compute_wt_mean

compute weighted.mean intensity of feature in ms/msms level data

A feature clustering algorithm for non-targeted mass spectrometric metabolomics data. This method is compatible with gas and liquid chromatography coupled mass spectrometry, including indiscriminant tandem mass spectrometry data <DOI: 10.1021/ac501530d>.

Helge Hecht

RAMClustR

Mass Spectrometry Metabolomics Feature Clustering and
Interpretation

Corey D. Broeckling

Fayyaz Afsar

Steffen Neumann

Asa Ben-Hur

Jessica Prenni

Matej Trojak

Zargham Ahmad

compute_wt_mean function

<dl><dt>data</dt>
<dd>feature in ms/msms level data</dd>
<dt>global.min</dt>
<dd>minimum intensity in ms/msms level data</dd>
<dt>fmz</dt>
<dd>feature retention time</dd>
<dt>ensure.no.na</dt>
<dd>logical: if TRUE, any 'NA' values in msint and/or msmsint are replaced with numerical values based on 10 percent of feature min plus noise. Used to ensure that spectra are not written with NA values.</dd></dl>

Arguments

compute_wt_mean — compute_wt_mean

<dl>

<dt>data</dt>
<dd>feature in ms/msms level data</dd>


<dt>global.min</dt>
<dd>minimum intensity in ms/msms level data</dd>


<dt>fmz</dt>
<dd>feature retention time</dd>


<dt>ensure.no.na</dt>
<dd>logical: if TRUE, any 'NA' values in msint and/or msmsint are replaced with numerical values based on 10 percent of feature min plus noise. Used to ensure that spectra are not written with NA values.</dd>

</dl>

compute_wt_mean: compute_wt_mean

Description

Usage

Value

Arguments