Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.05.03.539204v1?rss=1
Authors: Umu, S. U., Rapp Vander-Elst, K., Karlsen, V. T., Chouliara, M., Baekkevold, E. S., Jahnsen, F. L., Domanska, D.
Abstract:
Single-cell RNA sequencing (scRNA-seq) provides high-resolution transcriptome data to understand the heterogeneity of cell populations at the single-cell level. Analysis of scRNA-seq data requires utilization of numerous computational tools. However, non-expert users usually experience installation issues and lack of critical functionality or batch analysis modes, and the steep learning curves of existing pipelines. We have developed cellsnake, a comprehensive, reproducible and accessible single-cell data analysis workflow to overcome these problems. Cellsnake offers advanced features for standard users and facilitates downstream analyses in both R and Python environments. It is also designed for easy integration into existing workflows, allowing for rapid analyses of multiple samples. As an open-source tool, cellsnake is accessible through Bioconda, PyPi, Docker, and GitHub, making it a cost-effective and user-friendly option for researchers. By using cellsnake, researchers can streamline the analysis of scRNA-seq data and gain insights into the complex biology of single cells.
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