SciGeneX repository

⏬ Installation

System requirements

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).

Step 1 - Installation of SciGeneX

From R

The scigenex library is currently not available in CRAN or Bioc. To install from github, use:

devtools::install_github("dputhier/scigenex")
library(scigenex)

From the terminal

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)

Step 2 - Installation of MCL

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.

Installation of MCL using install_mcl()

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.

Installation of MCL from source

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
make install
# You should get mcl in your path
mcl -h

Installation of MCL from sources

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

Example

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

Documentation (in progress) is available at https://dputhier.github.io/scigenex/.