sCD14

A dataset containing the number of counts of 60 different genera in a group
of 151 samples (including HIV - infected and non - infected patients).
The <code>data.frame</code> is composed by 60 genera and a numeric 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

sCD14 function

The <code>data.frame</code> is composed by 60 genera and a variable<dl>
 <dt>genera</dt>
<dd>The first 60 columns, from g_Prevotella until
 o_NB1-n_g_unclassified referred to different genera.</dd> <dt>sCD14</dt>
<dd>a <code>numeric</code> variable with the value of the inflammation
 parameter sCD14 for each sample.</dd>
</dl>

Format

Microbiome and sCD14 inflammation parameter — sCD14

The <code>data.frame</code> is composed by 60 genera and a variable<dl>
 <dt>genera</dt>
<dd>The first 60 columns, from g_Prevotella until
 o_NB1-n_g_unclassified referred to different genera.</dd>

 <dt>sCD14</dt>
<dd>a <code>numeric</code> variable with the value of the inflammation
 parameter sCD14 for each sample.</dd>


</dl>

sCD14: Microbiome and sCD14 inflammation parameter

Description

Arguments

Format

References