find_good_features

find 'good' features, acceptable CV at either MS or MSMS level results in keeping

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

find_good_features function

<dl><dt>ramclustObj</dt>
<dd>ramclustObj containing MSdata with optional MSMSdata (MSe, DIA, idMSMS)</dd>
<dt>do.sets</dt>
<dd>select data frame to use.</dd>
<dt>max.cv</dt>
<dd>numeric maximum allowable cv for any feature. default = 0.5</dd>
<dt>qc</dt>
<dd>QC samples found by define_samples</dd></dl>

Arguments

find_good_features — find_good_features

<dl>

<dt>ramclustObj</dt>
<dd>ramclustObj containing MSdata with optional MSMSdata (MSe, DIA, idMSMS)</dd>


<dt>do.sets</dt>
<dd>select data frame to use.</dd>


<dt>max.cv</dt>
<dd>numeric maximum allowable cv for any feature. default = 0.5</dd>


<dt>qc</dt>
<dd>QC samples found by define_samples</dd>

</dl>

find_good_features: find_good_features

Description

Usage

Value

Arguments