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

单细胞 RNA 测序数据降维的有效方法

• 批准号: 10356883
• 负责人: James Michael Garritano
• 金额: $ 5.18万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-16 至 2023-03-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10356883
• 关键词: Address; Adopted; Algorithms; Biological; Cells; Code; Collection; Communities; Computer Hardware; Computing Methodologies; Consensus; Data; Data Analyses; Data Set; Development; Dimensions; Disease; Evaluation; Fellowship; Gaussian model; Genes; Hour; Human; Language; Learning; Libraries; Mathematics; Measures; Mentorship; Methods; Modeling; Modernization; Names; Noise; Normal Statistical Distribution; Paper; Physicians; Physiology; Population; Principal Component Analysis; Process; Publishing; RNA; Randomized; Research Personnel; Resolution; Running; Scientist; Speed; Statistical Bias; Statistical Methods; Systematic Bias; Techniques; Technology; Time; Tissues; Training; Variant; Visualization; base; design; dimensional analysis; distributed data; experience; experimental study; high dimensionality; improved; insight; laptop; non-Gaussian model; parallelization; professor; single cell analysis; single-cell RNA sequencing; statistics; supercomputer; theories; tool; transcriptome; transcriptome sequencing

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.

项目摘要：减少单细胞RNA测序数据重复性的有效方法 单细胞RNA测序是一项革命性的技术，能够发现人类生理学和 疾病单细胞RNA测序实验生成的数据集非常大， 使用传统的统计方法进行分析或可视化，直到使用 这就是所谓的“降维”技术。几乎每一次单细胞RNA测序分析都始于 使用称为主成分分析（PCA）的技术来实现降维。 然而，单细胞RNA测序提出了独特的挑战，使PCA变得困难。第一，这些 数据集是如此之大，以至于计算PCA需要专门的硬件和多个小时。快速算法， 近似PCA已被证明可以大大加快这一过程，但在 单细胞RNA测序社区，部分原因是没有并行算法已经写在R 计算机语言其次，PCA要求研究人员决定数据集的最终期望大小。 选择太小的尺寸会导致丢弃有价值的生物学见解，而选择太大的尺寸 增加了噪音。然而，对于如何选择单细胞RNA的最佳大小， 测序，有证据表明，这一规模可能被系统地低估。最后，PCA不能 直接应用于单细胞RNA测序中测量的计数数据，因此研究人员必须首先应用 该领域的当前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中产生了统计偏差 测序在这项研究中，专门定制的，快速的方法进行PCA的单细胞RNA- 测序数据将被开发：1a）一个框架，以严格衡量改变的后果 预处理参数对几个公开可用的单细胞RNA测序数据集的最终结果 以使PCA在单细胞RNA测序数据上的实验成为可能。1b）一个超快、并行化的 实施随机PCA，使研究人员能够使用标准笔记本电脑快速对 单细胞RNA测序数据。2)一种在表演时严格选择最终尺寸的技巧 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，使其变得适当分布，从而能够正确使用PCA， 导致统计偏差。该奖学金还包括一个详细的培训计划与宝贵的学习 申请人作为一名医生，科学家的发展经验，可以从高层次应用方法 解决生物医学问题。

项目成果

Quantitative assessment of p16 expression in FNA specimens from head and neck squamous cell carcinoma and correlation with HPV status.

• DOI: 10.1002/cncy.22399
• 发表时间: 2021-05
• 期刊: Cancer cytopathology
• 影响因子: 3.4
• 作者: Abi-Raad R;Prasad ML;Gilani S;Garritano J;Barlow D;Cai G;Adeniran AJ
• 通讯作者: Adeniran AJ

RAS mutation and associated risk of malignancy in the thyroid gland: An FNA study with cytology-histology correlation.

• DOI: 10.1002/cncy.22537
• 发表时间: 2022-04
• 期刊: CANCER CYTOPATHOLOGY
• 影响因子: 3.4
• 作者: Gilani, Syed M.;Abi-Raad, Rita;Garritano, James;Cai, Guoping;Prasad, Manju L.;Adeniran, Adebowale J.
• 通讯作者: Adeniran, Adebowale J.

Anaplastic Thyroid Carcinoma: Cytomorphologic Features on Fine-Needle Aspiration and Associated Diagnostic Challenges.

甲状腺未分化癌：细针抽吸的细胞形态学特征及相关诊断挑战。

• DOI: 10.1093/ajcp/aqab159
• 发表时间: 2022
• 期刊: American journal of clinical pathology
• 影响因子: 3.5
• 作者: Podany,Peter;Abi-Raad,Rita;Barbieri,Andrea;Garritano,James;Prasad,ManjuL;Cai,Guoping;Adeniran,AdebowaleJ;Gilani,SyedM
• 通讯作者: Gilani,SyedM