Single-cell RNA sequencing data processing using cloud-based serverless computing

Single-cell RNA sequencing (scRNA-seq) has become a routine method for measuring cell activities. We present a novel and generalizable methodology using serverless cloud computing to accelerate computationally intensive workflows. We create an on-demand "supercomputer" using rapidly deployable cloud serverless functions as automatically provisioned computation units. We tested our methodology of optimizing an scRNA-seq workflow by leveraging serverless functions on the cloud using two publicly available peripheral blood mononuclear cell (PBMC) datasets. In addition, we demonstrate our approach using a 450 GB human scRNA-seq knockout dataset, comprising 13 samples from different developmental time points, designed to study the temporal impact of perturbations on pancreatic differentiation. We compared the execution time of the scRNA-seq serverless workflow with an optimized workflow without serverless functions running on identical hardware, and demonstrate speedups for all tested datasets, reaching 7.0-fold for the largest dataset. Our software is open source and distributed under the MIT license. Code and documentation are publicly available at https://github.com/BioDepot/scRNA-serverless.