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).
The scigenex library is currently not available in CRAN or Bioc. To install from github, use:
devtools::install_github("dputhier/scigenex")
library(scigenex)
Download the tar.gz from github or clone the main branch. Uncompress and run the following command from within the uncompressed scigenex folder:
R CMD INSTALL .
Then load the library from within R.
library(scigenex)
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.
One also can install MCL from source using the following code.
# Download the latest version of mcl
wget http://micans.org/mcl/src/mcl-latest.tar.gz
# Uncompress and install mcl
tar xvfz mcl-latest.tar.gz
cd mcl-xx-xxx
./configure
make
sudo make install
# You should get mcl in your path
mcl -h
Finally you may install MCL using conda. Importantly, the mcl command should be available in your PATH from within R.
conda install -c bioconda mcl
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/.
