Function to compute a bioDistclass object from profile data and a mapping. For details of the process see the user's guide, but briefly the process involves using the mapping to identify reference features appropriate to each surrogate feature (if any), aggregating the surrogate data into pseudo-data for each reference feature, and then calculating the correlation distance between the reference features according to the surrogate data.
subset of features to be considered for the computation of the distances. If NULL then the features are first gathered from the features in referenceData. If referenceData is not provided then the list of features are gathered from mapping (bioMap class) and using the reference.
reference
A character indicating the variable that is being used as features to compute distance between
mapping
The mapping between feature types
referenceData
ExpressionSet object with the data from the reference features.
surrogateData
ExpressionSet object with the data from the surrogate features.
filtering
A filtering for the bioMap class. To be implemented.
noMappingDist
Distance value to be used when a reference feature do not map to any surrogate feature. If "max", maximum indirect distance among the rest of reference features is taken. If NA, distance weights are re-scaled so this surrogate association is not considered. If a number then the missing values are replaces with that value.
distance
Distance between features to be computed. Possible values are "pearson", "kendall", "spearman", "euclidean", "maximum", "manhattan", "canberra", "binary" and "minkowski". Default is "spearman".
aggregation
Action to perform when a reference feature maps to more than one surrogate feature. Options are "max", "sum", "mean" or "median" and the the values are aggregated according to the chosen statistic.
maxitems
The maximum number of surrogate features per reference feature to be used, selected according to "selectionRule" parameter. Default is 2.
selectionRule
Rule to select the surrogate features to be used (the number is determined by "maxitems"). It can be one of the following: (1) "maxcor" those presenting maximum correlation with corresponding main feature; in this case "referenceData" must be provided and the columns must overlap in at least 3 samples; (2) "maxmean": average across samples is computed and those features with higher mean are selected; case (3) is simmilar to (2) but considering other statistics: "maxmedian", "maxdiff", "maxFC", "sd" , "ee".
expfac
Not in use yet.
name
Character that describes the nature of the bioDist class computed
...
extra arguments passed to dist, eg "p=value" for the power used if calculating minkowski distance
Value
An object of class bioDistclass containing distances between the features in surrogateData.
Author(s)
David Gomez-Cabrero
Examples
data(STATegRa_S1)
data(STATegRa_S2)
require(Biobase)
# Truncate data for brevity
Block1 <- Block1[1:100,]
Block2 <- Block2[1:100,]
## Create ExpressionSets
mRNA.ds <- createOmicsExpressionSet(Data=Block1,pData=ed,pDataDescr=c("classname"))
miRNA.ds <- createOmicsExpressionSet(Data=Block2,pData=ed,pDataDescr=c("classname"))
## Create the bioMap
map.gene.miRNA<-bioMap(name = "Symbol-miRNA",
metadata = list(type_v1="Gene",type_v2="miRNA",
source_database="targetscan.Hs.eg.db",
data_extraction="July2014"),
map=mapdata)
# Create Gene-gene distance computed through miRNA data
bioDistmiRNA<-bioDist(referenceFeatures = rownames(Block1),
reference = "Var1",
mapping = map.gene.miRNA,
surrogateData = miRNA.ds, ### miRNA data
referenceData = mRNA.ds, ### mRNA data
maxitems=2,
selectionRule="sd",
expfac=NULL,
aggregation = "sum",
distance = "spearman",
noMappingDist = 0,
filtering = NULL,
name = "mRNAbymiRNA")
# Create Gene-gene distance through mRNA data
bioDistmRNA<-new("bioDistclass",
name = "mRNAbymRNA",
distance = cor(t(exprs(mRNA.ds)),method="spearman"),
map.name = "id",
map.metadata = list(),
params = list())
###### Generation of the list of Surrogated distances.
bioDistList<-list(bioDistmRNA,bioDistmiRNA)
sample.weights<-matrix(0,4,2)
sample.weights[,1]<-c(0,0.33,0.67,1)
sample.weights[,2]<-c(1,0.67,0.33,0)
###### Generation of the list of bioDistWclass objects.
bioDistWList<-bioDistW(referenceFeatures = rownames(Block1),
bioDistList = bioDistList,
weights=sample.weights)
