Description Usage Arguments Value Examples

shiftCor provides the QC based signal correction for large scale metabolomics and targeted proteomics.

1 2 3 |

`samPeno` |
The file with the meta information including the sample name, batches, class and order. |

`samFile` |
The file with the expression information. |

`Frule` |
Modified n precent rule function. A variable will be kept if it has a non-zero value for at least n precent of samples in any one group. (Default: 0.8) |

`MLmethod` |
The machine learning method for QC based signal correction. i.e. QC based random forest signal correction (QC-RFSC) and QC based LOESS signal correction (QC-RLSC). |

`ntree` |
Number of trees to grow in random forest model. |

`QCspan` |
The smoothing parameter for QC-RLSC which controls the bias-variance tradeoff in QC-RLSC method if the QCspan is set at '0', the generalised cross-validation will be performed to avoid overfitting the observed data. |

`degree` |
Lets you specify local constant regression (i.e., the Nadaraya-Watson estimator, degree=0), local linear regression (degree=1), or local polynomial fits (degree=2, the default) for QC-RLSC. |

`imputeM` |
The parameter for imputation method i.e., nearest neighbor averaging, 'KNN'; minimum values, 'min'; Half of minimum values, 'minHalf'; median values, 'median'. |

`coCV` |
Define the cutoff value (0-100) of CV for controlling the number of features. |

`plot` |
Defines if images of feature quality should be generated (TRUE) or not (FALSE). Defaults to FALSE. |

the shiftCor files. See the details at https://stattarget.github.io

1 2 3 4 | ```
datpath <- system.file('extdata',package = 'statTarget')
samPeno <- paste(datpath,'MTBLS79_sampleList.csv', sep='/')
samFile <- paste(datpath,'MTBLS79.csv', sep='/')
shiftCor(samPeno,samFile, MLmethod = 'QCRFSC', imputeM = 'KNN',coCV = 30)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.