dcScore {dcanr} | R Documentation

## Compute scores from differential association 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')

[Package dcanr version 1.8.0 Index]