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graphite
package. The PRS method (please see Reference for the details) was adapted to graphite
's graphs where each node is represented only by one gene.
PRS(x, group, pathways, type, preparePaths=TRUE, norm.method=NULL, test.method=NULL, p.th=0.05, logFC.th=2, nperm=1000, both.directions=TRUE, maxNodes=150, minEdges=0, commonTh=2, filterSPIA=FALSE, convertTo="none", convertBy=NULL)
ExpressionSet
object or a gene expression data matrix or count matrix, rows refer to genes, columns to samples
graphite
package or created by preparePathways()
"MA"
for microarray, "RNASeq"
for RNA-Seq, DEtable
data.frame from differential expression analysis, or DEGlist
a list of: log fold-changes of differentially expressed genes and names of the all genes analyses
preparePathways()
. Use FALSE
, if you have done this transformation separatelyNULL
then TMM-normalization is performed. Possible values are: "TMM", "DESeq2", "rLog", "none"
. Ignored for type: "MA","DEtable", "DElist"NULL
then "voomlimma"
is used. Possible values are: "DESeq2", "voomlimma", "vstlimma", "edgeR"
. Ignored for type: "MA","DEtable", "DElist" 1
if you don't want any threshold to be appliedpreparePathways()
preparePathways
if (require(DEGraph)) {
data("Loi2008_DEGraphVignette")
pathways<-pathways("hsapiens","biocarta")[1:10]
PRS( exprLoi2008, classLoi2008, pathways, type="MA", logFC.th=-1, nperm=100)
}
## Not run:
# if (require(gageData)) {
#
# data(hnrnp.cnts)
# hnrnp.cnts<-hnrnp.cnts[rowSums(hnrnp.cnts)>0,]
# group<-c(rep("sample",4), rep("control",4))
# pathways<-pathways("hsapiens","biocarta")[1:10]
# PRS(hnrnp.cnts, group, pathways, type="RNASeq", logFC.th=-1, nperm=100, test="vstlimma")
# }
# ## End(Not run)
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