Estimating a Structured Covariance Matrix From Multilab Measurements in High-Throughput Biology
利用多实验室高通量生物测量估计结构化协方差矩阵AI 解读 ↓
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利用多实验室高通量生物测量估计结构化协方差矩阵
- 研究问题
- 酵母 mRNA 与蛋白质水平的低观测相关性，是否源于跨研究偏差、测量误差和缺失问题，而非真实调控协调较弱？
- 方法与思路
- 作者对二十七个酵母数据集进行元分析，结合多层模型、不确定性量化与敏感性分析，估计转录和翻译协调的相关性代理。
- 材料陈述的贡献
- 摘要称研究条件下的相关性较高，并提出关于噪声、模型误设及不可忽略缺失如何影响高维响应相关估计的新理论。
- 阅读关注点
- 应核对跨实验室校准、测量误差结构、缺失机制，以及相关性作为协调代理的解释和敏感性分析范围。
- 阅读边界
- 摘要未给出相关系数、区间及模型条件，不能将酵母特定条件下的结果外推至其他生物；索引版本未注明，不能推断刊本变化。
原文摘要
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 human, 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 data sets, 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 levels-a proxy for coordination. From a statistical perspective, this problem motivates new theory on the impact of noise, model mis-specifications and non-ignorable 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.