Description Usage Arguments Value See Also Examples
This is the first step of RegEnrich analysis. differential expression analysis by this function needs to be performed on a 'RegenrichSet' object.
1 2 3 4 | regenrich_diffExpr(object, ...)
## S4 method for signature 'RegenrichSet'
regenrich_diffExpr(object, ...)
|
object |
a 'RegenrichSet' object, which is initialized by
|
... |
arguments for differential analysis.
After constructing a 'RegenrichSet' object,
all arguments for RegEnrich analysis have been initialized and
stored in 'paramsIn“ slot. while the arguments for differential analysis
can be re-specified here. |
This function returns a 'RegenrichSet' object with an updated
'resDEA' slot, which is a 'DeaSet' object, and an updated 'paramsIn' slot.
See newDeaSet
function for more details about 'DeaSet' class.
If an argument not in the above list is specified in the regenrich_diffExpr
function, a warning or error will be raised.
Initialization of a 'RegenrichSet' object
RegenrichSet
,and next step
regenrich_network
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | # 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')
# Using the predifined parameters in the previous step
(object = regenrich_diffExpr(object))
# re-specifying parameter 'minMeanExpr'
print(slot(object, 'paramsIn')$minMeanExpr)
(object = regenrich_diffExpr(object, minMeanExpr = 1))
print(slot(object, 'paramsIn')$minMeanExpr)
# Unrecognized argument 'unrecognizedArg' (Error)
# object = regenrich_diffExpr(object, minMeanExpr = 1,
# unrecognizedArg = 23)
# Argument not for differential expression analysis (Warning)
# print(slot(object, 'paramsIn')$networkConstruction)
# (object = regenrich_diffExpr(object, minMeanExpr = 1,
# networkConstruction = 'GRN'))
# print(slot(object, 'paramsIn')$networkConstruction) # not changed
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