Sethi, R., Ang, K. S., Li, M., Long, Y., Ling, J., & Chen, J. (2024). ezSingleCell: an integrated one-stop single-cell and spatial omics analysis platform for bench scientists. Nature Communications, 15(1). https://doi.org/10.1038/s41467-024-48188-2
Abstract:
ezSingleCell is an interactive and easy-to-use application for analysing various single-cell and spatial omics data types without requiring prior programing knowledge. It combines the best-performing publicly available methods for in-depth data analysis, integration, and interactive data visualization. ezSingleCell consists of five modules, each designed to be a comprehensive workflow for one data type or task. In addition, ezSingleCell allows crosstalk between different modules within a unified interface. Acceptable input data can be in a variety of formats while the output consists of publication ready figures and tables. In-depth manuals and video tutorials are available to guide users on the analysis workflows and parameter adjustments to suit their study aims. ezSingleCell’s streamlined interface can analyse a standard scRNA-seq dataset of 3000 cells in less than five minutes. ezSingleCell is available in two forms: an installation-free web application (https://immunesinglecell.org/ezsc/) or a software package with a shinyApp interface (https://github.com/JinmiaoChenLab/ezSingleCell2) for offline analysis.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - AI, Analytics and Informatics (AI3) Horizontal Technology Programme Office (HTPO) seed grant
Grant Reference no. : C211118015
This research / project is supported by the Singapore National Medical Research Council - Open Fund Individual Research Grant
Grant Reference no. : NMRC-OFIRG18nov-2013
This research / project is supported by the Singapore National Medical Research Council - Open Fund Large Collaborative Grant
Grant Reference no. : NMRC/OFLCG/003/2018
This research / project is supported by the National Research Foundation - Competitive Research Fund
Grant Reference no. : NRF-CRP19-2017-04