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

Latest revision as of 15:34, 2 September 2009

Software Requirements

• R, get from CRAN
• Bioconductor, get from Bioconductor
• Bioconductor packages. Install as needed:
  • beadarray
  • limma
  • annotation data for the array (normally illuminaMousev2BeadID.db)
• To install bioconductor packages use:

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)

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:

samples = pData(BSData)$Sample_Group

• Otherwise define these groups manually in the order that they were entered, check by looking at pData(BSData)

samples = c("Control", "Control", "Treatment1", "Treatment1, "Treatment2"...)

• Next the groups are used to set up a statistical design:

library(limma)
samples = as.factor(samples)
design = model.matrix(~0 + samples)
colnames(design) = levels(samples)
fit = lmFit(exprs(BSData.quantile), design)

• 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.

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)

Generating a Venn Diagram for Differential Expression

• First define a cutoff criteria for inclusion. One option is to use the decideTests function:

results = decideTests(ebFit)

• 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"
• Now use that classification to generate the Venn Diagram. The following will include both up and downregulated genes and color the numbers accordingly:

vennDiagram(results, include="both", col=c("red","green")

Annotation of Expression Sets and Fitted Data

• To see all possible annotation criteria use:

library(illuminaMousev2BeadID.db)
illuminaMousev2BeadID()

• Normally you want to annotate with at least the gene symbol and gene name. Add other criteria as required

ids = rownames(exprs(BSData))
GeneName = mget(ids, illuminaMousev2BeadIDGENENAME, ifnotfound = NA)
symbol = mget(ids, illuminaMousev2BeadIDSYMBOL, ifnotfound = NA)
anno = cbind(GeneSymbol = as.character(symbol), GeneName = as.character(GeneName))

• To add this annotation to the data analysis file:

ebFit$genes = anno
write.fit = (ebFit, file = "Filename.csv", adjust="BH")

• This example includes a false discovery rate ("BH") adjusted p.value.
• This function writes a tab-delimited text file containing for each gene (1) the average log-intensity, (2) the log-ratios, (3) moderated t-statistics, (4) t-statistic P-values, (5) F-statistic if available, (6) F-statistic P-values if available, (7) classification if available.
• To add this annotation data to the expression set:

data = exprs(BSData.quantile)
data = cbind(anno,data)
write.csv = (data, file = "Filename.csv")

• Remember that the expression set is Log2 adjusted, so to look at absolute expression levels use 2^value.