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Statistical modeling of cross-sample variation and learning of latent structures in microbiome sequencing data.

Award Number: R01GM135440
ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/15/2020
PERIOD OF PERFORMANCE END DATE: 08/31/2026

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Statistical modeling of cross-sample variation and learning of latent structures in microbiome sequencing data. - 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.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $0 )
2026	2024	DUKE UNIVERSITY	2200 W MAIN ST	DURHAM	NC	27705	DURHAM	USA	Biomedical Research and Research Training	000	5	1/5/2026	NON-COMPETING CONTINUATION	$0
														Subtotal = $0

Issue Date FY: 2025 ( Subtotal = $18,289 )
2025	2024	UNIVERSITY OF CHICAGO	5801 S ELLIS AVE	CHICAGO	IL	60637	COOK	USA	Biomedical Research and Research Training	001	6	9/23/2025	CHANGE OF GRANTEE / TRAINING INSTITUTION / AWARDING INSTITUTION	$168,780
2025	2024	DUKE UNIVERSITY	2200 W MAIN ST	DURHAM	NC	27705	DURHAM	USA	Biomedical Research and Research Training	000	5	9/23/2025	NON-COMPETING CONTINUATION	-$150,491
														Subtotal = $18,289

Issue Date FY: 2024 ( Subtotal = $345,053 )
2024	2024	DUKE UNIVERSITY	2200 W MAIN ST	DURHAM	NC	27705	DURHAM	USA	Biomedical Research and Research Training	000	5	8/30/2024	NON-COMPETING CONTINUATION	$345,053
														Subtotal = $345,053

Issue Date FY: 2023 ( Subtotal = $345,681 )
2023	2023	DUKE UNIVERSITY	2200 W MAIN ST	DURHAM	NC	27705	DURHAM	USA	Biomedical Research and Research Training	000	4	7/25/2023	NON-COMPETING CONTINUATION	$345,681
														Subtotal = $345,681

Issue Date FY: 2022 ( Subtotal = $346,292 )
2022	2022	DUKE UNIVERSITY	2200 W MAIN ST STE 710	DURHAM	NC	27708	DURHAM	USA	Biomedical Research and Research Training	000	3	8/22/2022	NON-COMPETING CONTINUATION	$346,292
														Subtotal = $346,292

Issue Date FY: 2021 ( Subtotal = $346,883 )
2021	2021	DUKE UNIVERSITY	2200 W MAIN ST STE 710	DURHAM	NC	27708	DURHAM	USA	Biomedical Research and Research Training	000	2	8/12/2021	NON-COMPETING CONTINUATION	$346,883
														Subtotal = $346,883

Issue Date FY: 2020 ( Subtotal = $334,000 )
2020	2020	DUKE UNIVERSITY	2200 W MAIN ST STE 710	DURHAM	NC	27708	DURHAM	USA	Biomedical Research and Research Training	000	1	9/15/2020	NEW	$334,000
														Subtotal = $334,000

Grand Total All Awards = $1,736,198

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Statistical modeling of cross-sample variation and learning of latent structures in microbiome sequencing data.

Award Number: R01GM135440

ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 09/15/2020

PERIOD OF PERFORMANCE END DATE: 08/31/2026

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