Single-cell RNA sequencing, known as scRNA-seq, is currently at the forefront of biotechnological innovation. This technology is critical for studying tissue and organ development, disease pathogenesis, and clinical implementation of personalized medicine. scRNA-seq experiments, by isolating single cells and their RNAs, enable gene expression measurement at a single-cell resolution, providing the ability to characterize molecular signatures of diverse cell types, states, and structures in tissue development and disease progression.
Due to the knowledge gap in properly modeling the high-dimensional and sparse scRNA-seq data that contains experimental and statistical noises, it remains a substantive challenge to construct a comprehensive view of single-cell transcriptomes in health and disease.
Li will use the grant to address the critical need for computational tools that can accurately evaluate biological hypotheses for diverse cell populations by developing novel statistical methods for analyzing and interpreting scRNA-seq data. The lab will develop methods and software tools for quantifying and comparing gene-gene associations, for identifying and comparing the usage of poly(A) tails in post-transcriptional regulation of RNAs, and for efficient integration of scRNA-seq data.
“This grant will help us develop novel statistical models for jointly analyzing and comparing scRNA-seq data from heterogeneous biological samples, such as multiple patients, developmental stages, or related species,” says Li, who is also an associate research member in the Cancer Prevention and Control Program at Rutgers Cancer Institute. “Ultimately, we hope this work will yield efficient and broadly applicable statistical and bioinformatics tools for generating substantial insights into identifying key cells, pathways, gene interactions, and RNA transcripts associated with various biological contexts, including human disease.”
The information detailed in this press release is supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R35GM142702. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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