rc.feature.filter.cv

extractor for xcms objects in preparation for clustering

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

rc.feature.filter.cv function

<dl><dt>ramclustObj</dt>
<dd>ramclustObj containing MSdata with optional MSMSdata (MSe, DIA, idMSMS)</dd>
<dt>qc.tag</dt>
<dd>character vector of length one or two. If length is two, enter search string and factor name in $phenoData slot (i.e. c("QC", "sample.type"). If length one (i.e. "QC"), will search for this string in the 'sample.names' slot by default.</dd>
<dt>max.cv</dt>
<dd>numeric maximum allowable cv for any feature. default = 0.5</dd></dl>

Arguments

Author

rc.feature.filter.cv — rc.feature.filter.cv

<dl>

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


<dt>qc.tag</dt>
<dd>character vector of length one or two. If length is two, enter search string and factor name in $phenoData slot (i.e. c("QC", "sample.type"). If length one (i.e. "QC"), will search for this string in the 'sample.names' slot by default.</dd>


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

</dl>

rc.feature.filter.cv: rc.feature.filter.cv

Description

Usage

Value

Arguments

Author

Details

References