SMITE-package {SMITE} | R Documentation |
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.
Package: | SMITE |
Type: | Package |
Version: | 1.0.0 |
Date: | 2015-07-06 |
License: | GPL (>=2) |
Neil Ari Wijetunga, Andrew Damon Johnston
Maintainer: Neil.Wijetunga@med.einstein.yu.edu, Andrew.Johnston@med.einstein.yu.edu
FEM BioNet
## 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)