期刊
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
卷 110, 期 509, 页码 27-44出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/01621459.2014.964404
关键词
High-dimensional inference; Inter-laboratory comparisons; Measurement error; Nonignorable missing data
资金
- National Science Foundation [IIS-1017967, CAREER IIS-1149662]
- National Institute of Health [R01 GM096193, R01 GM088344, P50 GM068763]
- Pew Charitable Trusts
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据