process: The process of geting the differential expression genes

In eshinesimida/spiapcc: SPIA-PCC: Signaling pathway impact analysis incorporated the change of Pearson correla-tion coefficient between two groups

Description

This function uses based method such as limma to get the differential expression gene

Usage

process(dataset)

Arguments

dateset

The dataset of cancer or disease from NCBI, such as GSE1145

Details

the previous SPIA method and integrate the change of of genes Pearson coefficient(PCC) from two groups. We proposed a set of three pathway analysis methods based on the change of PCC. We applied these approaches to colorectal cancer, lung cancer and Alzheimer's disease datasets and so on.

Value

There are five variable values, exprs stand for gene expression profile, normal stand for the number of normal samples, tumor stand for the number of tumor samples, DE stand for the fold change of differential expression genes, ALL stand for all genes of human

Examples

#import EnrichmentBrowser, KEGGandMetacoreDzPathwaysGEO, KEGGdzPathwaysGEO and SPIA package
library(EnrichmentBrowser)
library(KEGGandMetacoreDzPathwaysGEO)
library(KEGGdzPathwaysGEO)
library(SPIA)
data("GSE1145")
result <- process(GSE1145)

eshinesimida/spiapcc documentation built on May 17, 2019, 6:11 a.m.