Difference between revisions of "Using Bioconductor To Analyse Beadarray Data"

Revision as of 17:30, 21 August 2009

Software Requirements

• R, get from [CRAN]
• Bioconductor, get from [Bioconductor]
• Bioconductor packages. Install as needed:
  • beadarray
  • limma

source("http://www.bioconductor.org/biocLite.R")
biocLite("PACKAGE")

Loading Data

• At a minimum you need the Probe Profile data (normally a txt file).
• For all R procedures first change directory to your working directory then next create a new script, and save all executed lines in that script file.
• Load the beadarray library, indictate dataFile (required), sampleSheet (normally a xls or csv file) and control set (Control Probe, normally a txt file)

data = "FinalReport_SampleProbe.txt"
controls = "ControlProbe.txt"
samplesheet = "Proj_54_12Aug09_WGGEX_SS_name.csv"
BSData = readBeadSummaryData(dataFile = data, qcFile= controls, sampleSheet=samplesheet)

• You may need to alter either the ProbeID or ControlID to fit the illuminaprobe column from the sampleprobe or controlprobe datasets.
• This fits the data into the BSData dataframe. Phenotype data can be accessed by pData(BSData) and expression data can be accessed by exprs(BSData).

Data Normalisation

• Microarray data is typically quantile normalised and log2 transformed:

BSData.quantile = normaliseIllumina(BSData, method="quantile", transform="log2")

• To examine the effects of normalisation on the dataset use boxplots:

boxplot(as.data.frame(log2(exprs(BSData))),las=2,outline=FALSE, ylab="Intensity (Log2 Scale)")
boxplot(as.data.frame(exprs(BSData.quantile)),las=2,outline=FALSE, ylab="Intensity (Log2 Scale)")

• Save these boxplots as postscript files.

Clustering Analysis

• This analysis will generate a euclidean distance matrix then a cluster analysis of that matrix and will show the distribution between replicates. Ideally similar treatments will cluster together.

d = dist(t(exprs(BSData.quantile)))
plot(hclust(d)