CORmat2vecPermu

Computes the correlation between a randomly sampled vector from a matrix and a given vector.
The sampling is done multiple times to generate a distribution.

The biases introduced in association measures, particularly mutual information,
are influenced by factors such as tumor purity, mutation burden, and hypermethylation.
This package provides the estimation of conditional mutual information (CMI) and its
statistical significance with a focus on its application to multi-omics data. Utilizing
B-spline functions (inspired by Daub et al. (2004) <doi:10.1186/1471-2105-5-118>), the package offers tools to estimate the association between heterogeneous multi-
omics data, while removing the effects of confounding factors. This helps to unravel complex
biological interactions. In addition, it includes methods to evaluate the statistical significance
of these associations, providing a robust framework for multi-omics data integration and
analysis. This package is ideal for researchers in computational biology, bioinformatics,
and systems biology seeking a comprehensive tool for understanding interdependencies in
omics data.

Gaojianyong Wang

conMItion

Conditional Mutual Information Estimation for Multi-Omics Data

CORmat2vecPermu function

<dl><dt>mat</dt>
<dd>A numeric matrix. For example, each row represents a gene and each column represents a sample.</dd>
<dt>vec</dt>
<dd>A numeric vector, with length equal to the number of columns in `mat`.</dd>
<dt>cor_type</dt>
<dd>Type of correlation to calculate: "Pearson", "Kendall", or "Spearman". Default is "Pearson".</dd>
<dt>bulkIdx</dt>
<dd>Index to divide the task when processing many permutations. Default is 0.</dd>
<dt>permutationTimes</dt>
<dd>Number of permutations for sampling. Default is 1000.</dd>
<dt>seedNum</dt>
<dd>Seed for random number generation. Default is 99999999.</dd></dl>

Arguments

Permuted Correlation Between Matrix and Vector — CORmat2vecPermu

<dl>

<dt>mat</dt>
<dd>A numeric matrix. For example, each row represents a gene and each column represents a sample.</dd>


<dt>vec</dt>
<dd>A numeric vector, with length equal to the number of columns in `mat`.</dd>


<dt>cor_type</dt>
<dd>Type of correlation to calculate: "Pearson", "Kendall", or "Spearman". Default is "Pearson".</dd>


<dt>bulkIdx</dt>
<dd>Index to divide the task when processing many permutations. Default is 0.</dd>


<dt>permutationTimes</dt>
<dd>Number of permutations for sampling. Default is 1000.</dd>


<dt>seedNum</dt>
<dd>Seed for random number generation. Default is 99999999.</dd>

</dl>

CORmat2vecPermu: Permuted Correlation Between Matrix and Vector

Description

Usage

Value

Arguments

Examples