Using Bioconductor To Analyse Beadarray Data: Difference between revisions

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 [[Category: R]]
 [[Category: Bioinformatics]]
+[[Category: Bioconductor]]
 ==Software Requirements==
-*R, get from [[http://cran.r-project.org/ CRAN]]
+*R, get from [http://cran.r-project.org/ CRAN]
-*Bioconductor, get from [[http://www.bioconductor.org/download Bioconductor]]
+*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")
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 *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)
+*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:
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 </pre>
 ===Generating a Venn Diagram for Differential Expression===
-*First define a cutoff criteria for inclusion.
+*First define a cutoff criteria for inclusion.  One option is to use the decideTests function:
-**One option is to use the decideTests function:
+<pre>results = decideTests(ebFit)</pre>
-</pre>results = decideTests(ebFit, method="global")</pre>
+*The relevant options are for method and adjust.method
-**The relevant options are for method and adjust.method
+**method
-***method
+***default is "global", which allows for p-value comparasons
-****default is "global", which allows for p-value comparasons
+**adjust.method, this defines the false-discovery rate adjustment:
-***adjust.method, this defines the false-discovery rate adjustment:
+***default is "BH" for Benjami and Hochberg
-****default is "BH" for Benjami and Hochberg
+***other options are "none", "fdr" (same as BH), "holm" and "BY"
-****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.