SRF

Requirements:

1. python 3.6.1

2. numpy (1.12.1)

3. scikit-learn (0.18.1)

Installation:

The source code can be directly called from python.

Usage:

python SRF.py

-h|--help: get help

-g|--genefile: the gene expression data file path

-m|--methyfile: the methylation expression data file path

-r|--mirnafile: the miRNA expression data file path

-c|--clusternum: the number of clusters

-x|--weight1: the weight of gene expression data

-y|--weight2: the weight of methylation expression data

-z|--weight3: the weight of microRNA expression data

   [NOTE: (x + y + z) = 1]

The examples of input files are available with test_net.txt, test_label.txt

Example:

python SRF.py -g Gene_Expression_Data.txt -m Methy_Expression_Data.txt -r Mirna_Expression_Data.txt -c 3 -x 0.5 -y 0.3 -z 0.2

#Input:

1. Gene_Expression_Data.txt

2. Methy_Expression_Data.txt

3. Mirna_Expression_Data.txt

#Output: Patient Subtypes Labels. The first column represents patients' ID, and the second column represents patients' label.

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Copyright and License Information

Copyright (C) 2018 Northwestern Polytechnical University, Xi’an, China.