SIngle-cell Network Using Mutual information (SINUM)

This repository contains the source code for single-cell networks (SCNs) construction by utilizing mutual information inference of gene-gene association. The example datasets are stored inside example folder, as well the example outputs.

Basic usage

sinum.py

$ python code/sinum.py -f example/dataset/ChuType_exampleGEM_log2.txt -b 0.2 -z 0.0 -o ./example -n ChuType_example

Argument	Variable	Description	Default value
-f	GEM_file	path to gene expression matrix (GEM) file	example/dataset/ChuType_exampleGEM_log2.txt
-b	bs	box size	0.2
-z	cf	z-score threshold	0.0
-o	outdir	output directory	./example
-n	savename	save name	ChuType_example

There are two outputs: adjacency matrices & degree matrix.

The adjacency matrices (gene x gene) for each cell are in sparse matrix format(.npz).

The degree matrix (gene x cell) is in .csv format.

The outputs are stored in the outdir directory, within which two folders output_adj_network & output_degree_matrix are automatically created.

Advanced usage example

Direct calling of sinum_funcs in your code.

import pandas as pd
import sinum_funcs
# read the GEM as SINUM input
# GEM_file is the path to the gene expression matrix txt file
GEM = pd.read_csv(GEM_file,sep='\t',index_col=0)
# define boxsize parameter, default = 0.2
bs = 0.2
# define z-score cutoff as the determination of edges, default = 0.0
cf = 0.0
# give the path to save the result
outdir = "./output"
# give the name of the output file ex: dataset name
savename = "ChuType"

# execute the main SINUM algorithm
sinum_funcs.sinumnet(GEM, outdir, savename, boxsize = bs, cutoff=cf)

Dependencies

The code is written in python3, and not guaranteed to work in python2, espeacially for the package versions. Additionally, the following packages (and the suggested version) must also be installed:

• NumPy (1.20.1)
• Pandas (1.4.2)
• SciPy (1.8.1)
• scikit-learn (1.1.1)