This section explains how to build a classifier using data provided by the user. Part 1: Training a Classifier Using User Provided Data If using the newest functions provided by XCMS, please convert the “XCMSnExp” object to an “xcmsSet” object using as(XCMSnExp_object, “xcmsSet”). These functions require objects of the “xcmsSet” class which was replaced by the “XCMSnExp” class. While any version of XCMS's peak-picking, retention time correction, and grouping functions may be utilized, this package requires the user to provide two objects produced by the getEIC() and fillPeaks() functions. This tutorial will walk the user through the steps for each. MetaClean has two main use cases: (1) Training a Classifier Using User-Provdied Data and (2) Using Existing Models to Make Predictions. The package is designed for use with the preprocessing package XCMS and can be easily integrated into existing untargeted metabolomics pipelines. Once a predictive model has been built, it can be used to assign predictive labels and class probabilities to untargeted metabolomics datasets. It uses a combination of 11 peak quality metrics and 8 potential machine learning algorithms to build predictive models using user provided chromatographic data and associated labels. MetaClean is a package for building classifiers to identify low quality integrations in untargeted metabolomics data. Overview of MetaClean Overview of MetaClean
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