regenrich_diffExpr: Differential expression analysis step

In WTaoUMC/RegEnrich: Gene regulator enrichment analysis

Description

This is the first step of RegEnrich analysis. differential expression analysis by this function needs to be performed on a 'RegenrichSet' object.

Usage

regenrich_diffExpr(object, ...)

## S4 method for signature 'RegenrichSet'
regenrich_diffExpr(object, ...)

Arguments

object

a 'RegenrichSet' object, which is initialized by RegenrichSet function.

...

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. These arguments include 'method', 'minMeanExpr', 'design', 'reduced', 'contrast', 'coef', 'name', 'fitType', 'sfType', 'betaPrior', 'minReplicatesForReplace', 'useT', 'minmu', 'parallel', 'BPPARAM', 'altHypothesis', 'listValues', 'cooksCutoff', 'independentFiltering', 'alpha', 'filter', 'theta', 'filterFun', 'addMLE', 'blind', 'ndups', 'spacing', 'block', 'correlation', 'weights', 'proportion', 'stdev.coef.lim', 'trend', 'robust', and 'winsor.tail.p'. See RegenrichSet function for more details about these arguments.

Value

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.

See Also

Initialization of a 'RegenrichSet' object RegenrichSet,and next step regenrich_network.

Examples

# 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

WTaoUMC/RegEnrich documentation built on Aug. 4, 2021, 4:11 p.m.