Crohn

A dataset containing the number of counts of 48 different genera in a group
of 975 samples (including 662 samples of patients with Crohn`s disease and
313 controls).
The <code>data.frame</code> is composed by 48 genera and a factor variable

data

In the context of high-throughput genetic data,
CoDaCoRe identifies a set of sparse biomarkers that are
predictive of a response variable of interest (Gordon-Rodriguez
et al., 2021) <doi:10.1093/bioinformatics/btab645>. More
generally, CoDaCoRe can be applied to any regression problem
where the independent variable is Compositional (CoDa), to
derive a set of scale-invariant log-ratios (ILR or SLR) that
are maximally associated to a dependent variable.

Elliott Gordon-Rodriguez

codacore

Learning Sparse Log-Ratios for Compositional Data

Thomas Quinn

Crohn function

The <code>data.frame</code> is composed by 48 genera and a variable<dl>
 <dt>genera</dt>
<dd>The first 48 columns, from g_Turicibacter until
 g_Bilophila referred to different genera.</dd> <dt>y</dt>
<dd>a <code>factor</code> indicating if the sample corresponds to a case (
 CD) or a control (no).</dd>
</dl>

Format

Microbiome composition related to Crohn`s disease study — Crohn

The <code>data.frame</code> is composed by 48 genera and a variable<dl>
 <dt>genera</dt>
<dd>The first 48 columns, from g_Turicibacter until
 g_Bilophila referred to different genera.</dd>

 <dt>y</dt>
<dd>a <code>factor</code> indicating if the sample corresponds to a case (
 CD) or a control (no).</dd>


</dl>

Crohn: Microbiome composition related to Crohn`s disease study

Description

Arguments

Format

References