Nothing
## ---- eval=FALSE---------------------------------------------------------
# # Install devtools package
# utils::install.packages('devtools')
# # Install scISR from GitHub
# devtools::install_github('duct317/scISR')
## ---- eval=FALSE---------------------------------------------------------
# #Load required library
# library(scISR)
#
# # Load example data (Goolam dataset with reduced number of genes), other dataset can be download from our server at http://scisr.tinnguyen-lab.com/
# data('Goolam')
# # Raw data
# raw <- Goolam$data
# # Cell types information
# label <- Goolam$label
## ---- eval=FALSE---------------------------------------------------------
# # Generating subtyping result
# set.seed(1)
# imputed <- scISR(data = raw, ncores = 4)
## ---- eval=FALSE---------------------------------------------------------
# library(irlba)
# library(mclust)
# # Perform PCA and k-means clustering on raw data
# set.seed(1)
# # Filter genes that have only zeros from raw data
# raw_filer <- raw[rowSums(raw != 0) > 0, ]
# pca_raw <- irlba::prcomp_irlba(t(raw_filer), n = 50)$x
# cluster_raw <- kmeans(pca_raw, length(unique(label)),
# nstart = 2000, iter.max = 2000)$cluster
# print(paste('ARI of clusters using raw data:', round(adjustedRandIndex(cluster_raw, label),3)))
#
# # Perform PCA and k-means clustering on imputed data
# set.seed(1)
# pca_imputed <- irlba::prcomp_irlba(t(imputed), n = 50)$x
# cluster_imputed <- kmeans(pca_imputed, length(unique(label)),
# nstart = 2000, iter.max = 2000)$cluster
# print(paste('ARI of clusters using imputed data:', round(adjustedRandIndex(cluster_imputed, label),3)))
## ---- eval=FALSE---------------------------------------------------------
# sessionInfo()
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