Fast clustering pipeline for high-thoughtput single-cell data
If you use the Franken package please use the following citation:
Laura De Vargas Roditi, Andrea Jacobs, Jan H. Rueschoff, Pete Bankhead, Stephane Chevrier, Hartland W. Jackson, Thomas Hermanns, Christian D. Fankhauser, Cedric Poyet, Felix Chun, Niels J. Rupp, Alexandra Tschaebunin, Bernd Bodenmiller, Peter J. Wild. Single-Cell Proteomics Defines the Cellular Heterogeneity of Localized Prostate Cancer.
doi: https://doi.org/10.1101/2021.01.25.428046
Hardware requirements
Franken package requires only a standard computer with enough RAM to support the in-memory operations.
This package is supported for macOS. The package has been tested on the following system:
macOS: Mojave (10.14.6)
The package has been tested on the following R versions:
R 3.2, 3.4, 3.6 and 4 and depends on R (>= 3.2),
Franken can be installed use the devtools package (and should only take from 3-5 minutes):
devtools::install_github("ldvroditi/Franken")
For a demo of how to use Franken please see file TestDataRun.R (once the demo cytof data has been downloaded, this should run in a few minutes in a standard computer).
The prostate cancer data in the Franken Bioarxiv pre-print will be published upon publication but the data in the demo (and used for benchmarking in the pre-print) is from:
Single-Cell Mass Cytometry of Differential Immune and Drug Responses Across a Human Hematopoietic Continuum By Sean C. Bendall, Erin F. Simonds, Peng Qiu, El-ad D. Amir, Peter O. Krutzik, Rachel Finck, Robert V. Bruggner, Rachel Melamed, Angelica Trejo, Olga I. Ornatsky, Robert S. Balderas, Sylvia K. Plevritis, Karen Sachs, Dana Pe’er, Scott D. Tanner, Garry P. Nolan Science06 May 2011 : 687-696
Please download the demo cytof data from figshare repo and save it in a folder of your choosing (the path to your data folder will henceforth be referred in TestDataRun.R as yourdatafolder):
https://figshare.com/articles/Levine_32dim_fcs/11295719
This demo data has been pre-processed according to :
Weber, L.M. and Robinson, M.D. (2016), Comparison of clustering methods for high‐dimensional single‐cell flow and mass cytometry data. Cytometry, 89: 1084-1096. doi:10.1002/cyto.a.23030
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