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[[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:
**lumi
**limma
**annotation data for the array (normally lumiMouseAll.db)
*To install bioconductor packages use:
<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)</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"
*Now use that classification to generate the Venn Diagram. The following will include both up and downregulated genes and color the numbers accordingly:
<pre>vennDiagram(results, include="both", col=c("red","green")</pre>
==Annotation of Expression Sets and Fitted Data==
*To see all possible annotation criteria use:
<pre>
library(illuminaMousev2BeadID.db)
illuminaMousev2BeadID()
</pre>
*Normally you want to annotate with at least the gene symbol and gene name. Add other criteria as required
<pre>
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))
</pre>
*To add this annotation to the data analysis file:
<pre>
ebFit$genes = anno
write.fit = (ebFit, file = "Filename.csv", adjust="BH")
</pre>
*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:
<pre>
data = exprs(BSData.quantile)
data = cbind(anno,data)
write.csv = (data, file = "Filename.csv")
</pre>
*Remember that the expression set is Log2 adjusted, so to look at absolute expression levels use 2^value.
[[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:
**lumi
**limma
**annotation data for the array (normally lumiMouseAll.db)
*To install bioconductor packages use:
<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)</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"
*Now use that classification to generate the Venn Diagram. The following will include both up and downregulated genes and color the numbers accordingly:
<pre>vennDiagram(results, include="both", col=c("red","green")</pre>
==Annotation of Expression Sets and Fitted Data==
*To see all possible annotation criteria use:
<pre>
library(illuminaMousev2BeadID.db)
illuminaMousev2BeadID()
</pre>
*Normally you want to annotate with at least the gene symbol and gene name. Add other criteria as required
<pre>
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))
</pre>
*To add this annotation to the data analysis file:
<pre>
ebFit$genes = anno
write.fit = (ebFit, file = "Filename.csv", adjust="BH")
</pre>
*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:
<pre>
data = exprs(BSData.quantile)
data = cbind(anno,data)
write.csv = (data, file = "Filename.csv")
</pre>
*Remember that the expression set is Log2 adjusted, so to look at absolute expression levels use 2^value.
