getEntropyBi

Computes the joint entropy of two numeric vectors using the specified number of bins and spline order.

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

getEntropyBi function

<dl><dt>x_1</dt>
<dd>A numeric vector for the first variable.</dd>
<dt>x_2</dt>
<dd>A numeric vector for the second variable. Must be the same length as `x_1`.</dd>
<dt>bin</dt>
<dd>An integer specifying the number of bins. Default is 6.</dd>
<dt>sp_order</dt>
<dd>An integer specifying the spline order. Must be less than `bin`. Default is 2.</dd></dl>

Arguments

Calculate Joint Entropy for Two Variables — getEntropyBi

<dl>

<dt>x_1</dt>
<dd>A numeric vector for the first variable.</dd>


<dt>x_2</dt>
<dd>A numeric vector for the second variable. Must be the same length as `x_1`.</dd>


<dt>bin</dt>
<dd>An integer specifying the number of bins. Default is 6.</dd>


<dt>sp_order</dt>
<dd>An integer specifying the spline order. Must be less than `bin`. Default is 2.</dd>

</dl>

getEntropyBi: Calculate Joint Entropy for Two Variables

Description

Usage

Value

Arguments

Examples