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

Award Number: R15HG012087
ORGANIZATION: NATIONAL HUMAN GENOME RESEARCH INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/22/2021
PERIOD OF PERFORMANCE END DATE: 08/31/2024

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


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: 2025 ( Subtotal = $0 )
2025	2021	NEW JERSEY INSTITUTE OF TECHNOLOGY	323 DR MARTIN LUTHER KING JR BLVD	NEWARK	NJ	07102	ESSEX	USA	Human Genome Research	000	1	2/13/2025	NEW	$0
														Subtotal = $0

Issue Date FY: 2024 ( Subtotal = $450,034 )
2024	2024	NEW JERSEY INSTITUTE OF TECHNOLOGY	323 DR MARTIN LUTHER KING JR BLVD	NEWARK	NJ	07102	ESSEX	USA	Human Genome Research	000	2	8/26/2024	COMPETING CONTINUATION	$450,034
														Subtotal = $450,034

Issue Date FY: 2021 ( Subtotal = $450,849 )
2021	2021	NEW JERSEY INSTITUTE OF TECHNOLOGY	323 DR MARTIN LUTHER KING JR BLVD	NEWARK	NJ	07102	ESSEX	USA	Human Genome Research	000	1	9/22/2021	NEW	$450,849
														Subtotal = $450,849

Grand Total All Awards = $900,883

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

Award Number: R15HG012087

ORGANIZATION: NATIONAL HUMAN GENOME RESEARCH INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

PERIOD OF PERFORMANCE START DATE: 09/22/2021

PERIOD OF PERFORMANCE END DATE: 08/31/2024

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