Statistical modeling of cross-sample variation and learning of latent structures in microbiome sequencing data
Statistical modeling of cross-sample variation and learning of latent structures in microbiome sequencing data
批准号:
10688000
负责人:
Li Ma
金额:
$34.57万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2020
资助国家:
美国
项目状态:
未结题
起止时间:
2020-09-15 至 2025-08-31
关键词:
Acute DiseaseAgingAlgorithmsBig DataCancer PatientCharacteristicsChronic DiseaseCommunitiesComplexComputer softwareDataData SetDevelopmentDiseaseEffectivenessEquilibriumExperimental DesignsGeneral PopulationGoalsHealthHematopoietic Stem Cell TransplantationHeterogeneityHuman bodyImmune responseInflammatory Bowel DiseasesInterventionLeadLearningLinkLongitudinal StudiesMalignant NeoplasmsMental DepressionMethodologyMethodsMicrobeModelingModernizationNon-Insulin-Dependent Diabetes MellitusObesityOutcomePatientsPhylogenetic AnalysisPlayProcessResearchRoleSamplingStatistical Data InterpretationStatistical ModelsStructureSurveysTestingTimeUrinary tract infectionVariantWomanbacterial communitydata sharingdesignflexibilityhigh dimensionalityhuman microbiotaimprovedmicrobialmicrobial communitymicrobiomemicrobiome analysismicrobiome compositionmicrobiome researchmicrobiome sequencingmicrobiotaopen sourcepersonalized interventionsoftware developmenttooluser-friendly
中文摘要
项目摘要
驻留在人体上的细菌群落(微生物群)与各种急性和慢性炎症有关。
慢性疾病和病症,如肥胖症、炎症性肠病、2型糖尿病、抑郁症,
和尿路感染(UTIs),以及宿主对各种治疗和健康的反应
这些疾病和条件的干预措施。由于微生物群所起的关键作用已经成为
越来越多的人认识到,微生物组测序数据集现在是在越来越多的
复杂的实验设计和调查策略。虽然这些数据具有许多共同特征,
现代大数据的挑战,如高维性和稀疏性,它们也具有特征
微生物群特有的,包括(i)细菌物种之间的显性和潜在的上下文关系,
例如它们的进化和功能关系;以及(ii)样本之间的实质性异质性,
样品间变异的复杂结构。现代微生物组研究的有效分析需要
新的统计方法,将这些重要特征纳入数据生成机制。
该项目的目标是开发一套统计模型、方法、算法和软件,
这一迫切需要。最初的目标是开发一个多尺度概率框架来建模微生物组
组合物,有效地表征高维,稀疏,和大量的交叉样本
微生物组测序数据的变化,并结合了各种常见的实验设计,如
协变量、批次效应和多个时间点,同时在灵活性、分析简约性和
计算易处理性另一个重点是开发微生物组组成的潜在变量模型
用于识别潜在结构的数据,如样本集群和物种亚群落。最终
目标是制作用户友好的开源软件,实现所有提出的方法,
微生物组测序数据的分析。所有开发的模型和方法都是由两个-
PI Ma和他的团队的合作项目。一个是关于微生物群落的鉴定
另一项研究是关于老年妇女的微生物组的纵向变化,
接受造血干细胞移植的癌症患者。这些研究将作为所有
发展开发的模型、方法和软件不仅可以更好地预测健康状况,
这些和其他微生物组研究的结果,也有助于破译微生物组在各种
疾病和生物医学过程,最终目标是对微生物组进行个性化干预
患者的组合物,以改善健康状况。
英文摘要
PROJECT ABSTRACT
The bacterial communities (microbiota) residing on the human body have been linked to a variety of acute and
chronic diseases and conditions, such as obesity, inflammatory bowel disorders, Type 2 diabetes, depression,
and urinary tract infections (UTIs), as well as to the host’s response to a variety of treatments and health
interventions for these diseases and conditions. As the critical role played by the microbiota has become
increasingly recognized, microbiome sequencing data sets are now routinely generated under ever more
sophisticated experimental designs and survey strategies. While such data share many common features and
challenges of modern big data, such as high-dimensionality and sparsity, they also possess characteristics
peculiar to the microbiota, including (i) the explicit and latent contextual relationships among the bacterial species,
such as their evolutionary and functional relationships; and (ii) the substantial heterogeneity across samples and
complex structure in the sample-to-sample variation. Effective analysis of modern microbiome studies calls for
new statistical methodology that incorporates these important characteristics in the data generative mechanism.
This project’s objective is to develop a suite of statistical models, methods, algorithms, and software that meet
this urgent need. An initial aim is to develop a multi-scale probabilistic framework for modeling microbiome
compositions that effectively characterizes the high dimensionality, sparsity, and substantial cross-sample
variation in microbiome sequencing data, and incorporates a variety of common experimental designs, such as
covariates, batch effects, and multiple time points, while striking a balance in flexibility, analytical parsimony, and
computational tractability. An additional focus is to develop latent variable models for microbiome compositional
data for the purpose of identifying latent structures such as sample clusters and species subcommunities. A final
aim is to produce user-friendly, open-source software that implements all of the proposed methods for the
analysis of microbiome sequencing data. All of the models and methods developed are informed by two on-
going collaborative projects of PI Ma and his team. One is on the identification of microbial communities
associated with UTIs in aging women, and the other on the study of longitudinal changes in the microbiome of
cancer patients undergoing hematopoietic stem cell transplantation. These studies will serve as testbeds for all
development. The models, methods, and software developed will not only result in better prediction of the health
outcomes in these and other microbiome studies but also help decipher the roles of microbiome in various
diseases and biomedical processes, with the ultimate goal of personalized interventions on the microbiome
compositions of patients to lead to improved health.
期刊论文(4)
专著(0)
科研奖励(0)
会议论文
DOI:
10.1186/s12866-023-02799-9
发表时间:
2023-03-07
期刊:
BMC MICROBIOLOGY
影响因子:
4.2
作者:
[Ji, Zhicheng, Ma, Li]
通讯作者:
Ma, Li
Targeting the LIFR-LCN2 pathway to improve liver cancer therapy
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依托单位:
Statistical modeling of cross-sample variation and learning of latent structures in microbiome sequencing data
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Non-coding RNA functions in tumor metastasis
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Regulation of metastasis and epithelial-mesenchymal transition by microRNAs
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Role of microRNAs in malignant progression
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海外基金