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[[Category: R]]
[[Category: Bioinformatics]]
[[Category: Bioconductor]]
==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
**annotation data for the array (normally illuminaMousev2BeadID.db)
*To install bioconductor packages use:
<pre>
source("http://www.bioconductor.org/biocLite.R")
*First define groups for each treatment. If a samplesheet was provided correctly and had this information:
<pre>samples = pData(BSData)$Sample_Group</pre>
*Otherwise define these groups manually in the order that they were entered (, check by looking at pData(BSData)
<pre>samples = c("Control", "Control", "Treatment1", "Treatment1, "Treatment2"...)</pre>
*Next the groups are used to set up a statistical design:
</pre>
===Generating a Venn Diagram for Differential Expression===
*First define a cutoff criteria for inclusion.** One option is to use the decideTests function:<pre>results = decideTests(ebFit, method="global")</pre>**The relevant options are for method and adjust.method***method****default is "global", which allows for p-value comparasons***adjust.method, this defines the false-discovery rate adjustment:****default is "BH" for Benjami and Hochberg****other options are "none", "fdr" (same as BH), "holm" and "BY"*Now use that classification to generate the Venn Diagram. The following will include both up and downregulated genes and color the numbers accordingly:<pre>vennDiagram(results, include="both", col=c("red","green")</pre>
==Annotation of Expression Sets and Fitted Data==
*To see all possible annotation criteria use:
<pre>
library(illuminaMousev2BeadID.db)
illuminaMousev2BeadID()
</pre>
*Normally you want to annotate with at least the gene symbol and gene name. Add other criteria as required
<pre>
ids = rownames(exprs(BSData))
GeneName = mget(ids, illuminaMousev2BeadIDGENENAME, ifnotfound = NA)
symbol = mget(ids, illuminaMousev2BeadIDSYMBOL, ifnotfound = NA)
anno = cbind(GeneSymbol = as.character(symbol), GeneName = as.character(GeneName))
</pre>
*To add this annotation to the data analysis file:
<pre>
ebFit$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.