Perplexity: evaluating transcript abundance estimation in the absence of ground truth.

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Jason Fan, Skylar Chan, Rob Patro
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Abstract

Background: There has been rapid development of probabilistic models and inference methods for transcript abundance estimation from RNA-seq data. These models aim to accurately estimate transcript-level abundances, to account for different biases in the measurement process, and even to assess uncertainty in resulting estimates that can be propagated to subsequent analyses. The assumed accuracy of the estimates inferred by such methods underpin gene expression based analysis routinely carried out in the lab. Although hyperparameter selection is known to affect the distributions of inferred abundances (e.g. producing smooth versus sparse estimates), strategies for performing model selection in experimental data have been addressed informally at best.

Results: We derive perplexity for evaluating abundance estimates on fragment sets directly. We adapt perplexity from the analogous metric used to evaluate language and topic models and extend the metric to carefully account for corner cases unique to RNA-seq. In experimental data, estimates with the best perplexity also best correlate with qPCR measurements. In simulated data, perplexity is well behaved and concordant with genome-wide measurements against ground truth and differential expression analysis. Furthermore, we demonstrate theoretically and experimentally that perplexity can be computed for arbitrary transcript abundance estimation models.

Conclusions: Alongside the derivation and implementation of perplexity for transcript abundance estimation, our study is the first to make possible model selection for transcript abundance estimation on experimental data in the absence of ground truth.

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困惑:在缺乏基本事实的情况下评估转录物丰度估计。
背景:基于RNA-seq数据估计转录物丰度的概率模型和推理方法发展迅速。这些模型旨在准确估计转录水平丰度,考虑测量过程中的不同偏差,甚至评估结果估计中的不确定性,这些不确定性可以传播到后续分析中。通过这种方法推断出的估计的假定准确性支撑了在实验室中常规进行的基于基因表达的分析。虽然已知超参数选择会影响推断丰度的分布(例如产生平滑与稀疏估计),但在实验数据中执行模型选择的策略最多是非正式的。结果:给出了直接评价片段集丰度估计值的困惑度。我们从用于评估语言和主题模型的类似度量中调整了困惑,并扩展了度量,以仔细考虑RNA-seq独有的边缘情况。在实验数据中,具有最佳困惑度的估计值也与qPCR测量值最佳相关。在模拟数据中,困惑表现良好,与全基因组测量结果一致,与基础真理和差异表达分析一致。此外,我们从理论上和实验上证明,可以计算任意转录本丰度估计模型的困惑度。结论:除了推导和实现转录本丰度估计的困惑外,我们的研究首次在缺乏基础真理的情况下,对实验数据进行转录本丰度估计的模型选择。
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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