Personalized beyond Precision: Designing Unbiased Gold Standards to Improve Single-Subject Studies of Personal Genome Dynamics from Gene Products.
Personalized beyond Precision: Designing Unbiased Gold Standards to Improve Single-Subject Studies of Personal Genome Dynamics from Gene Products.
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DOI：
10.3390/jpm11010024
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发表时间：
2020-12-31
影响因子：
--
通讯作者：
Lussier YA
中科院分区：
文献类型：
--
作者：
Rachid Zaim S;Kenost C;Zhang HH;Lussier YA
背景：开发以患者为中心的基线标准，使其能够在基因组范围内检测具有临床意义的异常基因产物，仍然是推动个性化医学超越“精确医学”所暗示的小范围受试者所需的一项尚未解决的挑战。这份手稿提出了一种新的参考标准发展的方法，以评估转录本的单一主题分析的准确性，并提供了对蛋白质组和代谢组的扩展。在评估框架中，统计测试的分布假设不完美地模拟了基因产品的基因组动态，人工制品和偏见与真实的信号混淆。当研究在发现集和参考标准中使用相同的分析方法时，模型确认偏差会升级。在这样的研究中，重复的偏见与准确度的衡量标准是混淆的。我们假设，开发与方法无关的参考标准将减少这种复制偏见。我们建议用一个参考标准来评估发现方法，该参考标准来自于与发现方法不同的分析方法的共识，以最大限度地减少统计伪影偏差。我们的方法包括阈值效应大小和表达水平的结果过滤，以提高分析方法之间的一致性。我们开发并发布了R包“ReferenceNof1”，以促进构建健壮的参考标准。结果：由于RNA-Seq数据分析方法往往依赖于二项和负二项假设进行非参数分析，这种差异产生了统计噪声，并使参考标准方法变得依赖。在我们的实验设计中，在两个不同的数据集中，确定了五种类型的RNA分析的30种不同的折叠变化(FC)和表达计数(以下称为“表达”)的组合的准确性。这一设计应用于两个不同的数据集：乳腺癌细胞系和酵母研究，在两个实验条件下进行相同基因的生物复制。此外，参考标准(RS)包括除方法测试准确度之外的所有RNA分析方法。为了减少对特定分析方法的偏差，计算了不同分析方法的观测结果之间的成对Jaccard一致性指数以进行优化。通过阈值效应大小和表达水平的优化减少了不同方法分析结果之间的最大不一致，导致一致性提高了65%。结论：我们已经证明，比较转录学临床优化的不同单一对象分析方法的准确性需要一个新的评估框架。可靠和健壮的参考标准，独立于被评估的方法，可以在有限数量的参数组合下获得：折叠变化(FC)范围阈值、表达水平截止以及将被测试的方法排除在RS开发过程之外。当应用反保守的参考标准框架(例如，使用相同的方法进行RS开发和预测)时，预测与黄金标准(GS)之间的大部分一致性信号无法被其他方法确认，我们认为这是有偏差的结果。从单一受试者研究中确定DEG的统计测试产生了许多有偏见的结果，需要随后的过滤以增加可靠性。传统的单一对象研究涉及一个或几个患者随着时间的推移的测量，需要大量的概念框架扩展，以解决基因产品全基因组分析中的众多测量。拟议的ReferenceNof1框架通过提供一种强有力的方法来构建参考标准，解决了改进转录组规模单主题分析的一些内在挑战。
Background: Developing patient-centric baseline standards that enable the detection of clinically significant outlier gene products on a genome-scale remains an unaddressed challenge required for advancing personalized medicine beyond the small pools of subjects implied by “precision medicine”. This manuscript proposes a novel approach for reference standard development to evaluate the accuracy of single-subject analyses of transcriptomes and offers extensions into proteomes and metabolomes. In evaluation frameworks for which the distributional assumptions of statistical testing imperfectly model genome dynamics of gene products, artefacts and biases are confounded with authentic signals. Model confirmation biases escalate when studies use the same analytical methods in the discovery sets and reference standards. In such studies, replicated biases are confounded with measures of accuracy. We hypothesized that developing method-agnostic reference standards would reduce such replication biases. We propose to evaluate discovery methods with a reference standard derived from a consensus of analytical methods distinct from the discovery one to minimize statistical artefact biases. Our methods involve thresholding effect-size and expression-level filtering of results to improve consensus between analytical methods. We developed and released an R package “referenceNof1” to facilitate the construction of robust reference standards. Results: Since RNA-Seq data analysis methods often rely on binomial and negative binomial assumptions to non-parametric analyses, the differences create statistical noise and make the reference standards method dependent. In our experimental design, the accuracy of 30 distinct combinations of fold changes (FC) and expression counts (hereinafter “expression”) were determined for five types of RNA analyses in two different datasets. This design was applied to two distinct datasets: Breast cancer cell lines and a yeast study with isogenic biological replicates in two experimental conditions. Furthermore, the reference standard (RS) comprised all RNA analytical methods with the exception of the method testing accuracy. To mitigate biases towards a specific analytical method, the pairwise Jaccard Concordance Index between observed results of distinct analytical methods were calculated for optimization. Optimization through thresholding effect-size and expression-level reduced the greatest discordances between distinct methods’ analytical results and resulted in a 65% increase in concordance. Conclusions: We have demonstrated that comparing accuracies of different single-subject analysis methods for clinical optimization in transcriptomics requires a new evaluation framework. Reliable and robust reference standards, independent of the evaluated method, can be obtained under a limited number of parameter combinations: Fold change (FC) ranges thresholds, expression level cutoffs, and exclusion of the tested method from the RS development process. When applying anticonservative reference standard frameworks (e.g., using the same method for RS development and prediction), most of the concordant signal between prediction and Gold Standard (GS) cannot be confirmed by other methods, which we conclude as biased results. Statistical tests to determine DEGs from a single-subject study generate many biased results requiring subsequent filtering to increase reliability. Conventional single-subject studies pertain to one or a few patient’s measures over time and require a substantial conceptual framework extension to address the numerous measures in genome-wide analyses of gene products. The proposed referenceNof1 framework addresses some of the inherent challenges for improving transcriptome scale single-subject analyses by providing a robust approach to constructing reference standards.
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DOI：
10.1136/amiajnl-2013-002519
发表时间：
2014-11
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
--
作者：
Gardeux V;Achour I;Li J;Maienschein-Cline M;Li H;Pesce L;Parinandi G;Bahroos N;Winn R;Foster I;Garcia JG;Lussier YA
通讯作者：
Lussier YA
DOI：
10.1093/jamia/ocx069
发表时间：
2017-11-01
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
--
作者：
Gardeux V;Berghout J;Achour I;Schissler AG;Li Q;Kenost C;Li J;Shang Y;Bosco A;Saner D;Halonen MJ;Jackson DJ;Li H;Martinez FD;Lussier YA
通讯作者：
Lussier YA
DOI：
10.1093/bioinformatics/btv253
发表时间：
2015-06-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Schissler AG;Gardeux V;Li Q;Achour I;Li H;Piegorsch WW;Lussier YA
通讯作者：
Lussier YA
DOI：
10.1109/embc.2015.7319872
发表时间：
2015
期刊：
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
影响因子：
--
作者：
Sha Y;Phan JH;Wang MD
通讯作者：
Wang MD
影响因子：
1.8
作者：
McShane, Blakeley B.;Gal, David;Tackett, Jennifer L.
通讯作者：
Tackett, Jennifer L.