1.1, to incorporate the uncertainty on the evolutionary tree structure. By simulation studies and benchmarks on real data sets, we show that CLIME 1.0 and CLIME 1.1 outperform traditional methods that use simple metrics (e.g., the Hamming distance or Pearson correlation) to measure co-evolution between pairs of genes.