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Updated: Jul 12, 2025
Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing
Published on: October 12, 2018
Cellsnake: a user-friendly tool for single-cell RNA sequencing analysis
Sinan U Umu1, Karoline Rapp Vander-Elst2, Victoria T Karlsen2
1Department of Pathology, Institute of Clinical Medicine, University of Oslo, Oslo 0372, Norway.
Background:
Single-cell RNA sequencing (scRNA-seq) provides high-resolution transcriptome data to understand the heterogeneity of cell populations at the single-cell level. The analysis of scRNA-seq data requires the utilization of numerous computational tools. However, nonexpert users usually experience installation issues, a lack of critical functionality or batch analysis modes, and the steep learning curves of existing pipelines.
Results:
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.
Conclusion:
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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