A scalable integrated multi-modal single cell analysis framework for gene regulatory and cell-cell interaction networks

Advances in single cell technologies for high-throughput measurement of biological molecules is revolutionizing the study of biology and medicine. While computational tools for various tasks arising in single cell analysis have been developed, they often lack the ability to handle hundreds of datasets and/or millions of cells due to both computer memory and run-time constraints. This project addresses the compelling need for the development of computational methods and software for large scale data analysis, arising due to the rapid accumulation of single cell datasets in public repositories, and the need for analyzing them to fully unravel the complexity of biological systems. The project will lead to novel methods for integrated analysis of multiple types of single cell data, using such data for building biological networks and understanding inter-cellular communication, and development of multiple software products for broader adoption. The project team will include students who will gain valuable experience in interdisciplinary research. Use of software tools developed under the project will be taught at training workshops under the Atlanta Single Cell Omics and Analytics Initiative (https://ascomai.org/), which serves the single cell research communities of Georgia Tech, Emory University, and the Morehouse School of Medicine. The project will include significant involvement of underrepresented individuals. The project will lead to the development of scalable, memory-efficient algorithms and high-performance parallel implementations for large-scale single cell analysis leveraging the inherent parallelism in a multi-socket, multi-core server/workstation that is now commonplace. The problems targeted include 1) single- and multi-modal integration, 2) clustering of single cell RNA-sequencing and single cell ATAC-sequencing data from disparate sources, 3) construction of gene regulatory networks from large-scale integrated multi-modal single cell data, and 4) joint inference of intra-cell gene regulatory networks and cell-cell interaction networks. The research will be validated using several case studies, simulated and real-world benchmark data, and known gold standard benchmarks. The research products will be made available as standalone software tools that will be able to run seamlessly from laptops to workstations to high-end shared memory servers, efficiently exploiting all available resources to push the scale of datasets that can be analyzed. Results of the project will be made available at https://faculty.cc.gatech.edu/~saluru/single-cell and software products will be released as open source on Github at https://github.com/AluruLab. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.