SIM-package: Statistical Integration of Microarrays

Description

SIM is a statistical model to identify associations between two genomic datasets. Where one is assigned as dependent variable and the other as independent e.g. copy number measurements on several samples versus expression measurements on the same samples. A region of interest can be chosen to run the integrated analysis on either the same region for both dependent and independent datasets or different regions. For each dependent feature a P-value measures the association with the independent data, the contribution of each independent feature is given as Z-scores. The integrated analysis is based on the random-effect model for gene-sets as implemented in gt.

maybe something about annotation?

By default we use method.adjust = "BY" (Benjamini-Yekutieli) for multiple testing correction. This method accounts for dependence between measurements and is more conservative than "BH" (Benjamini-Hochberg). For details on the multiple testing correction methods see p.adjust. We have experienced that a rather low stringency cut-off on the BY-values of 20% allows the detection of associations for data with a low number of samples or a low frequency of abberations. False positives are rarely observed.

Make sure that the array probes are mapped to the same builds of the genome, and that the chrom.table used by the integrated.analysis is from the same build as well. See sim.update.chrom.table.

Arguments

Details

Package:

SIM

Type:

Package

Version:

1.7.1

Date:

2010-09-14

License:

Open

References

Menezes RX, Boetzer M, Sieswerda M, van Ommen GJ, Boer JM (2009). Integrated analysis of DNA copy number and gene expression microarray data using gene sets. BMC Bioinformatics, 10, 203-.

Goeman JJ, van de Geer SA, de Kort F, van Houwelingen HC (2004). A global test for groups of genes: testing association with a clinical outcome. Bioinformatics, 20, 93-109.

Examples

Run this code

#load the datasets and the samples to run the integrated analysis
data(expr.data)
data(acgh.data)
data(samples) 
         
#assemble the data
assemble.data(dep.data = acgh.data, 
              indep.data = expr.data,
              dep.ann = colnames(acgh.data)[1:4], 
              indep.ann = colnames(expr.data)[1:4], 
              dep.id="ID", 
              dep.chr = "CHROMOSOME",
              dep.pos = "STARTPOS",
              dep.symb="Symbol",  
              indep.id="ID",
              indep.chr = "CHROMOSOME", 
              indep.pos = "STARTPOS", 
              indep.symb="Symbol", 
              overwrite = TRUE,
              run.name = "chr8q")

#run the integrated analysis
integrated.analysis(samples = samples, 
                    input.regions ="8q", 
                    zscores=TRUE, 
                    run.name = "chr8q")

# use functions to plot the results of the integrated analysis

#plot the P-values along the genome
sim.plot.pvals.on.genome(input.regions = "8q", 
                         significance = c(0.2, 0.05), 
                         adjust.method = "BY", 
                         pdf = FALSE, 
                         run.name = "chr8q")

#plot the P-values along the regions
sim.plot.pvals.on.region(input.regions = "8q", 
						 adjust.method="BY", 
						 run.name = "chr8q")

#plot the z-scores in an association heatmap
#plot the zscores in a heatmap
sim.plot.zscore.heatmap(input.regions = "8q", 
                        method="full", 
                        significance=0.2,                        
                        z.threshold=3, 
                        show.names.indep=TRUE, 
                        show.names.dep=TRUE, 
                        adjust.method = "BY",  
                        add.plot = "smooth", 
                        smooth.lambda = 2,
                        pdf = FALSE, 
                        run.name = "chr8q")

sim.plot.zscore.heatmap(input.regions = "8q", 
                        method="full", 
                        significance = 0.05,                        
                        z.threshold = 1, 
                        show.names.indep=TRUE, 
                        show.names.dep=FALSE, 
                        adjust.method = "BY",  
                        add.plot = "heatmap", 
                        smooth.lambda = 2,
                        pdf = FALSE, 
                        run.name = "chr8q")
                        
sim.plot.zscore.heatmap(input.regions = "8q", 
                        method="full", 
                        significance = 0.05,                        
                        z.threshold = 1, 
                        show.names.indep=TRUE, 
                        show.names.dep=TRUE, 
                        adjust.method = "BY",  
                        add.plot = "none",                                                                      
                        pdf = FALSE, 
                        run.name = "chr8q")

#tabulate the P-values per region (prints to screen)
tabulate.pvals(input.regions = "8q", 
               adjust.method="BY", 
               bins=c(0.001,0.005,0.01,0.025,0.05,0.075,0.10,0.20,1.0),               
               run.name = "chr8q") 
               
table.dep <- tabulate.top.dep.features(input.regions="8q", 
		                  adjust.method="BY", 
						  method="full",
						  significance=0.05,
						  run.name="chr8q")
head(table.dep[["8q"]])

table.indep <- tabulate.top.indep.features(input.regions="8q",
		                                  adjust.method="BY",
										  method="full",
										  significance= 0.05,										  
										  z.threshold=c(-1, 1),
										  run.name="chr8q")
head(table.indep[["8q"]])	

sim.plot.overlapping.indep.dep.features(input.regions="8q", 
		                                adjust.method="BY", 
										significance=0.1, 
										z.threshold= c(-1,1),                                        
										log=TRUE,	
										summarize="consecutive",										
										pdf=FALSE, 
										method="full",
										run.name="chr8q")

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