In Merguerrero/BasicP: Function to support a microarray data analysis pipeline

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title: "Basic examples of using BasicP functions" author: "Alex Sanchez and Mercedes Guerrero" date: "r Sys.Date()" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Basic examples of using BasicP functions} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8}

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require(devtools)
install("/home/mguerrero/Dropbox/Practiques-Grau_Biotec-UVic_Mercedes_Guerrero/BasicP")
require(affy)
require(oligo) #ExonStudy
require("limma")

The goal of this Vignette is to illustrate the use of some functions exported from the package.

readOrLoad.rawData'Vignette Info

This function reads data from cel files or loads previously saved data.

readCELS <- TRUE 
my.targets <- "./celfiles/targets.txt"
targets<- read.table("./celfiles/targets.txt", head=TRUE, sep="\t", row.names = 1)
my.fileNames <-paste("./celfiles/",rownames(targets),sep="")
rawDataFileName <- "rawData.Rda"
my.outputDir <- "."
isExonStudy <- FALSE
orgPackage <- "org.Hs.eg" # Necesita este parametro para funciones internas
rawData <- BasicP::readOrLoad.RawData(readCELS = readCELS, phenoDat = my.targets,
                      fileNames = my.fileNames, dataFName =rawDataFileName,
                      outputDir = my.outputDir, exonSt = isExonStudy)

createOrLoadAnnotations'Vignette Info

This function creates annotation files or loads previously saved data.

loadAnnotations <- FALSE
chipPackAvailable <- TRUE
platformDesignPackAvailable <- FALSE
chipPackage <- "hgu133a2"
platformDesignPackage <- NULL
outputDir <- "./ResultsDir"
annotationsFileName <- "Annotations"
entrezTableFileName <-"Entrezs.Rda"
symbolsTableFileName <-"Symbols.Rda"
controlsTableFileName <- "controls.Rda"

anotacions <- BasicP::createOrLoadAnnotations (loadAnnotations= loadAnnotations, chipPackAvailable = chipPackAvailable, platformDesignPackAvailable = platformDesignPackAvailable,chipPackage = chipPackage, platformDesignPackage = platformDesignPackage, outputDir = outputDir,annotationsFileName = annotationsFileName,entrezTableFileName = entrezTableFileName, symbolsTableFileName = symbolsTableFileName, controlsTableFileName = controlsTableFileName)

normalization'Vignette Info

function to normalize the raw data.

load("rawData.Rda")
rawData <- my.raw
normMethod <- "RMA"
my.targets <- read.AnnotatedDataFrame("./celfiles/targets.txt", header = TRUE, row.names = 1)
celFilesDir <-"./celfiles"
loadFile <- FALSE 
normalized.eset.FileName <-  "normalizedData.Rda"   
outputDir <- "./ResultsDir"
exonStudy <- FALSE

eset_norm <- BasicP::normalization(my.data = rawData, method = normMethod, targetsinfo = my.targets, inputDir = celFilesDir, loadFile = loadFile , normalizedFName = normalized.eset.FileName, outputDir = outputDir, exonSt = exonStudy)

normplots2File'Vignette Info

Function that makes a report of the normalization

load("./ResultsDir/normalizedData.Rda")
repes <- duplicated(exprs(my.norm), MARGIN=1)
exprs(my.norm) <- exprs(my.norm)[!repes,]
eset_norm <- my.norm
my.colors <- rainbow(length(sampleNames(eset_norm)))
my.names <- pData(eset_norm)$ShortName
myCex<- 0.8
dim3 <- FALSE
fileName <- "NormalizedPlots.pdf"
outputDir <- "./ResultsDir"
PCAPlots <- TRUE
csv <- "csv2"

BasicP::normplots2File(my.data = eset_norm, sampleNames = my.names, my.colors = my.colors, my.groups = pData(eset_norm)$Group, my.method = "average",my.cex = myCex ,posText = 2, dim3 = FALSE,fileName = fileName, outputDir = outputDir,PCAPlots = TRUE, csv = fileType)

filterData'Vignette Info

Function to filter the data

load("./ResultsDir/normalizedData.Rda")
repes <- duplicated(exprs(my.norm), MARGIN=1)
exprs(my.norm) <- exprs(my.norm)[!repes,]
eset_norm <- my.norm
load("./ResultsDir/controls.Rda")
removeNAs <- TRUE
load("./ResultsDir/Entrezs.Rda")
entrezs <- entrezTable
SignalFilter <- TRUE
signalThreshold <- 50
signalFilter.Function <- "filt.by.Signal"
signalThreshold.as.percentage <- TRUE
VarFilter <- TRUE 
variabilityThreshold <- 50
variability.Function <- "sdf"
variabilityThreshold.as.percentage <- TRUE
pairing.Function <- NULL
my.targets <-read.AnnotatedDataFrame("./celfiles/targets.txt", header = TRUE, row.names = 1)
doReport <- TRUE
outputDir <- "./ResultsDir"
FilteringReportFileName <- "FilteringReport.txt"

