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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.

项目摘要与测量数百万个个体细胞的全基因组表达的能力一起，技术也带来了将这些数据转化为更好地了解潜在的生物现象。用于单细胞数据分析的现有计算方法和软件具有与可扩展性、准确性、可用性和解释能力相关的关键限制。的主要目标该项目旨在开创一种新的单细胞数据分析平台，能够：i）准确鉴定复杂组织中的细胞类型和它们的组成，ii）推断细胞发育阶段和假- 时间轨迹，和iii）鉴定表型中的细胞类型特异性途径和推定机制对比拟议的平台还将能够对批量表达数据进行去卷积，以识别细胞类型每个散装样品的组成。拟议工作的重要性在于它有可能提供新的本发明提供了用于单细胞数据分析的方法，其远远超过当前最先进技术的性能。精确的解卷积还将使研究人员能够从大量的信息库中提取更多的信息，现有的批量数据，包括GDC/TCGA、NCBI SRA、GEO和ArrayExpress，目前包含这些数据来自于大量的实验，这些实验总共花费了超过10亿美元。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去卷积可以极大地受益于：i）系统级拥有细胞发展的关键特征的知识，以及ii）经验证的细胞类型和参考单细胞图谱中可用的单细胞数据集。事实上，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，如果我们适当地利用参考单细胞数据集和途径知识，大约90-100%。的将通过与最先进的软件进行比较，对拟议的平台进行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，系统水平分析）和批量表达数据的细胞去卷积。这将使用663 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、单细胞数据和匹配的细胞流式细胞术）。进一步提高了路径分析和机理推断能力使用真实的敲除数据集（其中表型的真正原因是已知的）进行验证。公司， Advaita拥有强大的知识产权组合，经验丰富的团队，以及在该领域的良好记录，并将类似的分析平台商业化。Advaita的现有产品目前由顶级校长使用调查人员、核心设施和全球制药公司。