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Imputing quantitative mass spectrometry proteomics data using non-negative matrix factorization

使用非负矩阵分解估算定量质谱蛋白质组数据

基本信息

批准号：
10677226
负责人：
Lincoln Jeffery Harris
金额：
$ 3.82万
依托单位：
UNIVERSITY OF WASHINGTON
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：
2023-04-16 至 2026-04-15
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10677226
关键词：
Address Aging Alzheimer&apos s Disease Alzheimer&apos s disease patient Alzheimer’s disease biomarker Amyloid beta-Protein Benchmarking Biological Biology Brain region Cells Cerebrospinal Fluid Computer software Computing Methodologies Data Data Discovery Data Set Dimensions Disease FASTK Gene Funding Future Genetic Joints Knowledge Label Learning Left Link Machine Learning Malignant Neoplasms Mass Spectrum Analysis Measurement Measures Messenger RNA Methods Molecular Molecular and Cellular Biology Network-based Neural Network Simulation Neurodegenerative Disorders Noise Pathogenesis Patients Pattern Peptides Performance Persons Prevalence Procedures Process Prognosis Proteins Proteome Proteomics Public Health Publishing Reproducibility Research Personnel Running Sampling Software Tools Therapeutic Intervention Training United States National Institutes of Health Work age related asymptomatic Alzheimer&apos s disease biomarker discovery biomarker identification comorbidity computerized data processing deep neural network differential expression experimental study global health hyperphosphorylated tau improved ionization laser capture microdissection learning strategy light weight machine learning method malformation mass spectrometer novel open source patient biomarkers phosphoproteomics specific biomarkers statistical learning therapeutic target virtual

项目摘要

PROJECT SUMMARY/ABSTRACT Alzheimer's disease (AD) represents an emerging global health threat and is a expected to double in prevalence by 2050. AD is a disease of malformed proteins, and signiﬁcant progress has been made characterizing the AD proteome with mass spectrometery. However, data missingness represents a signiﬁcant barrier to the interpretation of existing AD mass spectrometry experiments. Missingness refers to peptides or proteins that are present in the biological sample but are not detected by the mass spectrometer due to various technical factors. This project will address missingness by developing machine learning methods for imputing, or estimating, missing values in quantitative mass spectrometry data. The project will develop two separate imputation methods, one using non-negative matrix factorization and the other deep neural networks. These imputation methods will increase the reproducibility and statistical power of mass spectrometry experiments and will enable new discoveries in existing proteomics experiments. These imputation methods will be applicable to virtually any kind of mass spectrometry experiment – tandem mass tag, data dependent acquisition, data independent acquisition, spectral counts, label-free quantiﬁcation, etc. These imputation methods will be released as lightweight, open-source and easy-to-use software packages and may be incorporated into existing data processing workﬂows. I will demonstrate the utility of these imputation methods by reanalysing data from several existing AD proteomic studies. My imputation methods will identify novel differentially expressed proteins, co-expression modules and AD biomarkers in these existing datasets. I will also analyze unpublished data-independent acquisition (DIA) proteomics data derived from AD patient cerebrospinal ﬂuid samples. Here I will focus on identifying biomarkers that differentiate between patients based on genetic background and co-morbidity status. I will also identify biomarkers of patients with asymptomatic AD. The imputation methods developed by this proposal will enable future discoveries by independent AD researchers. This proposal aligns with the NIA Strategic Direction seeking to "identify and understand the genetic, molecular and cellular mechanisms underlying the pathogenesis of AD."

项目概要/摘要阿尔茨海默病 (AD) 是一种新兴的全球健康威胁，预计患病率将在 2019 年翻倍 2050. AD 是一种畸形蛋白质疾病，AD 蛋白质组表征已取得重大进展与质谱仪。然而，数据缺失对解释现有的数据构成了重大障碍。 AD质谱实验。缺失是指生物样品中存在但未通过质量检测到的肽或蛋白质光谱仪由于各种技术因素。该项目将通过开发机器学习来解决缺失问题估算或估计定量质谱数据中缺失值的方法。该项目将发展两种独立的插补方法，一种使用非负矩阵分解，另一种使用深度神经网络。这些插补方法将提高质谱实验的再现性和统计能力并将在现有的蛋白质组学实验中实现新的发现。这些插补方法将适用于几乎任何类型的质谱实验 – 串联质量标签、数据相关采集、数据独立采集、光谱计数、无标记定量等。这些插补方法将以轻量级、开源且易于使用的软件包，可以合并到现有的数据处理工作流程中。我将通过重新分析几个现有 AD 蛋白质组学的数据来演示这些插补方法的实用性研究。我的插补方法将识别新的差异表达蛋白、共表达模块和 AD 这些现有数据集中的生物标志物。我还将分析未发表的数据独立采集 (DIA) 蛋白质组学数据来源于 AD 患者脑脊液样本。在这里我将重点关注识别区分的生物标志物基于遗传背景和共病状态的患者之间的差异。我还将确定患者的生物标志物无症状AD。该提案开发的插补方法将使独立 AD 研究人员未来的发现成为可能。该提案与 NIA 的战略方向一致，旨在“识别和理解遗传、分子和 AD 发病机制的细胞机制。”