Evaluation of fecal mRNA reproducibility via a marginal transformed mixture modeling approach

• Published in: BMC bioinformatics Vol. 11; no. 1; p. 13
• Main Authors: George, Nysia I, Lupton, Joanne R, Turner, Nancy D, Chapkin, Robert S, Davidson, Laurie A, Wang, Naisyin
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 07.01.2010; BioMed Central; BMC
• Subjects: Algorithms; Bioassays; Biological variation; Colon; Computational biology; Confidence intervals; Databases, Genetic; DNA microarrays; Experiments; Feces - chemistry; Gene expression; Gene Expression Profiling; Messenger RNA; Methodology article; Models, Statistical; Oligonucleotide Array Sequence Analysis - methods; Pattern Recognition, Automated; Physiological aspects; Reproducibility of Results; RNA, Messenger - analysis; Studies; United States
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-11-13

Developing and evaluating new technology that enables researchers to recover gene-expression levels of colonic cells from fecal samples could be key to a non-invasive screening tool for early detection of colon cancer. The current study, to the best of our knowledge, is the first to investigate and report the reproducibility of fecal microarray data. Using the intraclass correlation coefficient (ICC) as a measure of reproducibility and the preliminary analysis of fecal and mucosal data, we assessed the reliability of mixture density estimation and the reproducibility of fecal microarray data. Using Monte Carlo-based methods, we explored whether ICC values should be modeled as a beta-mixture or transformed first and fitted with a normal-mixture. We used outcomes from bootstrapped goodness-of-fit tests to determine which approach is less sensitive toward potential violation of distributional assumptions. The graphical examination of both the distributions of ICC and probit-transformed ICC (PT-ICC) clearly shows that there are two components in the distributions. For ICC measurements, which are between 0 and 1, the practice in literature has been to assume that the data points are from a beta-mixture distribution. Nevertheless, in our study we show that the use of a normal-mixture modeling approach on PT-ICC could provide superior performance. When modeling ICC values of gene expression levels, using mixture of normals in the probit-transformed (PT) scale is less sensitive toward model mis-specification than using mixture of betas. We show that a biased conclusion could be made if we follow the traditional approach and model the two sets of ICC values using the mixture of betas directly. The problematic estimation arises from the sensitivity of beta-mixtures toward model mis-specification, particularly when there are observations in the neighborhood of the the boundary points, 0 or 1. Since beta-mixture modeling is commonly used in approximating the distribution of measurements between 0 and 1, our findings have important implications beyond the findings of the current study. By using the normal-mixture approach on PT-ICC, we observed the quality of reproducible genes in fecal array data to be comparable to those in mucosal arrays.