Calculates intramodular connectivity, i.e., connectivity of nodes to other nodes within the same module.

`intramodularConnectivity(adjMat, colors, scaleByMax = FALSE)`intramodularConnectivity.fromExpr(datExpr, colors,
corFnc = "cor", corOptions = "use = 'p'",
weights = NULL,
distFnc = "dist", distOptions = "method = 'euclidean'",
networkType = "unsigned", power = if (networkType=="distance") 1 else 6,
scaleByMax = FALSE,
ignoreColors = if (is.numeric(colors)) 0 else "grey",
getWholeNetworkConnectivity = TRUE)

adjMat

adjacency matrix, a square, symmetric matrix with entries between 0 and 1.

colors

module labels. A vector of length `ncol(adjMat)`

giving a module label for each
gene (node) of the network.

scaleByMax

logical: should intramodular connectivities be scaled by the maximum IM connectivity in each module?

datExpr

data frame or matrix containing expression data. Columns correspond to genes and rows to samples.

corFnc

character string specifying the function to be used to calculate co-expression similarity for correlation networks. Defaults to Pearson correlation. Any function returning values between -1 and 1 can be used.

corOptions

character string specifying additional arguments to be passed to the function given
by `corFnc`

. Use `"use = 'p', method = 'spearman'"`

to obtain Spearman correlation.

weights

optional matrix of the same dimensions as `datExpr`

, giving the weights for individual
observations in `datExpr`

. These will be passed on to the correlation function.

distFnc

character string specifying the function to be used to calculate co-expression
similarity for distance networks. Defaults to the function `dist`

.
Any function returning non-negative values can be used.

distOptions

character string specifying additional arguments to be passed to the function given
by `distFnc`

. For example, when the function `dist`

is used, the argument `method`

can be used to specify various ways of computing the distance.

networkType

network type. Allowed values are (unique abbreviations of) `"unsigned"`

,
`"signed"`

, `"signed hybrid"`

, `"distance"`

.

power

soft thresholding power.

ignoreColors

level(s) of `colors`

that identifies unassigned genes. The intramodular
connectivity in this "module" will not be calculated.

getWholeNetworkConnectivity

logical: should whole-network connectivity be computed as well? For large networks, this can be quite time-consuming.

If input `getWholeNetworkConnectivity`

is `TRUE`

, a data frame with 4 columns giving the total connectivity, intramodular connectivity, extra-modular
connectivity, and the difference of the intra- and extra-modular connectivities for all genes; otherwise a
vector of intramodular connectivities,

The module labels can be numeric or character. For each node (gene), the function sums adjacency entries (excluding the diagonal) to other nodes within the same module. Optionally, the connectivities can be scaled by the maximum connectivy in each module.

Dong J, Horvath S (2007) Understanding Network Concepts in Modules, BMC Systems Biology 2007, 1:24