Description Usage Arguments Details Value Author(s) Examples
This function maps a list of datasets through EntrezGene IDs in order to get the union of the genes.
1 2 | map.datasets(datas, annots, do.mapping = FALSE,
mapping.coln = "EntrezGene.ID", mapping, verbose = FALSE)
|
datas |
List of matrices of gene expressions with samples in rows and probes in columns, dimnames being properly defined. |
annots |
List of matrices of annotations with at least one column named "EntrezGene.ID", dimnames being properly defined. |
do.mapping |
|
mapping.coln |
Name of the column containing the biological annotation to be used to map the different datasets, default is "EntrezGene.ID". |
mapping |
Matrix with columns "EntrezGene.ID" and "probe.x" used to force the mapping such that the probes of platform x are not selected based on their variance. |
verbose |
|
In case of several probes representing the same EntrezGene ID, the most variant is selected if mapping
is not specified. When a EntrezGene ID does not exist in a specific dataset, NA values are introduced.
datas |
List of datasets (gene expression matrices) |
annots |
List of annotations (annotation matrices) |
Benjamin Haibe-Kains
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ## load VDX dataset
data(vdxs)
## load NKI dataset
data(nkis)
## reduce datasets
ginter <- intersect(annot.vdxs[ ,"EntrezGene.ID"], annot.nkis[ ,"EntrezGene.ID"])
ginter <- ginter[!is.na(ginter)][1:30]
myx <- unique(c(match(ginter, annot.vdxs[ ,"EntrezGene.ID"]),
sample(x=1:nrow(annot.vdxs), size=20)))
data2.vdxs <- data.vdxs[ ,myx]
annot2.vdxs <- annot.vdxs[myx, ]
myx <- unique(c(match(ginter, annot.nkis[ ,"EntrezGene.ID"]),
sample(x=1:nrow(annot.nkis), size=20)))
data2.nkis <- data.nkis[ ,myx]
annot2.nkis <- annot.nkis[myx, ]
## mapping of datasets
datas <- list("VDX"=data2.vdxs,"NKI"=data2.nkis)
annots <- list("VDX"=annot2.vdxs, "NKI"=annot2.nkis)
datas.mapped <- map.datasets(datas=datas, annots=annots, do.mapping=TRUE)
str(datas.mapped, max.level=2)
|
Loading required package: survcomp
Loading required package: survival
Loading required package: prodlim
Loading required package: mclust
Package 'mclust' version 5.3
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Loading required package: limma
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Loading required package: iC10
Loading required package: pamr
Loading required package: cluster
Loading required package: iC10TrainingData
Loading required package: AIMS
Loading required package: e1071
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 object is masked from 'package:limma':
plotMA
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colMeans, colSums, colnames, do.call,
duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
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which.max, which.min
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List of 2
$ datas :List of 2
..$ VDX: num [1:150, 1:68] 10.5 10.8 10 11.2 11.6 ...
.. ..- attr(*, "dimnames")=List of 2
..$ NKI: num [1:150, 1:68] 0.053 -0.387 0.004 -0.035 0.082 -0.082 -0.162 -0.397 -0.093 -0.135 ...
.. ..- attr(*, "dimnames")=List of 2
$ annots:List of 2
..$ VDX:'data.frame': 68 obs. of 7 variables:
..$ NKI:'data.frame': 68 obs. of 7 variables:
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