Using Bioconductor To Analyse Beadarray Data - Revision history

Davebrid at 15:34, 2 September 2009

2009-09-02T15:34:08Z

2009-08-21T18:17:52Z

Software Requirements

← Older revision		Revision as of 18:17, 21 August 2009
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	==Software Requirements==		==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:		*Bioconductor packages. Install as needed:
	**beadarray		**beadarray

2009-08-21T18:06:57Z

Software Requirements

← Older revision		Revision as of 18:06, 21 August 2009
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	**limma		**limma
	**annotation data for the array (normally illuminaMousev2BeadID.db)		**annotation data for the array (normally illuminaMousev2BeadID.db)
			*To install bioconductor packages use:
	<pre>		<pre>
	source("http://www.bioconductor.org/biocLite.R")		source("http://www.bioconductor.org/biocLite.R")

2009-08-21T18:06:22Z

Annotation of Expression Sets and Fitted Data

← Older revision		Revision as of 18:06, 21 August 2009
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	data = cbind(anno,data)		data = cbind(anno,data)
	write.csv = (data, file = "Filename.csv")		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.		*Remember that the expression set is Log2 adjusted, so to look at absolute expression levels use 2^value.

2009-08-21T18:06:01Z

Differential Expression Analysis

← Older revision		Revision as of 18:06, 21 August 2009
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	*First define groups for each treatment. If a samplesheet was provided correctly and had this information:		*First define groups for each treatment. If a samplesheet was provided correctly and had this information:
	<pre>samples = pData(BSData)$Sample_Group</pre>		<pre>samples = pData(BSData)$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>		<pre>samples = c("Control", "Control", "Treatment1", "Treatment1, "Treatment2"...)</pre>
	*Next the groups are used to set up a statistical design:		*Next the groups are used to set up a statistical design:

2009-08-21T18:05:34Z

← Older revision		Revision as of 18:05, 21 August 2009
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	data = cbind(anno,data)		data = cbind(anno,data)
	write.csv = (data, file = "Filename.csv")		write.csv = (data, file = "Filename.csv")
			*Remember that the expression set is Log2 adjusted, so to look at absolute expression levels use 2^value.

2009-08-21T18:04:42Z

← Older revision		Revision as of 18:04, 21 August 2009
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	anno = cbind(GeneSymbol = as.character(symbol), GeneName = as.character(GeneName))		anno = cbind(GeneSymbol = as.character(symbol), GeneName = as.character(GeneName))
	</pre>		</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")

2009-08-21T18:00:23Z

@@ Line 8: / Line 8: @@
 **beadarray
 **limma
 <pre>
 source("http://www.bioconductor.org/biocLite.R")
@@ Line 76: / Line 77: @@
 ==Annotation of Expression Sets and Fitted Data==

2009-08-21T17:56:35Z

Generating a Venn Diagram for Differential Expression

← Older revision		Revision as of 17:56, 21 August 2009
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	===Generating a Venn Diagram for Differential Expression===		===Generating a Venn Diagram for Differential Expression===
	*First define a cutoff criteria for inclusion. One option is to use the decideTests function:		*First define a cutoff criteria for inclusion. One option is to use the decideTests function:
	<pre>results = decideTests(ebFit~~, method="global"~~)</pre>		<pre>results = decideTests(ebFit)</pre>
	*The relevant options are for method and adjust.method		*The relevant options are for method and adjust.method
	**method		**method

2009-08-21T17:56:08Z

Generating a Venn Diagram for Differential Expression

← Older revision		Revision as of 17:56, 21 August 2009
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	</pre>		</pre>
	===Generating a Venn Diagram for Differential Expression===		===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, method="global")</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
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	***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==		==Annotation of Expression Sets and Fitted Data==