Estimating a Structured Covariance Matrix From Multilab Measurements in High-Throughput Biology

• 成果类型: Article
• 署名作者: Franks, Alexander M.; Csardi, Gabor; Drummond, D. Allan; Airoldi, Edoardo M.
• 署名单位: Harvard University; University of Chicago
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2014.964404
• 发表日期: 2015
• 页码: 27-44
• 关键词: gene-expression; measurement error; statistical framework; yeast transcriptome; protein expression; rna-seq; quantification; microarrays; abundance; reveals

摘要

We consider the problem of quantifying the degree of coordination between transcription and translation, in yeast. Several studies have reported a surprising lack of coordination over the years, in organisms as different as yeast and humans, using diverse technologies. However, a close look at this literature suggests that the lack of reported correlation may not reflect the biology of regulation. These reports do not control for between-study biases and structure in the measurement errors, ignore key aspects of how the data connect to the estimand, and systematically underestimate the correlation as a consequence. Here, we design a careful meta-analysis of 27 yeast datasets, supported by a multilevel model, full uncertainty quantification, a suite of sensitivity analyses, and novel theory, to produce a more accurate estimate of the correlation between mRNA and protein levelsa proxy for coordination. From a statistical perspective, this problem motivates new theory on the impact of noise, model misspecifications, and nonignorable missing data on estimates of the correlation between high-dimensional responses. We find that the correlation between mRNA and protein levels is quite high under the studied conditions, in yeast, suggesting that post-transcriptional regulation plays a less prominent role than previously thought.