Enabling scalable single-cell transcriptomic analysis through distributed computing with Apache spark.

ABSTRACT: As the field of single-cell genomics continues to develop, the generation of large-scale scRNA-seq datasets has become more prevalent. Although these datasets offer tremendous potential for shedding light on the complex biology of individual cells, the sheer volume of data presents significant challenges for management and analysis. Off late, to address these challenges, a new discipline, known as "big single-cell data science," has emerged. Within this field, a variety of computational tools have been developed to facilitate the processing and interpretation of scRNA-seq data. However, several of these tools primarily focus on the analytical aspect and tend to overlook the burgeoning data deluge generated by scRNA-seq experiments. In this study, we try to address this challenge and presen

SUBMITTER: Adil A 

PROVIDER: S-EPMC12307815 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature