Efficient Methods for Dimensionality Reduction ofSingle-Cell RNA-Sequencing Data

项目综述：单细胞RNA测序数据降维的有效方法 单细胞RNA测序是一项革命性的技术，使人类生理学和 疾病。单细胞RNA测序实验产生的数据集如此之大，以至于它们不可能 使用传统统计方法进行分析或可视化，直到使用 名为“降维”的技术。几乎所有的单细胞rna测序分析都是从 使用一种称为主成分分析(PCA)的技术来实现降维。 然而，单细胞RNA测序带来了独特的挑战，使得PCA变得困难。首先，这些东西的大小 数据集如此之大，以至于计算PCA需要专门的硬件和数小时。快速算法可实现 近似主成分分析已被证明显著加快了这一过程，但并未在 单细胞-RNA测序社区，部分原因是还没有并行算法在R 计算机语言。其次，主成分分析要求研究人员决定数据集的最终期望大小。 选择太小的尺码会导致丢弃有价值的生物学见解，而选择太大的尺码会导致放弃有价值的生物学见解 增加噪音。然而，对于如何选择单细胞rna的最佳大小，还没有达成共识。 测序，有证据表明这个大小可能被系统性地低估了。最后，主成分分析不能 直接应用于在单细胞RNA测序中测量的计数数据，因此研究人员必须首先应用 对其进行归一化处理的技术。目前该领域的标准是应用对数变换- 然而，最近的几项研究表明，对数变换在单细胞RNA中产生统计偏差 测序。在这项研究中，专门针对单细胞RNA执行PCA的快速方法- 将开发测序数据：1A)严格衡量变化后果的框架 几个公开可用的单细胞RNA测序数据集最终结果的前处理参数 使主成分分析能够在单细胞RNA测序数据上进行实验。1b)超高速、并行化 随机主成分分析的实现允许研究人员使用标准笔记本电脑快速执行主成分分析 单细胞RNA测序数据。2)表演时严格选择最终尺寸的技术 单细胞RNA测序数据集的主成分分析。3)一种转化单细胞的方法 RNA测序数据，以使其适当分布，从而能够正确使用PCA，而无需 招致统计偏差的。该奖学金还包括一份详细的培训计划，其中包含有价值的学习内容。 申请者作为一名能应用高等医学方法的内科科学家的发展经验 量纲统计在解决生物医学问题中的作用。

Project Summary: Efficient Methods for Dimensionality Reduction of Single-Cell RNA-Sequencing Data Single-cell RNA-sequencing is a revolutionary technology enabling discoveries in human physiology and disease. The datasets generated from single-cell RNA-sequencing experiments are so large that they cannot be analyzed or visualized using traditional statistical methods until the datasets have been shrunk using a technique named “dimensionality reduction.” Almost every analysis of single-cell RNA-sequencing begins using a technique named principal component analysis (PCA) to accomplish dimensionality reduction. However, single-cell RNA-sequencing presents unique challenges making PCA difficult. First, the size of these datasets is so large that computing PCA requires specialized hardware and multiple hours. Fast algorithms to approximate PCA have been shown to dramatically speed up this process, but have not proliferated in the single cell-RNA sequencing community, in part because no parallelized algorithm has been written in the R computing language. Second, PCA requires the researcher to decide the final desired size of the dataset. Choosing too small of a size results in discarding valuable biological insights, while choosing too large a size increases the noise. However, there is no consensus on how to pick the optimal size for single-cell RNA sequencing, and there is evidence that this size might be systematically underestimated. Lastly, PCA cannot be applied directly to the count-data measured in single cell RNA sequencing, so researchers must first apply a preprocessing technique to normalize it. The current standard in the field is to apply the log transform – however, several recent studies have shown that the log transform creates statistical biases in single-cell RNA sequencing. In this fellowship, specifically tailored, fast methods for performing PCA on single-cell RNA- sequencing data will be developed: 1a) A framework to rigorously measure the consequence of changing preprocessing parameters on the final results of several publicly available single cell RNA sequencing datasets to enable experimentation of PCA on single-cell RNA-sequencing data. 1b) An ultra-fast, parallelized implementation of randomized PCA allowing researchers using standard laptops to rapidly perform PCA on single cell RNA sequencing data. 2) A technique for rigorously choosing the final size when performing principal component analysis for single-cell RNA-sequencing datasets. 3) A method for transforming single-cell RNA-sequencing data so that it becomes appropriately distributed enabling proper usage of PCA without incurring statistical biases. This fellowship also includes a detailed training plan with valuable learning experiences for the applicant’s development as a physician-scientist who can apply methods from high dimensional-statistics to solving biomedical problems.