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:
- 金额:$ 3.82万
- 依托单位:
- 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:
- 关键词:AddressAgingAlzheimer&aposs DiseaseAlzheimer&aposs disease patientAlzheimer’s disease biomarkerAmyloid beta-ProteinBenchmarkingBiologicalBiologyBrain regionCellsCerebrospinal FluidComputer softwareComputing MethodologiesDataData DiscoveryData SetDimensionsDiseaseFASTK GeneFundingFutureGeneticJointsKnowledgeLabelLearningLeftLinkMachine LearningMalignant NeoplasmsMass Spectrum AnalysisMeasurementMeasuresMessenger RNAMethodsMolecularMolecular and Cellular BiologyNetwork-basedNeural Network SimulationNeurodegenerative DisordersNoisePathogenesisPatientsPatternPeptidesPerformancePersonsPrevalenceProceduresProcessPrognosisProteinsProteomeProteomicsPublic HealthPublishingReproducibilityResearch PersonnelRunningSamplingSoftware ToolsTherapeutic InterventionTrainingUnited States National Institutes of HealthWorkage relatedasymptomatic Alzheimer&aposs diseasebiomarker discoverybiomarker identificationcomorbiditycomputerized data processingdeep neural networkdifferential expressionexperimental studyglobal healthhyperphosphorylated tauimprovedionizationlaser capture microdissectionlearning strategylight weightmachine learning methodmalformationmass spectrometernovelopen sourcepatient biomarkersphosphoproteomicsspecific biomarkersstatistical learningtherapeutic targetvirtual
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。”
项目成果
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Evaluating Proteomics Imputation Methods with Improved Criteria
- DOI:10.1021/acs.jproteome.3c00205
- 发表时间:2023-10-20
- 期刊:
- 影响因子:4.4
- 作者:Harris,Lincoln;Fondrie,William E.;Noble,William S.
- 通讯作者:Noble,William S.
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