scClust

This package is to perform clustering on single-cell RNA-sequencing data with different similarity metrics on k-means and SIMLR clustering methods.

Requirements

Before the installation, some of R packages must be installed. These include:

• parallel
• amap
• caret
• mclust
• clusteval
• dendextend
• igraph
• foreach
• doParallel
• doSNOW
• ggplot2
• Hmisc

Installation

devtools::install_github("taiyunkim/scClustBench", build_vignettes = TRUE)
library(scClust)

Building the vignette may take some time. If you wish not to create the vignette during installation, try:

devtools::install_github("taiyunkim/scClustBench")
library(scClust)

NOTE: For mac users, the official cran mirror of R tools for OS X and R tools for OS X on r.research.att.com that lists the gfortran binary are out of date. You will need to update gfortran and add the following line FLIBS=-L/usr/local/Cellar/gcc/X.Y.Z/lib/gcc/X (where X.Y.Z is your gcc version) to ~/.R/Makevars prior to this package installation.

Current version of this package is implemented to run SIMLR (Wang et al, 2017) or k-means clustering methods with various similarity metrics.

Available metrics include:

    SIMLR - "pearson" correlation, "spearman" correlation and "euclidean" distances.

    K-means - "pearson" correlation, "spearman" correlation, "euclidean" distance, "manhattan" distance and "maximum" distance.

Using scClust

For further demonstration, see:

browseVignettes("scClust")

Load Data

data(GSE82187.sample)
mat <- GSE82187

mat <- log2(mat+1)

# set number of clusters (classes defined in colnames)
nCs <- length(table(colnames(mat))

Section 1. Clustering with different similarity metrics with scClust

To run scClust,

# SIMLR
simlr.result <- scClust(mat, nCs, similarity = "pearson", method = "simlr", seed = 1, cores.ratio = 0)

# K-means
kmeans.result <- scClust(mat, nCs, similarity = "pearson", method = "kmeans", seed = 1, nstart = 10, iter.max = 10)

This function allows you to perform clustering with a user specified similarity metrics. The return values of scClust are identical to clustering methods for Kmeans and SIMLR functions.

Section 2. Benchmarking different similarity metrics with scClustBench

This section is to compare a set of similarity metrics on clustering methods to benchmark their perfomance accuracy.

To run scClustBench,

# SIMLR
simlr.result <- scClustBench(mat, nCs, method = "simlr", rep = 2, seed = 1, cores = 1, cores.ratio = 0)

# K-means
kmeans.result <- scClustBench(mat, nCs, method = "kmeans", rep = 2, seed = 1, cores = 1, nstart = 10, iter.max = 10)

You can evaluate this result with the function evalScClustBench and plot with plotSimlrEval or plotKmeansEval.