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

• 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
• 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

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 用于单细胞数据分析的现有计算方法和软件。 在可扩展性、准确性、可用性和解释能力方面存在严重限制。 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 所提出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。 所提出的工作的重要性在于它提供新的潜力。 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 事实上，我们的初步单细胞图集中提供了经过验证的细胞类型和参考单细胞数据集。 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%。 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， 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 匹配的细胞流式细胞仪）的通路分析和机制推断能力将得到进一步提升。 使用真实的敲除数据集进行验证（其中已知表型的真正原因）， 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， Advaita现有产品已商业化，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。

项目成果

A novel approach for predicting upstream regulators (PURE) that affect gene expression.

一种预测影响基因表达的上游调节因子（PURE）的新方法。

• 发表时间: 2023-10-30
• 影响因子: 4.6
• 作者: Nguyen, Tuan;Craig, Douglas B;Tran, Duc;Nguyen, Tin;Draghici, Sorin
• 通讯作者: Draghici, Sorin

Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges.

细胞反卷积十四年：方法、应用、技术评估和突出挑战。

• 发表时间: 2024-04-15
• 影响因子: 14.9
• 作者: Nguyen, Hung;Nguyen, Ha;Tran, Duc;Draghici, Sorin;Nguyen, Tin
• 通讯作者: Nguyen, Tin