matNSamp

This function calculates the pairwise number of non-missing values in a matrix.

Performs differential correlation analysis on input
matrices, with multiple conditions specified by a design matrix. Contains
functions to filter, process, save, visualize, and interpret differential
correlations of identifier-pairs across the entire identifier space, or with
respect to a particular set of identifiers (e.g., one). Also contains several
functions to perform differential correlation analysis on clusters (i.e., modules)
or genes. Finally, it contains functions to generate empirical p-values for the
hypothesis tests and adjust them for multiple comparisons. Although the package
was built with gene expression data in mind, it is applicable to other types of
genomics data as well, in addition to being potentially applicable to data from
other fields entirely. It is described more fully in the manuscript
introducing it, freely available at <doi:10.1186/s12918-016-0349-1>.

Andrew McKenzie

DGCA

Differential Gene Correlation Analysis

matNSamp function

<dl><dt>matA</dt>
<dd>Input data matrix with numeric entries.</dd>
<dt>impute</dt>
<dd>Binary value; if true, indicates that imputation was performed previously, and so checking for NAs is not necessary.</dd>
<dt>matB</dt>
<dd>Optional input data matrix with which the comparison with matA will be made.</dd>
<dt>secondMat</dt>
<dd>Logical indicator of whether there is a second matrix in the comparison or not.</dd></dl>

Arguments

Find the number of non-missing values. — matNSamp

<dl>

<dt>matA</dt>
<dd>Input data matrix with numeric entries.</dd>


<dt>impute</dt>
<dd>Binary value; if true, indicates that imputation was performed previously, and so checking for NAs is not necessary.</dd>


<dt>matB</dt>
<dd>Optional input data matrix with which the comparison with matA will be made.</dd>


<dt>secondMat</dt>
<dd>Logical indicator of whether there is a second matrix in the comparison or not.</dd>

</dl>

matNSamp: Find the number of non-missing values.

Description

Usage

Value

Arguments