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

基本信息

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
项目类别：
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

项目摘要

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：降低单细胞RNA-Sere-Sere-Ser-Ser-Se-Sterionding数据的有效方法单细胞RNA测序是一项革命性的技术，在人类生理学和疾病。由单细胞RNA序列实验产生的数据集是如此之大，以至于不能是使用传统统计方法对数据集进行了分析或可视化，直到使用称为“降低维度”的技术。几乎所有对单细胞RNA的分析开始使用名为主组件分析（PCA）的技术来降低维度。但是，单细胞RNA测序提出了独特的挑战，使PCA变得困难。首先，这些大小数据集是如此之大，以至于计算PCA需要专门的硬件和多小时。快速算法已显示近似PCA可以显着加快此过程的速度，但没有在单细胞-RNA测序社区，部分原因是没有在R中写入并行的算法计算语言。其次，PCA要求研究人员决定数据集的最终所需尺寸。选择太小的尺寸会导致丢弃有价值的生物学见解，同时选择太大的尺寸增加噪音。但是，如何选择单细胞RNA的最佳尺寸尚无共识测序，并且有证据表明这种大小可能会被系统地低估。最后，PCA不能是直接应用于单细胞RNA测序中测量的计数数据，因此研究人员必须首先应用预处理技术使其正常化。该领域的当前标准是应用日志变换 - 但是，最近的一些研究表明，对数变换会在单细胞RNA中产生统计偏差测序。在此奖学金中，专门针对单细胞RNA-执行PCA的快速方法 - 将开发测序数据：1A）严格衡量更改后果的框架几个公开可用的单细胞RNA测序数据集的最终结果的预处理参数为了实现PCA在单细胞RNA-Sere-Ser-Ser-se-er-sepering数据上的实验。 1b）超快速的平行实施随机PCA，允许研究人员使用标准笔记本电脑快速执行PCA 单细胞RNA测序数据。 2）执行时严格选择最终尺寸的技术单细胞RNA序列数据集的主成分分析。 3）一种转换单细胞的方法 RNA序列数据，以便它变得适当分布，可以适当使用PCA 产生统计偏见。该奖学金还包括一项详细的培训计划，并具有宝贵的学习作为身体科学家的申请人发展的经验，可以应用高度的方法解决生物医学问题的维度统计。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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

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

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.