DMS/NIGMS 1: Addressing Measurement Limitations for Sequence Count Data

序列计数数据（例如，16 S rRNA测序或单细胞RNA-seq）在现代细胞中普遍存在。 生物医学研究然而，即使在没有测量噪声和实验限制的情况下， 由于设计的局限性，这些数据传达的关于被测量的底层生物系统的信息有限。 除了常见的局限性，如不适当的研究设计，其他两种形式的局限性， 影响甚至主导研究结论。规模限制的出现是因为 研究中的系统（例如，人肠道中的细菌总数）通常与规模无关 的数据。相比之下，测量偏差使观察到的计数分布偏斜，因为一些实体 与其他人相比，系统性地代表性不足。尽管认识到这些问题，我们缺乏 用于根据这些限制进行和评估序列计数数据分析的工具。这里我们 开发新的统计理论和工具，以解决测量偏差和规模限制问题。这 提案有三个目标。(1)建立一个理论框架，客观地评估现有的方法， 鉴于这些局限性。(2)发展模拟推理作为一种新的理论和计算 一个框架，允许分析师使用他们喜欢的模型和软件，同时考虑到不确定性 由于这些数据的局限性。(3)将这些工具应用于以下三个案例研究： 真实的序列计数数据。总的来说，这些目标提供了新的理论和计算工具， 评估和执行对这些数据限制具有鲁棒性的序列计数数据分析。的 拟议的工作也大大偏离了现状。与现有方法相比， 解决这些数据的限制，通过假设，往往是隐含的，我们发展统计理论， 这些工具明确地对这些假设中的不确定性和潜在误差进行建模。我们证明，这 方法可以导致较低的I型和II型错误在理论上和实践中。这些工具将 增强序列计数数据分析的可重复性和严谨性，这是跨项目的核心。 美国国立卫生研究院。 相关性（参见说明）： DNA测序用于分析不同细菌的数量或不同基因的表达 在一个有机体中。然而测量过程的局限性（例如，测量偏差）限制了我们的能力 使用这些数据。这项工作将开发新的统计方法，使科学家能够解释这些问题。 数据的局限性，从而增加我们对人类健康和疾病的了解。

Sequence count data (e.g., 16S rRNA sequencing or single-cell RNA-seq) are ubiquitous in modern biomedical research. Yet even in the absence of measurement noise and limitations of experimental design, these data convey limited information about the underlying biological system being measured. Beyond familiar limitations such as inappropriate study design, two other forms of limitations have been shown to impact or even dominate study conclusions. Scale limitations arise because the scale of the system under study (e.g., the total number of bacteria in a persons gut) is typically independent of the scale of the data. In contrast, measurement bias skews the observed distribution of counts as some entities are systematically underrepresented compared to others. Despite an appreciation of these problems, we lack tools for performing and evaluating analyses of sequence count data in light of these limitations. Here we develop new statistical theory and tools for addressing measurement bias and scale limitations. This proposal has 3 aims. (1) Develop a theoretical framework for objectively evaluating existing approaches in light of these limitations. (2) Develop Simulated Inference as a new theoretical and computational framework which allows analysts to use their preferred models and software while incorporating uncertainty stemming from these data limitations. (3) Validate these tools through application to three case-studies of real sequence count data. In total, these aims provide new theoretical and computational tools for evaluating and performing analyses of sequence count data that are robust to these data limitations. The proposed work is also a substantial departure from the status quo. In contrast to existing methods which address these data limitations through assumptions that are often implicit, we develop statistical theory and tools that explicitly model uncertainty and potential error in those assumptions. We demonstrate that this approach can lead to lower Type-I and Type-II errors both in theory and in practice. Overall these tools will enhance the reproducibility and rigor of sequence count data analysis which is central to projects across the NIH. RELEVANCE (See instructions): DNA sequencing is used to profile the amount of different bacteria or the expression of different genes within an organism. Yet limitations of the measurement process (e.g., measurement bias) restrict our ability to use this data. This work will develop new statistical methods which enable scientists to account for these data limitations and therefore to increase our understanding of human health and disease.