In cx30/INDEED: An Implementation of Integrated Differential Expression and Differential Network Analysis for Biomarker Candidate Selection

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INDEED

Overview

This package implements INDEED algorithm from Zuo et. al.'s Methods paper: INDEED: Integrated differential expression and differential network analysis of omic data for biomarker discovery (PMID: 27592383).

This R package will generate a csv file containing information such as p-values, node degree and activity score for each biomolecule. A higher activity score indicates that the corresponding biomolecule has more neighbors connceted in the differential network and their p-values are more statistically significant. It will also generate a csv file for the differential network created by INDEED.

Installation

You can install INDEED from github with:

# The development version from GitHub:
# install.packages("devtools")
devtools::install_github("cx30/INDEED")

Usage

library(INDEED)

# Example 1:
# Using partial correlation to obtain sparse differential network
select_sig(Met_GU, Met_Group_GU, Met_name_GU, partial = TRUE)

# Example 2:
# Using Spearman correlation to obtain differential network
select_sig(Met_GU, Met_Group_GU, Met_name_GU, partial = FALSE, method = "spearman")

cx30/INDEED documentation built on May 5, 2019, 2:41 a.m.