README.Rmd

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```{r echo=FALSE, results="hide", message=FALSE}
library("badger")
```

<!-- <img src="https://raw.githubusercontent.com/dputhier/scigenex/main/inst/sticker/scigenex_logo.png" width="150"  align="right"/> -->


```{r, echo = FALSE, results='asis'}
cat(
	badge_lifecycle(stage = "stable", color="green"),
	badge_repostatus("Active"),
	badge_license("MIT"),
	badge_last_commit(ref="dputhier/scigenex"),
	badge_codecov("dputhier/scigenex")
)
```

# SciGeneX repository

## :arrow_double_down: 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
    sudo 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))

## :book: Documentation

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