CITATION.bib

@article {Lun2022.03.02.482701,
	author = {Lun, Aaron and Kancherla, Jayaram},
	title = {Single-cell data analysis in the browser},
	elocation-id = {2022.03.02.482701},
	year = {2022},
	doi = {10.1101/2022.03.02.482701},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {We present kana, a web application for interactive single-cell RNA-seq (scRNA-seq) data analysis in the browser. Like, literally, in the browser: kana leverages web technologies such as WebAssembly to efficiently perform the relevant computations on the user{\textquoteright}s machine, avoiding the need to provision and maintain a backend service. The application provides a streamlined one-click workflow for all steps in a typical scRNA-seq analysis, starting from a count matrix and finishing with marker detection. Results are presented in an intuitive web interface for further exploration and iterative analysis. Testing on public datasets shows that kana can analyze over 100,000 cells within 5 minutes on a typical laptop.Competing Interest StatementThe authors have declared no competing interest.},
	URL = {https://www.biorxiv.org/content/early/2022/03/04/2022.03.02.482701},
	eprint = {https://www.biorxiv.org/content/early/2022/03/04/2022.03.02.482701.full.pdf},
	journal = {bioRxiv}
}