## ---- echo=FALSE--------------------------------------------------------------
knitr::opts_chunk$set(error=FALSE, message=FALSE, warning=FALSE)
library(BiocStyle)

## -----------------------------------------------------------------------------
library(scRNAseq)
sce <- GrunPancreasData()

# Quality control to remove bad cells.
library(scuttle)
qcstats <- perCellQCMetrics(sce)
qcfilter <- quickPerCellQC(qcstats, sub.fields="altexps_ERCC_percent")
sce <- sce[,!qcfilter$discard]

# Normalization by library size.
sce <- logNormCounts(sce)

# Feature selection.
library(scran)
dec <- modelGeneVar(sce)
hvgs <- getTopHVGs(dec, n=1000)

# Dimensionality reduction.
set.seed(1000)
library(scater)
sce <- runPCA(sce, ncomponents=20, subset_row=hvgs)

# Clustering.
library(bluster)
mat <- reducedDim(sce)
clust.info <- clusterRows(mat, NNGraphParam(), full=TRUE)
clusters <- clust.info$clusters
table(clusters)

## -----------------------------------------------------------------------------
sil <- approxSilhouette(mat, clusters)
sil
boxplot(split(sil$width, clusters))

## -----------------------------------------------------------------------------
pure <- neighborPurity(mat, clusters)
pure
table(Max=pure$maximum, Assigned=clusters)
boxplot(split(pure$purity, clusters))

## -----------------------------------------------------------------------------
g <- clust.info$objects$graph
ratio <- pairwiseModularity(g, clusters, as.ratio=TRUE)

library(pheatmap)
pheatmap(log10(ratio+1), cluster_cols=FALSE, cluster_rows=FALSE,
    col=rev(heat.colors(100)))

## -----------------------------------------------------------------------------
cluster.gr <- igraph::graph_from_adjacency_matrix(log2(ratio+1), 
    mode="upper", weighted=TRUE, diag=FALSE)

# Increasing the weight to increase the visibility of the lines.
set.seed(1100101)
plot(cluster.gr, edge.width=igraph::E(cluster.gr)$weight*5,
    layout=igraph::layout_with_lgl)

## -----------------------------------------------------------------------------
merged <- mergeCommunities(g, clusters, number=10)
table(merged)

## -----------------------------------------------------------------------------
hclusters <- clusterRows(mat, HclustParam(cut.dynamic=TRUE))
pairwiseRand(clusters, hclusters, mode="index")

## -----------------------------------------------------------------------------
ratio <- pairwiseRand(clusters, hclusters, mode="ratio")

library(pheatmap)
pheatmap(ratio, cluster_cols=FALSE, cluster_rows=FALSE,
    col=viridis::viridis(100), breaks=seq(-1, 1, length=101))

## -----------------------------------------------------------------------------
set.seed(1001010)
ari <-bootstrapStability(mat, clusters=clusters, 
    mode="index", BLUSPARAM=NNGraphParam())
ari

## -----------------------------------------------------------------------------
set.seed(1001010)
ratio <-bootstrapStability(mat, clusters=clusters,
    mode="ratio", BLUSPARAM=NNGraphParam())

library(pheatmap)
pheatmap(ratio, cluster_cols=FALSE, cluster_rows=FALSE,
    col=viridis::viridis(100), breaks=seq(-1, 1, length=101))

## -----------------------------------------------------------------------------
sessionInfo()