mlDNA-package: Machine Learning-based Differential Network Analysis

In mlDNA: Machine Learning-based Differential Network Analysis of Transcriptome Data

Description

This package provides functions for performing the machine learning (ML)-based differential network analysis of transcriptome data. It can be used to:

1) perform machine learning-based gene filtering with positive sample-only learning algorithm for identifying a set of candidate genes with four different classes of expression characteristics, including the absoulte expression values, the within-condition expression variations, the between-condition expression changes, and the coefficient of variation;

2) construct gene co-expression networks from gene expression data with seven optional methods, including five correlation and two non-correlation measures;

3) perform a comprehensive network comparision with more than thirty network-based characteristics including degree, closeness, eccentricity, eigenvector, and PageRank;

4) identify biologically important genes with different ML algorithms by combining network-based characteristics generated from differential network analysis;

5) estimate the covergence degree between different experimential conditions;

7) quantify the activity of pathways;

8) detect condition specifcally expressed genes.

The tutorial of the mlDNA package can be found at: http://www.cmbb.arizona.edu/mlDNA.

Details

Package:	mlDNA
Type:	Package
Version:	1.1
Date:	2013-11-18
License:	GPL(>=2)

Author(s)

Chuang Ma, Xiangfeng Wang

Maintainer: Chuang Ma <[e-mail]>

References

[1] Chuang Ma, Xiangfeng Wang. Machine learning-based differential network analysis: a study of stress-responsive transcriptomes in Arabidopsis thaliana. 2013(Submitted).

mlDNA documentation built on May 2, 2019, 2:15 p.m.