Statistical Methods for Single-Cell RNA-seq Experiments

单细胞 RNA-seq 实验的统计方法

• 批准号: 9321929
• 负责人: Christina Kendziorski
• 金额: $ 33.28万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9321929
• 关键词: Address; Area; Attenuated; Biological; Case Study; Cells; Communication; Communities; Complex; Data; Data Analyses; Development; Diagnostic; Disease; Embryonic Development; Ensure; Environment; Frequencies; Galaxy; Gene Expression; Genes; Genome; Homeostasis; Imagery; Letters; Malignant Neoplasms; Masks; Measurement; Messenger RNA; Methods; Modeling; Morphologic artifacts; Noise; Organism; Phase; Protocols documentation; Research; Research Personnel; Residual state; Resistance development; Signal Transduction; Solid; Source; Statistical Methods; Structure; Technology; Variant; base; differential expression; experimental study; improved; insight; interest; mRNA Expression; new technology; novel; prevent; response; simulation; single cell sequencing; tool; transcriptome sequencing

Abstract Single-cell RNA-sequencing (scRNA-seq) has emerged as a revolutionary tool that allows us to address scientific questions that were elusive just a few years ago. Specifically, the scRNA-seq technology has the potential to enable insights into how a single cell develops into a complex organism, how cell-to-cell variation functions to maintain homeostasis, and how diseases such as cancer develop resistance to treatment. Unfortunately, much of this potential has yet to be realized as statistical methods to analyze scRNA-seq data are lacking. For many types of analyses, the methods currently in use obscure and, in some cases, distort biological signals. A number of statistical and computational challenges must be addressed to prevent inaccurate conclusions, and to optimize novel discovery. This proposal addresses those challenges. In particular, we propose normalization methods to adjust scRNA-seq data for artifacts and thereby ensure robust and accurate downstream inference. We also propose statistical methods to characterize and remove variability induced by oscillatory genes which can mask effects under study. By de-noising the data, these methods will improve the power with which signals of interest can be studied. Finally, we propose methods for identifying cell sub-populations and characterizing their differences across biological conditions. These types of methods are required for identifying novel sub-populations and for characterizing the ways in which cell- specific expression changes in response to various environments. Successful completion of the project will help to ensure that maximal information is obtained from powerful scRNA-seq experiments.

摘要 单细胞RNA测序（scRNA-seq）已经成为一种革命性的工具，使我们能够解决 几年前还难以理解的科学问题。具体而言，scRNA-seq技术具有 有可能使人们了解单个细胞如何发展成为一个复杂的有机体，细胞与细胞之间的变异， 维持体内平衡的功能，以及癌症等疾病如何对治疗产生抗药性。 不幸的是，这种潜力的大部分尚未被实现为分析scRNA-seq数据的统计方法 缺乏。对于许多类型的分析，目前使用的方法模糊，在某些情况下，扭曲 生物信号必须解决一些统计和计算挑战，以防止 不准确的结论，并优化新的发现。本提案针对这些挑战。在 特别是，我们提出了标准化方法来调整scRNA-seq数据的伪影，从而确保鲁棒性。 和准确的下游推理。我们还提出了统计方法来表征和去除 振荡基因引起的变异性可以掩盖研究中的效应。通过对数据进行降噪， 这些方法将提高研究感兴趣信号的能力。最后，我们提出的方法， 鉴定细胞亚群并表征它们在生物条件下的差异。这些类型的 需要方法来鉴定新的亚群和表征细胞- 特定的表达会随着环境的变化而变化。该项目的成功完成将 有助于确保从强大scRNA-seq实验中获得最大信息。