###### Plot of distances.
bioDistWPlot(referenceFeatures = rownames(Block1) ,
listDistW = bioDistWList,
method.cor="spearman")
###### Computing the matrix of features/distances associated.
fm<-bioDistFeature(Feature = rownames(Block1)[1] ,
listDistW = bioDistWList,
threshold.cor=0.7)
bioDistFeaturePlot(data=fm)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(STATegRa)
Warning message:
replacing previous import 'Biobase::combine' by 'gridExtra::combine' when loading 'STATegRa'
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/STATegRa/bioDist.Rd_%03d_medium.png", width=480, height=480)
> ### Name: bioDist
> ### Title: bioDist
> ### Aliases: bioDist
> ### bioDist,character,character,bioMap,ExpressionSet,ExpressionSet-method
>
> ### ** Examples
>
> data(STATegRa_S1)
> data(STATegRa_S2)
> require(Biobase)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
rbind, rownames, sapply, setdiff, sort, table, tapply, union,
unique, unsplit
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
>
> # Truncate data for brevity
> Block1 <- Block1[1:100,]
> Block2 <- Block2[1:100,]
>
> ## Create ExpressionSets
> mRNA.ds <- createOmicsExpressionSet(Data=Block1,pData=ed,pDataDescr=c("classname"))
> miRNA.ds <- createOmicsExpressionSet(Data=Block2,pData=ed,pDataDescr=c("classname"))
>
> ## Create the bioMap
> map.gene.miRNA<-bioMap(name = "Symbol-miRNA",
+ metadata = list(type_v1="Gene",type_v2="miRNA",
+ source_database="targetscan.Hs.eg.db",
+ data_extraction="July2014"),
+ map=mapdata)
>
> # Create Gene-gene distance computed through miRNA data
> bioDistmiRNA<-bioDist(referenceFeatures = rownames(Block1),
+ reference = "Var1",
+ mapping = map.gene.miRNA,
+ surrogateData = miRNA.ds, ### miRNA data
+ referenceData = mRNA.ds, ### mRNA data
+ maxitems=2,
+ selectionRule="sd",
+ expfac=NULL,
+ aggregation = "sum",
+ distance = "spearman",
+ noMappingDist = 0,
+ filtering = NULL,
+ name = "mRNAbymiRNA")
>
> # Create Gene-gene distance through mRNA data
> bioDistmRNA<-new("bioDistclass",
+ name = "mRNAbymRNA",
+ distance = cor(t(exprs(mRNA.ds)),method="spearman"),
+ map.name = "id",
+ map.metadata = list(),
+ params = list())
>
> ###### Generation of the list of Surrogated distances.
>
> bioDistList<-list(bioDistmRNA,bioDistmiRNA)
> sample.weights<-matrix(0,4,2)
> sample.weights[,1]<-c(0,0.33,0.67,1)
> sample.weights[,2]<-c(1,0.67,0.33,0)
>
> ###### Generation of the list of bioDistWclass objects.
>
> bioDistWList<-bioDistW(referenceFeatures = rownames(Block1),
+ bioDistList = bioDistList,
+ weights=sample.weights)
>
> ###### Plot of distances.
> bioDistWPlot(referenceFeatures = rownames(Block1) ,
+ listDistW = bioDistWList,
+ method.cor="spearman")
Warning messages:
1: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
Cannot compute exact p-value with ties
2: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
Cannot compute exact p-value with ties
3: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
Cannot compute exact p-value with ties
4: In plot.window(...) :
relative range of values = 10 * EPS, is small (axis 2)
5: In plot.window(...) :
relative range of values = 10 * EPS, is small (axis 2)
6: In plot.window(...) :
relative range of values = 10 * EPS, is small (axis 2)
7: In plot.window(...) :
relative range of values = 10 * EPS, is small (axis 2)
>
> ###### Computing the matrix of features/distances associated.
>
> fm<-bioDistFeature(Feature = rownames(Block1)[1] ,
+ listDistW = bioDistWList,
+ threshold.cor=0.7)
> bioDistFeaturePlot(data=fm)
>
>
>
>
>
> dev.off()
null device
1
>
providing 
.
