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

> **NIH NIH R15** · NEW JERSEY INSTITUTE OF TECHNOLOGY · 2021 · $450,849

## Abstract

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.

## Key facts

- **NIH application ID:** 10291567
- **Project number:** 1R15HG012087-01
- **Recipient organization:** NEW JERSEY INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Zhi Wei
- **Activity code:** R15 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $450,849
- **Award type:** 1
- **Project period:** 2021-09-22 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10291567

## Citation

> US National Institutes of Health, RePORTER application 10291567, Deep Learning Methods to Integrate Biological Information for Analysis of Single-cell RNAseq Data (1R15HG012087-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10291567. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*