Flexible clustering for Bioconductor
In bluster: Clustering Algorithms for Bioconductor

knitr::opts_chunk$set(error=FALSE, message=FALSE, warnings=FALSE)
library(BiocStyle)

Introduction

The r Biocpkg("bluster") package provides a flexible and extensible framework for clustering in Bioconductor packages/workflows. At its core is the clusterRows() generic that controls dispatch to different clustering algorithms. We will demonstrate on some single-cell RNA sequencing data from the r Biocpkg("scRNAseq") package; our aim is to cluster cells into cell populations based on their PC coordinates.

library(scRNAseq)
sce <- ZeiselBrainData()

# Trusting the authors' quality control, and going straight to normalization.
library(scuttle)
sce <- logNormCounts(sce)

# Feature selection based on highly variable genes.
library(scran)
dec <- modelGeneVar(sce)
hvgs <- getTopHVGs(dec, n=1000)

# Dimensionality reduction for work (PCA) and pleasure (t-SNE).
set.seed(1000)
library(scater)
sce <- runPCA(sce, ncomponents=20, subset_row=hvgs)
sce <- runUMAP(sce, dimred="PCA")

mat <- reducedDim(sce, "PCA")
dim(mat)

Hierarchical clustering

Our first algorithm is good old hierarchical clustering, as implemented using hclust() from the stats package. This automatically sets the cut height to half the dendrogram height.

library(bluster)
hclust.out <- clusterRows(mat, HclustParam())
plotUMAP(sce, colour_by=I(hclust.out))

Advanced users can achieve greater control of the procedure by passing more parameters to the HclustParam() constructor. Here, we use Ward's criterion for the agglomeration with a dynamic tree cut from the r CRANpkg("dynamicTreeCut") package.

hp2 <- HclustParam(method="ward.D2", cut.dynamic=TRUE)
hp2
hclust.out <- clusterRows(mat, hp2)
plotUMAP(sce, colour_by=I(hclust.out))

$k$-means clustering

Our next algorithm is $k$-means clustering, as implemented using the kmeans() function. This requires us to pass in the number of clusters, either as a number:

set.seed(100)
kmeans.out <- clusterRows(mat, KmeansParam(10))
plotUMAP(sce, colour_by=I(kmeans.out))

Or as a function of the number of observations, which is useful for vector quantization purposes:

kp <- KmeansParam(sqrt)
kp
set.seed(100)
kmeans.out <- clusterRows(mat, kp)
plotUMAP(sce, colour_by=I(kmeans.out))

Graph-based clustering

We can build shared or direct nearest neighbor graphs and perform community detection with r CRANpkg("igraph"). Here, the number of neighbors k controls the resolution of the clusters.

set.seed(101) # just in case there are ties.
graph.out <- clusterRows(mat, NNGraphParam(k=10))
plotUMAP(sce, colour_by=I(graph.out))

It is again straightforward to tune the procedure by passing more arguments such as the community detection algorithm to use.

set.seed(101) # just in case there are ties.
np <- NNGraphParam(k=20, cluster.fun="louvain")
np
graph.out <- clusterRows(mat, np)
plotUMAP(sce, colour_by=I(graph.out))

Two-phase clustering

We also provide a wrapper for a hybrid "two-step" approach for handling larger datasets. Here, a fast agglomeration is performed with $k$-means to compact the data, followed by a slower graph-based clustering step to obtain interpretable meta-clusters. (This dataset is not, by and large, big enough for this approach to work particularly well.)

set.seed(100) # for the k-means
two.out <- clusterRows(mat, TwoStepParam())
plotUMAP(sce, colour_by=I(two.out))

Each step is itself parametrized by BlusterParam objects, so it is possible to tune them individually:

twop <- TwoStepParam(second=NNGraphParam(k=5))
twop
set.seed(100) # for the k-means
two.out <- clusterRows(mat, TwoStepParam())
plotUMAP(sce, colour_by=I(two.out))

Obtaining full clustering statistics

Sometimes the vector of cluster assignments is not enough. We can obtain more information about the clustering procedure by setting full=TRUE in clusterRows(). For example, we could obtain the actual graph generated by NNGraphParam():

nn.out <- clusterRows(mat, NNGraphParam(), full=TRUE)
nn.out$objects$graph

The assignment vector is still reported in the clusters entry:

table(nn.out$clusters)

Further comments

clusterRows() enables users or developers to easily switch between clustering algorithms by changing a single argument. Indeed, by passing the BlusterParam object across functions, we can ensure that the same algorithm is used through a workflow. It is also helpful for package functions as it provides diverse functionality without compromising a clean function signature. However, the true power of this framework lies in its extensibility. Anyone can write a clusterRows() method for a new clustering algorithm with an associated BlusterParam subclass, and that procedure is immediately compatible with any workflow or function that was already using clusterRows().