SMITE-package {SMITE} | R Documentation

## 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)

### Author(s)

Neil Ari Wijetunga, Andrew Damon Johnston

Maintainer: Neil.Wijetunga@med.einstein.yu.edu, Andrew.Johnston@med.einstein.yu.edu

### 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)

[Package SMITE version 1.8.0 Index]