Significance-based Modules Integrating the Transcriptome and Epigenome

Share:

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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
## 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)

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.