neighborNet {NeighborNet} | R Documentation |
Neighbor Net: An approach to infer putative disease-specific mechanisms using neighboring gene networks.
neighborNet(de, ref, listofgenes, threshold = 0.1, minsize = 2)
de |
a vector including the differentially expressed genes; |
ref |
the reference vector for all genes in the analysis |
listofgenes |
a list representing the neighbor networks associated to each gene; the name of the list must be the same as genes in the |
threshold |
a threshold of choosing significant neighbor networks (default is 0.1) |
minsize |
minimum size of the neighbor networks that should be considered in the analysis (default is 2) |
See details in the cited articles.
An object of class graphNEL
.
Sahar Ansari and Sorin Draghici
Sahar Ansari, Michele Donato, Nafiseh Saberian, Sorin Draghici; An approach to infer putative disease-specific mechanisms using neighboring gene networks, Bioinformatics, Volume 33, Issue 13, 1 July 2017, Pages 1987–1994
# load multiple colorectal cancer study (public data available in GEO # ID: GSE4183, GSE9348, GSE21510, GSE32323, GSEl8671) # These files contains the tables, produced by the limma package with # added gene information. # The table contains the expression fold change and signficance of each # probe set comparing colorectal cancer disease and normal. load(system.file("extdata/dataColorectal4183.RData", package = "NeighborNet")) load(system.file("extdata/dataColorectal9348.RData", package = "NeighborNet")) load(system.file("extdata/dataColorectal21510.RData", package = "NeighborNet")) load(system.file("extdata/dataColorectal32323.RData", package = "NeighborNet")) load(system.file("extdata/dataColorectal8671.RData", package = "NeighborNet")) head(dataColorectal4183) load(system.file("extdata/listofgenes.RData", package = "NeighborNet")) head(listofgenes) # select differentially expressed genes for each data set at p-value below 1% # and absolute value for more than 1.5 and save their entrez ID in a vector de1 to de5 pvThreshold <- 0.01 foldThreshold <- 1.5 de1 <- dataColorectal4183$EntrezID [ dataColorectal4183$adj.P.Val < pvThreshold & abs(dataColorectal4183$logFC) > foldThreshold] de2 <- dataColorectal9348$EntrezID [ dataColorectal9348$adj.P.Val < pvThreshold & abs(dataColorectal9348$logFC) > foldThreshold] de3 <- dataColorectal21510$EntrezID [ dataColorectal21510$adj.P.Val < pvThreshold & abs(dataColorectal21510$logFC) > foldThreshold] de4 <- dataColorectal32323$EntrezID [ dataColorectal32323$adj.P.Val < pvThreshold & abs(dataColorectal32323$logFC) > foldThreshold] de5 <- dataColorectal8671$EntrezID [ dataColorectal8671$adj.P.Val < pvThreshold & abs(dataColorectal8671$logFC) > foldThreshold] all <- unique( c(dataColorectal4183$EntrezID, dataColorectal9348$EntrezID, dataColorectal21510$EntrezID, dataColorectal32323$EntrezID, dataColorectal8671$EntrezID)) de <- unique( c(de1,de2,de3,de4,de5)) sig_net <- neighborNet (de, all, listofgenes)