Abstract
Single-cell RNA sequencing (scRNA-seq) has generated a rapidly expanding collection of public datasets that provide insight into development, disease, and therapy. However, researchers lack an end-to-end solution for seamlessly retrieving, preprocessing, integrating, and analyzing these data because existing tools address only isolated steps and require manual curation of accessions, metadata, and technical variability, known as batch effects. In this study, we developed Celline, a Python package that executes an entire workflow using a single-line commands per step. Celline automatically gathers raw single-cell RNA-seq data from multiple public repositories and extracts metadata using large language models. It then wraps established tools, including Scrublet for doublet removal, Seurat and Scanpy for quality control and cell-type annotation, Harmony and scVI for batch correction, and Slingshot for trajectory inference, into one-line commands, enabling seamless integrative analyses. To validate Celline-acquired data quality and the integrated framework's practical utility, we applied it to 2 mouse brain cortex datasets from embryonic days 14.5 and 18. Technical validation demonstrated that Celline successfully retrieved data, standardized metadata, and enabled standard analyses that removed low-quality cells, annotated 11 major cell types, improved integration quality (scIB score +0.22), and completed trajectory analysis. Thus, Celline transforms scattered public scRNA-seq resources into unified, analysis-ready datasets with minimal effort. Its modular design allows pipeline extension, encourages community-driven advances, and accelerates the discovery of single-cell data.