Name Mode Size
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MbkmeansParam.Rd 100644 1 kb
blocksize.Rd 100644 1 kb
clusterRows.Rd 100644 0 kb
compute_wcss.Rd 100644 1 kb
mbkmeans.Rd 100644 5 kb
mini_batch.Rd 100644 3 kb
predict_mini_batch.Rd 100644 1 kb
predict_mini_batch_r.Rd 100644 1 kb
README.md
# mbkmeans: Mini-batch k-means clustering for single-cell RNA-seq [![Project Status: Active - The project has reached a stable, usable state and is being actively developed.](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active) [![BioC release](http://www.bioconductor.org/shields/build/release/bioc/mbkmeans.svg)](https://bioconductor.org/checkResults/release/bioc-LATEST/mbkmeans) [![BioC devel](http://www.bioconductor.org/shields/build/release/bioc/mbkmeans.svg)](https://bioconductor.org/checkResults/devel/bioc-LATEST/mbkmeans) [![R-CMD-check](https://github.com/drisso/mbkmeans/workflows/R-CMD-check-bioc/badge.svg)](https://github.com/drisso/mbkmeans/actions) This package implements the mini-batch k-means algorithm for large datasets, including support for on-disk data representation. The method is described in details in the paper: [S. Hicks, R. Liu, Y. Ni, E. Purdom, D. Risso (2021). mbkmeans: Fast clustering for single cell data using mini-batch k-means. PLOS Computational Biology.](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008625) ## Installation In virtually all cases, installing from Bioconductor is recommended. ```{r} if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("mbkmeans") ``` In the rare event you need the development version from GitHub, use the following. ```{r} library(devtools) BiocManager::install("drisso/mbkmeans") ```