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

序列计数数据(例如，16S rRNA测序或单细胞RNA-seq)在现代 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 设计中，这些数据传达了关于被测量的潜在生物系统的有限信息。 除了熟悉的限制，如不适当的研究设计，还有另外两种形式的限制 被证明会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，一个人肠道中的细菌总数)通常与规模无关 数据的一部分。相反，测量偏差会扭曲观察到的计数分布，因为有些实体就是这样 与其他人相比，系统性地代表不足。尽管我们认识到这些问题，但我们缺乏 根据这些限制执行和评估顺序计数数据分析的工具。在这里我们 开发新的统计理论和工具，以解决测量偏差和规模限制。这 提案有三个目标。(1)制定客观评估现有方法的理论框架 考虑到这些限制。(2)发展模拟推理作为一种新的理论和计算方法 框架，允许分析师在考虑不确定性的同时使用他们首选的模型和软件 源于这些数据限制。(3)通过三个案例研究对这些工具进行验证 实数顺序计数数据。总之，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稳健的顺序计数数据的分析。这个 拟议的工作也大大偏离了现状。与现有方法相比，现有方法 通过通常隐含的假设来解决这些数据限制，我们开发了统计理论和 对这些假设中的不确定性和潜在错误进行明确建模的工具。我们证明了这一点 无论在理论上还是在实践中，这种方法都可以减少类型I和类型II的错误。总的来说，这些工具将 提高顺序计数数据分析的可重复性和严谨性，这是整个项目的核心 美国国立卫生研究院。 相关性(请参阅说明)： DNA测序被用来分析不同细菌的数量或不同基因的表达 在一个有机体内。然而，测量过程的局限性(例如，测量偏差)限制了我们的能力 来使用这些数据。这项工作将开发新的统计方法，使科学家能够解释这些 数据限制，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.