SMITE (version 1.0.2)

SMITE-package: Significance-based Modules Integrating the Transcriptome and Epigenome

Description

SMITE provides a method of scoring and visualizing multi-level epigenomic data in order to prioritize genes within a genome-wide experiment. These scores can then be used to identify subnetworks within an interaction network called modules. Each module represents a collection of highly interacting genes that are implicated by the experiment.

Details

Package:

SMITE

Type:

Package

Version:

1.0.0

Date:

2015-07-06

License:

GPL (>=2)

See Also

FEM BioNet

Examples

## NOTE: commented out for example.  See vignette for better explanation ##

options(stringsAsFactors=FALSE)

data(methylationdata)
methylation <- methylation[-which(is.na(methylation[, 5])), ]
methylation[,5] <- replace(methylation[,5],methylation[,5] == 0, 
    min(subset(methylation[,5], methylation[,5]!=0), na.rm=TRUE))

data(curated_expressiondata)

data(hg19_genes_bed)
data(histone_h3k4me1)

#test_annotation<-makePvalueAnnotation( data=hg19_genes, 
#other_data=list(h3k4me1=h3k4me1), gene_name_col=5, other_tss_distance=5000)

##fill in expression data
#test_annotation<-annotateExpression(test_annotation, expression_curated)


##fill in methylation data

#test_annotation<-annotateModification(test_annotation, methylation, 
#weight_by=c(promoter="distance", body="distance", h3k4me1="distance"),
#verbose=TRUE, mod_corr=TRUE)

##create a pvalue object that will count the effect of the h3k4me1 as
##bidirectional

#test_annotation<-makePvalueObject(test_annotation, 
#effect_directions=c(methylation_promoter="decrease",
#methylation_body="decrease",
#methylation_h3k4me1="bidirectional"))

##normalize the pvalues compared to colExp

#test_annotation<-normalizePval(test_annotation,ref="expression_pvalue",
#method="rescale")
    
##score with all four features contributing

#test_annotation<-SMITEscorePval(test_annotation,
#weights=c(methylation_promoter=.3,methylation_body=.1,expression=.3,
#methylation_h3k4me1=.3))

##load REACTOME 
#load(system.file("data","Reactome.Symbol.Igraph.rda", package="SMITE"))
 
##run Spinglass using REACTOME network

#test_annotation<-runSpinglass(test_annotation, REACTOME, maxsize=50, 
#num_iterations=10)

##run goseq on individual modules to determine bias 
#test_annotation <- runGOseq(test_annotation,
#coverage=read.table(system.file("extdata", 
#"hg19_symbol_hpaii.sites.inbodyand2kbupstream.bed.gz", package="SMITE")),
#type="kegg")

##search go seq output for keywords
#searchGOseq(test_annotation, "Cell")

##Draw a network
#plotModule(test_annotation, which_network=6, layout="fr")

##sample final file ##
data(test_annotation_score_data)