exprs.filtered <- BasicP::filterData(expres = exprs(eset_norm),controls = names(controlsTable),removeNAs = TRUE, entrezs = entrezs ,bySignal = SignalFilter,signalThr = signalThreshold, grups = pData(eset_norm)$grupo, sigFun.Name = signalFilter.Function, sigThr.as.perc = signalThreshold.as.percentage, byVar = VarFilter, variabilityThr = variabilityThreshold, varFun.Name = variability.Function,
varThr.as.perc = variabilityThreshold.as.percentage, pairingFun.Name = pairing.Function, targets = my.targets, doReport = doReport, outputDir = outputDir, filteringReportFName = FilteringReportFileName)

saveData'Vignette Info

Function to save R objects in files.

load("./ResultsDir/normalizedData.Rda")
repes <- duplicated(exprs(my.norm), MARGIN=1)
exprs(my.norm) <- exprs(my.norm)[!repes,]
eset_norm <- my.norm
normalized.all.FileName <- "normalized.all"
fileType <-"csv2"
symbolsTable <- load("./ResultsDir/Symbols.Rda")
expres.all.FileName <- "expres.Rda"
linksFileName <- "Links.txt"
outputDir <- "./ResultsDir"

BasicP::saveData(expres = exprs(eset_norm), expres.csv.FileName = normalized.all.FileName, csvType=fileType, description = "Normalized values for all genes", anotPackage = NULL, symbolsVector = symbolsTable, SYMBOL = "SYMBOL", expres.bin.FileName = expres.all.FileName, linksFile = linksFileName, outputDir = outputDir)

lmAnalysis'Vignette Info

Setting design and contrasts matrix to proceed with the linear model analysis.

targets <- read.table(file ="./celfiles/targets.txt" , header = TRUE, sep = "\t")
column2design<- 5
lev <- targets[,column2design] 
design <- model.matrix( ~ 0 + lev)        
colnames(design) <- levels(lev)
rownames(design) <- targets$ShortName
numParameters <- ncol(design)
print(design)

cont.matrix<-makeContrasts(AvsB= (A -B), AvsL= (A-L), BvsL=(B-L),levels=design)
print(cont.matrix)

Linear model analysis.Function included in dolmAnalysis.

load("./ResultsDir/exprs.filtered.Rda")
contrasts2test <- 1:ncol(cont.matrix)
anotPackage = NULL
comparison =  "Estudi"
outputDir = "./ResultsDir"
ENTREZIDs = "entrezTable"
SYMBOLIDs = "symbolsTable"
linksFile = "Links.txt"
fitFileName = "fit.Rda"
csvType= "csv"
rows2HTML= NULL
anotFileName <- "Annotations"
runMulticore = 0 
toTIFF= FALSE

fitMain <- 
  lmAnalysis(exprs.filtered = exprs.filtered, design = design, cont.matrix = cont.matrix, contrasts2test = contrasts2test, anotPackage = anotPackage, outputDir = outputDir, comparison = comparison, Expressions_And_Top = TRUE , showParams = FALSE , use.dupCorr = FALSE, block = NULL, nDups = 1 , ENTREZIDs = ENTREZIDs, SYMBOLIDs = SYMBOLIDs, linksFile = linksFile,fitFileName = fitFileName , csvType=csvType, rows2HTML = NULL, anotFileName = anotFileName)

#Function to make a list for make the dolmAnalysis.
add2parsList <- function(oneList,object)
{
  pos <- length(oneList) + 1
  oneList[[pos]] <- object
  names(oneList)[pos] <- oneList[[pos]]$comparisonName
  return(oneList)
}

doLmAnalysis'Vignette Info

Function to analyze the linerar model with the different parameters.

lmParsList <- list()
Estudi <- list(dades = NULL,
               expresFileName = "exprs.filtered.Rda",
               targets = targets,
               designMat = design,
               contMat = cont.matrix,
               whichContrasts = 1:ncol(cont.matrix),
               anotPack = NULL,
               outputDir = outputDir,
               ExpressionsAndTop = TRUE,
               showLmParams = FALSE, 
               use.dupCorr = FALSE,
               block = NULL,
               nDups = 1,
               comparisonName = comparison,  
               ENTREZIDs = "entrezTable",
               SYMBOLIDs = "symbolsTable",
               fileOfLinks = linksFile,
               fitFileName = fitFileName,
               csvType=csvType,
               rows2HTML = NULL,
               anotFilename = anotFileName
               )

lmParsList <- add2parsList(lmParsList, Estudi)

for(ix in 1:length(lmParsList))
{
  fit.Main   <- doLmAnalysis(lmParsList[ix])
}

Merguerrero/BasicP documentation built on May 20, 2019, 2:24 p.m.