Description Usage Arguments Value Examples

GMS.Lasso recovers the abundance of missing peaks via either TS.Lasso or the minimum abundance per compound.

1 2 |

`input_data` |
Raw abundance matrix with missing value, with features in rows and samples in columns. |

`alpha` |
Weights for L1 penalty in Elastic Net. The default and suggested value is alpha=1, which is for Lasso. |

`nfolds` |
The number of folds used in parameter (lambda) tuning. |

`log.scale` |
Whether the input_data needs log scale transform.The default is log.scale=T, assuming input_data is the raw abundance matrix. If input_data is log abundance matrix, log.scale=F. |

`TS.Lasso` |
Whether to use TS.Lasso or the minimum per compound for imputation. |

`imputed.final` |
The imputed abundance matrix at the scale of input_data. |

1 2 3 4 5 6 7 8 9 | ```
data('tcga.bc')
# tcga.bc contains mass specturm abundance of 150 metabolites for 30 breast cancer
# tumor and normal tissue samples with missing values.
imputed.compound.min=GMS.Lasso(tcga.bc,log.scale=TRUE,TS.Lasso=FALSE)
# Impute raw abundance matrix tcga.bc with compound minimum
imputed.tslasso=GMS.Lasso(tcga.bc,log.scale=TRUE,TS.Lasso=TRUE)
# Impute raw abundance matrix tcga.bc with TS.Lasso
``` |

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