## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>"
)

## ----eval=FALSE---------------------------------------------------------------
#  To install this package, start R (version "4.2") and enter:
#  if (!require("BiocManager", quietly = TRUE))
#      install.packages("BiocManager")
#  
#  BiocManager::install("ccImpute")

## ----setup, message=FALSE, warning=FALSE--------------------------------------
library(scRNAseq)
library(scater)
library(ccImpute)
library(SingleCellExperiment)
library(stats)
library(mclust)

## ----message=FALSE, warning=FALSE---------------------------------------------
sce <- DarmanisBrainData()
sce <- logNormCounts(sce)

## -----------------------------------------------------------------------------
# Set seed for reproducibility purposes.
set.seed(0) 
# Compute PCA reduction of the dataset
reducedDims(sce) <- list(PCA=prcomp(t(logcounts(sce)))$x)

# Get an actual number of cell types
k <- length(unique(colData(sce)$cell.type))

# Cluster the PCA reduced dataset and store the assignments
set.seed(0) 
assgmts <- kmeans(reducedDim(sce, "PCA"), centers = k, iter.max = 1e+09,
    nstart = 1000)$cluster

# Use ARI to compare the k-means assignments to label assignments
adjustedRandIndex(assgmts, colData(sce)$cell.type)

## -----------------------------------------------------------------------------
assay(sce, "imputed") <- ccImpute(logcounts(sce), k = k)

## -----------------------------------------------------------------------------
# Recompute PCA reduction of the dataset
reducedDim(sce, "PCA_imputed") <- prcomp(t(assay(sce, "imputed")))$x

# Cluster the PCA reduced dataset and store the assignments
assgmts <- kmeans(reducedDim(sce, "PCA_imputed"), centers = k, iter.max = 1e+09,
    nstart = 1000)$cluster

# Use ARI to compare the k-means assignments to label assignments
adjustedRandIndex(assgmts, colData(sce)$cell.type)

## ----reproduce3, echo=FALSE-------------------------------------------------------------------------------------------
## Session info
library("sessioninfo")
options(width = 120)
session_info()