Contact Principal Investigator / Project Leader: Yang, Mary
Organization: University of Arkansas Little Rock
Title:
DEVELOP NOVEL DEEP LEARNING AND COMBINATORIAL OPTIMIZATION METHODS TO
IDENTIFY KEY DISEASE REGULATORY ELEMENTS FOR SINGLE-CELL DATA
Abstract Text:
Description:
Traditional bulk sequencing measures the average of cell population constituents, inevitably
masking the intrinsic cell-to-cell heterogeneity. Single-cell technologies, on the other hand,
enable a high-resolution measurement for each individual cell, providing new opportunities to
capture cell population diversity and dissect the heterogeneity of complex diseases. Meanwhile,
the high-sparsity and the relatively small number of sequencing reads pose new data analytic
challenges. In this proposed project, we will develop innovative computational methods for
single-cell RNA sequencing (scRNA-seq) data analysis and integration to identify key regulatory
elements that underlie disease heterogeneity and drive disease development. The scRNA-seq
data contains substantial proportion of zero expression counts due to low capture efficiency and
stochastic gene expression. We will develop a novel data-driven deep learning model to recover
the missing values. Our model utilizes a deep learning algorithm to capture complex and latent
distributions of missing values without assuming an underlying distribution, thus, ensuring
effective performance across various scRNA-seq generated by different protocols. scRNA-seq
profiles enable characterization of unique transcriptome for each cell type. We hypothesize that
disrupted expression patterns accompanying the disease development in different cell types are
controlled by sequential alterations of the activity and connectivity in the regulatory networks.
Hence, using scRNA-seq data, we will first infer cell lineage trajectories. Then, we will develop
a novel deep neural network method to reconstruct cellular regulatory networks according to
pseudo-time ordering of the cell types. With a new network alignment model, we will exploit the
dynamic changes of regulations in the disease process, revealing key regulators and providing
cell type-specific drug targets. The fulfillment of the proposed project will facilitate single-cell
genomic and biomedical research efforts allowing for a much broader, cross-disciplinary
understanding of the underlying mechanisms of complex diseases. The proposed project will be
devised into capstone projects and will be primarily completed by undergraduate students under
the PI's supervision with the assistance of a graduate student. The project will serve as a
vehicle to equip undergraduate students with essential research skills and interdisciplinary
knowledge, and to stimulate the students' ambition to pursue careers in the biomedical science.
This project will create a multidisciplinary platform in a comprehensive university setting that
encourages undergraduate students to engage in biomedical research.!
