regenrich_network {RegEnrich} | R Documentation |
As the second step of RegEnrich analysis, network inference is followed by differential expression analysis (regenrich_diffExpr).
Provide a network to 'RegenrichSet' object.
regenrich_network(object, ...) ## S4 method for signature 'RegenrichSet' regenrich_network(object, ...) regenrich_network(object) <- value ## S4 replacement method for signature 'RegenrichSet,TopNetwork' regenrich_network(object) <- value ## S4 replacement method for signature 'RegenrichSet,data.frame' regenrich_network(object) <- value
object |
a 'RegenrichSet' object, to which
|
... |
arguments for network inference.
After constructing a 'RegenrichSet' object using |
value |
either a 'TopNetwork' object or 'data.frame' object. If value is a 'data.frame' object, then the number of columns of |
This function returns a 'RegenrichSet' object with an updated
'network' and 'topNetP' slots, which are 'TopNetwork' objects, and
an updated 'paramsIn' slot.
See TopNetwork-class
class for more details.
This function returns a 'RegenrichSet' object with an updated
'network' and 'topNetP' slots, which are 'TopNetwork' objects, and an
updated 'paramsIn' slot.
See TopNetwork-class
class for more details.
Previous step regenrich_diffExpr
,
and next step regenrich_enrich
. User defined
network regenrich_network<-
# library(RegEnrich) data("Lyme_GSE63085") data("TFs") data = log2(Lyme_GSE63085$FPKM + 1) colData = Lyme_GSE63085$sampleInfo # Take first 2000 rows for example data1 = data[seq(2000), ] design = model.matrix(~0 + patientID + week, data = colData) # Initializing a 'RegenrichSet' object object = RegenrichSet(expr = data1, colData = colData, method = 'limma', minMeanExpr = 0, design = design, contrast = c(rep(0, ncol(design) - 1), 1), networkConstruction = 'COEN', enrichTest = 'FET') # Differential expression analysis (object = regenrich_diffExpr(object)) # Network inference using 'COEN' method (object = regenrich_network(object))