dcScore: Compute scores from differential association analysis

In dcanr: Differential co-expression/association network analysis

Description

Implementations and wrappers for existing implementations for methods inferring differential associations/co-expression. This method requires a matrix of expression and a binary condition to compute the differential association scores for all pairs of features (genes). Applications are not limited to analysis of gene expression data and may be used for differential associations in general.

Usage

dcScore(emat, condition, dc.method, ...)

## S4 method for signature 'matrix'
dcScore(emat, condition, dc.method = "zscore", ...)

## S4 method for signature 'Matrix'
dcScore(emat, condition, dc.method = "zscore", ...)

## S4 method for signature 'data.frame'
dcScore(emat, condition, dc.method = "zscore",
  ...)

## S4 method for signature 'ExpressionSet'
dcScore(emat, condition, dc.method = "zscore",
  ...)

## S4 method for signature 'SummarizedExperiment'
dcScore(emat, condition,
  dc.method = "zscore", ...)

## S4 method for signature 'DGEList'
dcScore(emat, condition, dc.method = "zscore", ...)

Arguments

emat	a matrix, Matrix, data.frame, ExpressionSet, SummarizedExperiment or DGEList
condition	a numeric, (with 1's and 2's representing a binary condition), a factor with 2 levels or a character representing 2 conditions
dc.method	a character, representing the method to use. Use dcMethods() to get a list of methods
...	possible arguments are cor.method, diffcoex.beta, ebcoexpress.useBWMC, ebcoexpress.plot, ldgm.lambda, ldgm.ntarget and ldgm.iter. See details

Details

When using data from sequencing experiments, make sure appropriate filtering for low counts and data transformation has been performed. Not doing so will affect estimation of correlation coefficients which most methods rely on.

Additional method specific parameters can be supplied to the function. cor.method can be set to either 'pearson' (default) or 'spearman' to determine the method to use for estimating correlations. These are the two measures currently supported in the package. We recommend using the 'spearman' correlation when dealing with sequencing data.

The beta parameter in the DiffCoEx method can be specified using diffcoex.beta (defaults to 6). This enable soft thresholding of correlations similar to WGCNA.

EBcoexpress specific parameters include ebcoexpress.useBWMC (defaults to TRUE) representing whether to use the bi-weight mid-correlation coefficient or not, and ebcoexpress.plot which plots the diagnostic plots if set to TRUE (defaults to FALSE).

LDGM specific parameters include ldgm.lambda, ldgm.ntarget and ldgm.iter. ldgm.lambda specifies the L1 regularisation parameter to use when fitting the model. This can be tuned and specified by the user. Alternatively, this can be tuned such that the resulting network has a specified number of edges. In this case, ldgm.ntarget should be specified instead. ldgm.iter is the maximum number of iterations to perform when tuning ldgm.lambda using ldgm.ntarget (defaults to 50).

EBcoexpress, GGM-based and ECF are implemented by providing interfaces to, or using functions from the EBcoexpress, GeneNet, and COSINE packages respectively. If using any of these methods, please cite the appropriate packages and the appropriate methodology articles.

Value

a matrix, of scores/statistics representing differential associations; p-values will be returned if FTGI is used and posterior probabilities if EBcoexpress is used.

See Also

dcMethods

Examples

x <- matrix(rnorm(60), 2, 30)
cond <- rep(1:2, 15)
dcScore(x, cond) #defaults to zscore
dcScore(x, cond, dc.method = 'diffcoex')

dcanr documentation built on Nov. 8, 2020, 5:48 p.m.