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

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title: "SPIA-PCC: Signaling pathway impact analysis incorporated the change of Pearson correlation coefficient between two groups" author: "Xianbin Li" date: 'r Sys.Date()' output: pdf_document: default html_document: default word_document: default vignette: | %\VignetteIndexEntry{narray Usage Examples} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8}

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library(knitr)
opts_chunk$set(
  cache = FALSE,
  echo = TRUE,
  warning = FALSE,
  error = FALSE,
  message = FALSE
)

spiapcc pathway signatures

This R package provides function that uses the previous SPIA method and integrate the change 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.

Scoring the KEGGandMetacoreDzPathwaysGEO package data for pathway analysis

This is to outline how to prepare expression data, in this case from the KEGGandMetacoreDzPathwaysGEO package for pathway analysis using spiap.

Preparing the gene expression matrix

library(EnrichmentBrowser)
library(KEGGandMetacoreDzPathwaysGEO)
library(KEGGdzPathwaysGEO)
library(SPIA)

# load the dateset
data("GSE1145")
# get the gene expression matrix
exprs_all <- exprs(GSE1145)
# get the gene symbol of gene expression matrix
all.eset <- probe.2.gene.eset(GSE1145)
head(featureNames(all.eset))


# Normalization of gene expression profile
before.norm <- exprs(all.eset)
all.eset <- normalize(all.eset, norm.method="quantile")
after.norm <- exprs(all.eset)

# Change matrix to dataframe style
exprs_all1 <- data.frame(after.norm)

# plot of normalization
par(mfrow=c(1,2))
boxplot(before.norm)
boxplot(after.norm)

Obtaining case and control samples

table(pData(all.eset)$Group)
pData(all.eset)$GROUP <- ifelse(pData(all.eset)$Group == "d", 1, 0)
normal <- length(which(pData(all.eset)$GROUP == '0'))
tumor <- length(which(pData(all.eset)$GROUP == '1'))

Get differential expression genes

# get differential expression genes
all.eset <- de.ana(all.eset)
head(fData(all.eset), n=4)
all_de <- fData(all.eset)

#The plot of differential expression genes 
par(mfrow=c(1,2))
pdistr(fData(all.eset)$ADJ.PVAL)
volcano(fData(all.eset)$FC, fData(all.eset)$ADJ.PVAL)

Get database of signaling pathways from KEGG

# get pathway dataset
kegg.gs <- get.kegg.genesets("hsa")

Get the results of SPIA method and spiap method, we use colorectal cancer dataset from GSE8671

library(spiapcc)

# get differential expression genes on threshold 0.1
tg <- all_de[all_de$ADJ.PVAL < 0.1,]
# get fold change pf differential expression genes
DE_colorectal = tg$FC
names(DE_colorectal)<-as.vector(rownames(tg))
# get all gene names
ALL_colorectal = rownames(all_de)
#The result of spia method
res_spia = spia(de = DE_colorectal, all=ALL_colorectal, organism="hsa",nB=2000,plots=FALSE,beta=NULL,combine="fisher",verbose=TRUE)
res_spia <- res_spia[,-12]
head(res_spia)
gse_madat2 <- exprs_all1

# The results of spia_nt method
res_nt = spiapcc(de=DE_colorectal, all=ALL_colorectal,normal = normal,tumor = tumor,  gse_madat2 = gse_madat2,organism="hsa",nB=2000,plots=FALSE,
                   beta=NULL,combine="fisher",verbose=T, flag = 1)
res_nt <- res_nt[,-12]
head(res_nt)

#The results of spia_tn method
res_tn = spiapcc( de=DE_colorectal, all=ALL_colorectal,normal = normal,tumor = tumor,  gse_madat2 = gse_madat2,organism="hsa",nB=2000,plots=FALSE,
                   beta=NULL,combine="fisher",verbose=T, flag = -1)
res_tn <- res_tn[,-12]
head(res_tn)

#The results of spia_abs method
res_abs = spiapcc( de=DE_colorectal, all=ALL_colorectal,normal = normal,tumor = tumor,  gse_madat2 = gse_madat2,organism="hsa",nB=2000,plots=FALSE,
                   beta=NULL,combine="fisher",verbose=T, flag = 0)
res_abs <- res_abs[,-12]
head(res_abs)

# 
data("GSE3467")
result <- process(GSE3467)
exprs_nrom <- result$exprs
normal <- result$normal
tumor <- result$tumor
DE <- result$DE
ALL <- result$ALL

res = spiapcc( de=DE, all=ALL,normal = normal,tumor = tumor,  gse_madat2 = exprs_nrom, organism="hsa",nB=2000,plots=FALSE,
                   beta=NULL,combine="fisher",verbose=T, flag = 0)
res <- res[,-12]
head(res)

R version information

sessionInfo()

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