pqm_development

<p>Data frame with peaks quality metrics and labels for all of the 500 EICs in the example development dataset.</p>

datasets

Utilizes 11 peak quality metrics and 8 diverse machine learning algorithms to build a classifier for the automatic
assessment of peak integration quality of peaks from untargeted metabolomics analyses.
The 12 peak quality metrics were adapted from those defined in the following references:
Zhang, W., & Zhao, P.X. (2014) <doi:10.1186/1471-2105-15-S11-S5>
Toghi Eshghi, S., Auger, P., & Mathews, W.R. (2018) <doi:10.1186/s12014-018-9209-x>.

Kelsey Chetnik

MetaClean

Detection of Low-Quality Peaks in Untargeted Metabolomics Data

pqm_development function

<p>A data frame with 13 variables (EIC Number, the 11 peak quality metrics, and Class Labels): <code>EICNo</code>,
<code>ApexBoundaryRatio_mean</code>, <code>ElutionShift_mean</code>, <code>FWHM2Base_mean</code>, <code>Jaggedness_mean</code>, <code>Modelaity_mean</code>,
<code>RetentionTimeCorrelation_mean</code>, <code>Symmetry_mean</code>, <code>GaussianSimilarity_mean</code>, <code>Sharpness_mean</code>,
<code>TPASR_mean</code>, <code>ZigZag_mean</code>, and <code>Class</code>.</p>

pqm_development: Example Peak Quality Metrics Data Frame for Development Dataset.

Description

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