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A web-based platform for robust single-cell analysis, bulk data deconvolution and system-level analysis

基于网络的平台，用于强大的单细胞分析、批量数据反卷积和系统级分析

基本信息

批准号：
10766073
负责人：
Cristiana Iosef
金额：
$ 87.8万
依托单位：
ADVAITA CORPORATION
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10766073
关键词：
3-Dimensional Academia Area Atlases Automobile Driving Bioinformatics Biological Biological Monitoring Biological Phenomena Biomedical Research Cell Extracts Cells Characteristics Complex Computer software Computing Methodologies Core Facility Data Data Analyses Data Set Development Diagnostic Feedback Flow Cytometry Gene Expression Genes Goals Human Immunology Individual Industry Knock-out Knowledge Life Measurement Measures Methodology Methods Mission Music Neurobiology Organ Pathway Analysis Pathway interactions Performance Pharmacologic Substance Phase Phenotype Principal Investigator Research Personnel Resolution Sampling Scientist System Techniques Technology The Cancer Genome Atlas Time Tissues Translating Whole Organism Work analysis pipeline anticancer research base biological systems cell type cellular development commercialization comparative cost deep learning design drug discovery experience experimental study genome-wide gradient boosting high dimensionality learning algorithm phenotypic data random forest repository single cell analysis single cell sequencing single cell technology single-cell RNA sequencing software development tool transcriptome transcriptome sequencing transfer learning unsupervised learning usability user friendly software web platform

项目摘要

PROJECT SUMMARY Together with the ability to measure genome-wide expression of millions of individual cells, single-cell technologies have also brought the challenge of translating such data into a better understanding of the underlying biological phenomena. Existing computational methods and software for single-cell data analysis have critical limitations related to scalability, accuracy, usability, and interpretation capabilities. The main goal of this project is to pioneer a new platform for the analysis of single-cell data that is capable of: i) accurately identifying cell types and their composition in complex tissues, ii) inferring cell developmental stages and pseudo- time trajectories, and iii) identifying cell-type-specific pathways and putative mechanisms in a phenotype comparison. The proposed platform will also be able to deconvolve bulk expression data to identify the cell type composition of each bulk sample. The significance of the proposed work lies in its potential to provide new methodologies for single-cell data analysis that far exceed the performance of current state-of-the-art techniques. The accurate deconvolution will also allow researchers to extract more information from the vast repositories of existing bulk data, including GDC/TCGA, NCBI SRA, GEO, and ArrayExpress, which are currently containing data from bulk experiments that collectively cost over a billion dollars. The hypothesis driving this work is that single-cell data analysis and cellular deconvolution of bulk data can greatly benefit from: i) the systems-level knowledge that holds key characteristics for cellular developments, and ii) the valuable information available in validated cell types and reference single-cell datasets available in single-cell atlases. Indeed, our preliminary work shows that single-cell data analysis and cellular deconvolution can achieve an outstanding accuracy of approximately 90—100% if we properly utilize reference single-cell datasets and pathway knowledge. The proposed platform will be extensively validated by comparing its capabilities against the state-of-the-art software in both single-cell data analysis (cell type identification, developmental states and time-trajectory inference, systems-level analysis) and cellular deconvolution of bulk expression data. This will be done using both 663 datasets representing 279 cell types and 116 human organ parts (including bulk data, single-cell data, and matched cell flow cytometry). The pathway analysis and mechanisms inference capabilities will be further validated using real knock-out datasets (in which the true cause of the phenotype is known). The company, Advaita, has a strong IP portfolio, an experienced team, and a proven track record in this area, having developed and commercialized similar analysis platforms. Advaita's existing products are currently used by top principal investigators, core facilities, and pharmaceutical companies around the world.

项目概要连同测量数百万个单个细胞的全基因组表达的能力，单细胞技术也带来了将这些数据转化为更好地理解的挑战潜在的生物现象。现有的单细胞数据分析计算方法和软件在可扩展性、准确性、可用性和解释能力方面存在严重限制。主要目标该项目旨在开创一个用于分析单细胞数据的新平台，该平台能够：i）准确地识别复杂组织中的细胞类型及其组成，ii) 推断细胞发育阶段和伪细胞时间轨迹，以及 iii) 识别表型中的细胞类型特异性途径和推定机制比较。拟议的平台还能够对批量表达数据进行反卷积以识别细胞类型每个批量样品的成分。拟议工作的意义在于它有可能提供新的单细胞数据分析方法远远超出了当前最先进技术的性能。准确的反卷积还将使研究人员能够从庞大的数据库中提取更多信息。现有的批量数据，包括 GDC/TCGA、NCBI SRA、GEO 和 ArrayExpress，目前包含来自总共花费超过十亿美元的批量实验的数据。推动这项工作的假设是单细胞数据分析和批量数据的细胞反卷积可以极大地受益于：i) 系统级掌握细胞发育关键特征的知识，以及 ii) 中可用的有价值的信息单细胞图集中提供经过验证的细胞类型和参考单细胞数据集。确实，我们的初步工作表明单细胞数据分析和细胞反卷积可以实现出色的精度如果我们正确利用参考单细胞数据集和通路知识，大约可以达到 90-100%。这所提出的平台将通过将其功能与最先进的软件进行比较来得到广泛验证在单细胞数据分析（细胞类型识别、发育状态和时间轨迹推断）中，系统级分析）和批量表达数据的细胞反卷积。第663章代表 279 种细胞类型和 116 个人体器官部分的数据集（包括批量数据、单细胞数据和匹配细胞流式细胞术）。路径分析和机制推理能力将进一步提升使用真实的敲除数据集进行验证（其中表型的真正原因已知）。公司， Advaita 拥有强大的知识产权组合、经验丰富的团队以及该领域良好的业绩记录，并将类似的分析平台商业化。 Advaita现有产品目前已被顶级校长使用世界各地的研究人员、核心设施和制药公司。