This function performs gene screening based on a given trait and gene network properties
automaticNetworkScreening(
datExpr,
y,
power = 6,
networkType = "unsigned",
detectCutHeight = 0.995,
minModuleSize = min(20, ncol(as.matrix(datExpr))/2),
datME = NULL,
getQValues = TRUE,
...)data frame containing the expression data, columns corresponding to genes and rows to samples
vector containing trait values for all samples in datExpr
soft thresholding power used in network construction
character string specifying network type. Allowed values are (unique abbreviations
of) "unsigned", "signed", "hybrid".
cut height of the gene hierarchical clustering dendrogram. See
cutreeDynamic for details.
minimum module size to be used in module detection procedure.
optional specification of module eigengenes. A data frame whose columns are the module eigengenes. If given, module analysis will not be performed.
logical: should q-values (local FDR) be calculated?
other arguments to the module identification function blockwiseModules
A list with the following components:
a data frame containing results of the network screening procedure. See
networkScreening for more details.
calculated module eigengenes (or a copy of the input datME, if given).
hub gene significance for all calculated modules. See
hubGeneSignificance.
Network screening is a method for identifying genes that have a high gene significance and are members
of important modules at the same time.
If datME is given, the function calls networkScreening with the default
parameters. If datME is not given, module eigengenes are first calculated using network analysis
based on supplied parameters.
networkScreening, hubGeneSignificance,
networkScreening, cutreeDynamic