Deep Learning Methods to Integrate Biological Information for Analysis of Single-cell RNAseq Data

整合生物信息进行单细胞 RNAseq 数据分析的深度学习方法

• 批准号: 10291567
• 负责人: Zhi Wei
• 金额: $ 45.08万
• 依托单位: NEW JERSEY INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-22 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10291567
• 关键词: Algorithms; Architecture; Area; Binomial Model; Bioinformatics; Biological; Cells; Charge; Cluster Analysis; Collaborations; Complex; Computers; Data; Data Analyses; Data Set; Detection; Development; Dimensions; Documentation; Dropout; Euclidean Space; Event; Frequencies; Goals; Grant; Graph; Hair follicle structure; Human; Investigation; Knowledge; Lead; Maps; Measurement; Methodology; Methods; Modeling; Natural regeneration; Paneth Cells; Pennsylvania; Performance; Phenotype; Regulation; Research Personnel; Research Project Grants; Science; Scientist; Students; Supervision; Technology; Transcript; Triplet Multiple Birth; Universities; Visualization software; Work; autoencoder; base; biological systems; career; cell type; computer program; computerized tools; data visualization; deep learning; design; experimental study; flexibility; genomic data; improved; insight; interest; learning strategy; loss of function; machine learning method; medical schools; novel; programs; recruit; single cell analysis; single-cell RNA sequencing; transcriptome; transcriptome sequencing; undergraduate student

Project Summary The broad long-term objective of the project concerns the development of novel machine learning methods and computational tools for modeling genomic data motivated by important biological questions and experiments. The analysis of single-cell RNAseq (scRNAseq) data presents substantial computational and bioinformatics challenges. The specific aim of the project is to develop novel model-based deep learning methods with prior biological information considered for modelling scRNAseq data. These problems are all motivated by the PI’s close collaborations with biomedical investigators. The proposed approaches are designed to integrate biological information for improving both analytical performance and biological interpretability. The methods hinge on novel integration of biological insights and deep learning methods for analysis of the noisy, sparse, and over-dispersed scRNAseq data, including zero- inflated negative binominal model, autoencoder, deep embedding, hyperbolic embedding, and reversed graph embedding. The new methods can be applied to two important biological problems using the scRNAseq technologies: cell type identification and discovery via clustering analysis and cell developments via trajectory inference. They will facilitate effective analyses of the increasingly important scRNAseq data sets and contribute to the important on-going studies that the PI is currently collaborating on, Paneth cell regulation and regeneration of human hair follicles. The project will develop practical and feasible computer programs in order to implement the proposed methods, and to evaluate the performance of these methods through real applications. The work proposed here will contribute deep learning methods to modeling scRNAseq data and to studying complex phenotypes and biological systems and offer insights into each of the biological areas represented by the various data sets. All programs developed under this grant and detailed documentation will be made available free-of-charge to interested researchers. Undergraduates researchers from diverse backgrounds will be recruited as an integral part in the project for implementing most critical parts of the proposed aims. The research project will stimulate the interests of students so that they can consider a career in the biomedical sciences.

项目摘要 该项目的广泛的长期目标涉及新机器的开发 用于对基因组数据建模的学习方法和计算工具， 生物问题和实验。单细胞RNAseq（scRNAseq）数据的分析 提出了大量的计算和生物信息学挑战。的具体目标 该项目旨在开发基于先验生物信息的新型模型深度学习方法 考虑用于对scRNAseq数据建模。这些问题都是由PI的关闭动机 与生物医学研究人员的合作。建议的方法旨在 用于改进分析性能和生物学性能 可解释性这些方法取决于生物学见解和深度学习的新整合 用于分析噪声、稀疏和过度分散的scRNAseq数据的方法，包括零- 膨胀负二项模型，自动编码器，深度嵌入，双曲线嵌入， 反向图嵌入这些新方法可以应用于两种重要的生物学 使用scRNAseq技术的问题：通过聚类进行细胞类型识别和发现 通过轨迹推断进行分析和细胞发育。它们将有助于有效分析 越来越重要的scRNAseq数据集，并有助于重要的正在进行的研究 PI目前正在合作的项目，潘氏细胞调节和人类毛发再生 毛囊该项目将开发实际可行的计算机程序，以便 实施所提出的方法，并评估这些方法的性能，通过 真实的应用。这里提出的工作将为建模贡献深度学习方法 scRNAseq数据和研究复杂的表型和生物系统，并提供见解 到由各种数据集表示的每个生物区域中。开发的所有程序 根据这项赠款和详细的文件将免费提供给感兴趣的 研究人员来自不同背景的本科生研究人员将被招募为 项目的组成部分，以执行拟议目标的最关键部分。的 研究项目将激发学生的兴趣，使他们能够考虑在 生物医学科学

项目成果

Clustering of single-cell multi-omics data with a multimodal deep learning method.

• DOI: 10.1038/s41467-022-35031-9
• 发表时间: 2022-12-13
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Lin X;Tian T;Wei Z;Hakonarson H
• 通讯作者: Hakonarson H