Random frog: An efficient reversible jump Markov Chain Monte Carlo-like approach for variable selection with applications to gene selection and disease classification

• Published in: Analytica chimica acta Vol. 740; pp. 20 - 26
• Main Authors: Li, Hong-Dong, Xu, Qing-Song, Liang, Yi-Zeng
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 31.08.2012; Elsevier
• Subjects: Algorithms; Analytical chemistry; Breast Neoplasms - genetics; Chemistry; Colon; Colon - metabolism; Colonic Neoplasms - genetics; Computer simulation; data collection; Disease - classification; Disease - genetics; disease diagnosis; Estrogens; Estrogens - genetics; Exact sciences and technology; Frogs; gene expression; Gene Expression Profiling; Gene expression-based disease classification; General, instrumentation; Genes; Humans; Markov chain; Markov Chain Monte Carlo; Markov Chains; Mathematical models; microarray technology; Monte Carlo Method; Monte Carlo methods; Oligonucleotide Array Sequence Analysis - methods; Random frog; Variable selection; Random frog; Markov Chain Monte Carlo; Gene expression-based disease classification; Variable selection; Monte Carlo method; Sample size; Selection; Microanalysis; Estrogen; Identification; Chemical sensor; Gene expression; Algorithm; Design; Gene; Simulation; Classification; Distribution; Diagnosis; Limit; Sensor array; Application
• ISSN: 0003-2670; 1873-4324; 1873-4324
• DOI: 10.1016/j.aca.2012.06.031

[Display omitted] ► The proposed method possesses advantages of RJMCMC algorithms. ► The proposed method is easier to implement than RJMCMC. ► Competitive results over published work were obtained. ► Random frog is computationally very efficient. The identification of disease-relevant genes represents a challenge in microarray-based disease diagnosis where the sample size is often limited. Among established methods, reversible jump Markov Chain Monte Carlo (RJMCMC) methods have proven to be quite promising for variable selection. However, the design and application of an RJMCMC algorithm requires, for example, special criteria for prior distributions. Also, the simulation from joint posterior distributions of models is computationally extensive, and may even be mathematically intractable. These disadvantages may limit the applications of RJMCMC algorithms. Therefore, the development of algorithms that possess the advantages of RJMCMC methods and are also efficient and easy to follow for selecting disease-associated genes is required. Here we report a RJMCMC-like method, called random frog that possesses the advantages of RJMCMC methods and is much easier to implement. Using the colon and the estrogen gene expression datasets, we show that random frog is effective in identifying discriminating genes. The top 2 ranked genes for colon and estrogen are Z50753, U00968, and Y10871_at, Z22536_at, respectively. (The source codes with GNU General Public License Version 2.0 are freely available to non-commercial users at: http://code.google.com/p/randomfrog/.)