View source for Using Bioconductor To Analyse Beadarray Data

[[Category: R]]
[[Category: Bioinformatics]]

==Software Requirements==
*R, get from [[http://cran.r-project.org/ CRAN]]
*Bioconductor, get from [[http://www.bioconductor.org/download Bioconductor]]
*Bioconductor packages.  Install as needed:
**beadarray
**limma
<pre>
source("http://www.bioconductor.org/biocLite.R")
biocLite("PACKAGE")
</pre>

==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)
<pre>
data = "FinalReport_SampleProbe.txt"
controls = "ControlProbe.txt"
samplesheet = "Proj_54_12Aug09_WGGEX_SS_name.csv"
BSData = readBeadSummaryData(dataFile = data, qcFile= controls, sampleSheet=samplesheet)
</pre>
*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:
<pre>BSData.quantile = normaliseIllumina(BSData, method="quantile", transform="log2")</pre>
*To examine the effects of normalisation on the dataset use boxplots:
<pre>
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)")
</pre>
*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.
<pre>
d = dist(t(exprs(BSData.quantile)))
plot(hclust(d)
</pre>

==Differential Expression Analysis==
*Normalised data can be analysed using the limma package for statistical differences
*First define groups for each treatment.  If a samplesheet was provided correctly and had this information:
<pre>samples = pData(BSData)$Sample_Group</pre>
*Otherwise define these groups manually in the order that they were entered (check by looking at pData(BSData)
<pre>samples = c("Control", "Control", "Treatment1", "Treatment1, "Treatment2"...)</pre>
*Next the groups are used to set up a statistical design:
<pre>
library(limma)
samples = as.factor(samples)
design = model.matrix(~0 + samples)
colnames(design) = levels(samples)
fit = lmFit(exprs(BSData.quantile), design)
</pre>
*Now set up contrast matrices to define how you want the data analyses. For example you may want to compare some treatments to a control, as well as between some treaments.  See the limma user guide for more information about specific analyses.  When defining the contrast matrix use the sample group names as defined above.
<pre>
cont.matrix = makeContrasts(Treatment1vsControl = Treatment1 - Control, Treatment2vsControl = Treatment2 - Control, Treatment1vsTreatment2 = Treatment1 - Treatment2, levels = design)
fit.cont = contrasts.fit(fit, cont.matrix)
ebFit = eBayes(fit.cont)
</pre>
===Generating a Venn Diagram for Differential Expression===
*First define a cutoff criteria for inclusion.
#One option is to use the decideTests function:
<pre>results = decideTests(ebFit, method="global")</pre>
*The relevant options are for method and adjust.method
**method
***default is "global", which allows for p-value comparasons
**adjust.method, this defines the false-discovery rate adjustment:
***default is "BH" for Benjami and Hochberg
***other options are "none", "fdr" (same as BH), "holm" and "BY"
#

==Annotation of Expression Sets and Fitted Data==