The partitioning steps are currently performed using a system call to the Markov Cluster (MCL) algorithm that presently limits the use of DBF-MCL to unix-like platforms. Importantly, the mcl
command should be in your PATH and reachable from within R (see dedicated section).
You may skip this step as the latest versions of SciGeneX will call scigenex::install_mcl()
to install MCL in ~/.scigenex
directory if this program is not found in the PATH.
The install_mcl()
has been developed to ease MCL installation. This function should be call automatically from within R when calling the gene_clustering()
function. If install_mcl()
does not detect MCL in the PATH it will install it in ~/.scigenex
.
The scigenex library contains several datasets including the pbmc3k_medium which is a subset from pbmc3k 10X dataset.
library(Seurat)
library(scigenex)
set_verbosity(1)
# Load a dataset
load_example_dataset("7871581/files/pbmc3k_medium")
# Select informative genes
res <- select_genes(pbmc3k_medium,
distance = "pearson",
row_sum=5)
# Cluster informative features
## Construct and partition the graph
res <- gene_clustering(res,
inflation = 1.5,
threads = 4)
# Display the heatmap of gene clusters
res <- top_genes(res)
plot_heatmap(res, cell_clusters = Seurat::Idents(pbmc3k_medium))
Documentation (in progress) is available at https://dputhier.github.io/scigenex/.