Description Usage Arguments Value Examples

`hyp()`

is the main driver for differential analysis. It will perform
the following steps:

Transform

`normal`

and`disease`

to approximations for relative enzyme activities (not if type = "raw") by inversion.Calculate enzyme activity inter- and intra group log-fold changes and credible intervals.

Estimate the dispersions with an empirical Bayes approximation and use this to extract significance.

If type is "fva", it will perform a flux variability analysis to see whether differences can be due to variation in the fluxes alone.

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`reacts` |
The reaction list. |

`samples` |
A factor or character string with either "normal" and "disease" entries or a single element named "ratio" if mass-action terms and fluxes are disease/normal ratios. Only required for type "exact". |

`ma_terms` |
A matrix or data frame containing the metabolic in the columns. |

`fluxes` |
Pre-compputed or measured fluxes with the same classification
as in |

`type` |
The type of analysis to be performed. Either 'bias', 'exact', 'fva' or 'raw' for a pass-through option. |

`correction_method` |
A correction method for the multiple test p-values. Takes the same arguments as the method argument in p.adjust. |

`cred_level` |
The confidence level for the confidence intervals. Defaults to 95%. |

`sorted` |
Whether the results should be sorted by p-value and mean log-fold change. |

`obj` |
Only needs to be set if type = 'fva'. Defines the
objective reaction whose flux is maximized. Can be any of the acceptable
formats for |

`v_min` |
The smallest allowed flux for each reaction for all reactions.
Must be >=0. Can be of length 1 or |

`alpha` |
The minimum fraction of maximum objective value required during flux variability analysis. The default is 100% of optimum. |

`full` |
If TRUE also returns the individual log fold changes along with the differential regulation data. |

If full is FALSE only returns the generated hypothesis as a data frame. This is a data frame with the following columns:

- idx
The index of the reaction.

- name
The name of the reaction/enzyme.

- reaction
The actual reaction, e.g. A <=> B + C.

- type
What type of regulation, "up", "down" or "same".

- sd_normal
Standard deviation of the log2-fold changes between samples from the normal group.

- sd_disease
Standard deviation of the log2-fold changes between samples of the disease vs normal group.

- mean_log_fold
The mean log2-fold change between the disease and normal group.

- ci_low, ci_high
The bayesian credible interval for the confidence level given by

`cred_level`

. Those are calculated using the bayesian bootstrap.- pval
The empricial Bayes estimate of the p-values.

- corr_pval
The p-values corrected for multiple testing.

- fva_log_fold
Only if type = "fva". The largest absolute log-fold change that can be explained by flux variability alone.

If full is TRUE returns a list of generated hypothesis and the individual log fold changes between all reference basis and between reference and treatments. The full output will be a list with following elements:

- hyp
The generated hypotheses together with statistics and reactions.

- lfc_normal
The log2-fold changes of enzyme activity within the normal group for each of the irreversible reactions. Those are never sorted, so the first entry corresponds to the first reaction, etc.

- lfc_disease
The log2-fold changes of enzyme activity within between the disease and normal group for each of the irreversible reactions. Also never sorted.

- lfc_va
Only if type=='fva'. The maximum log2-fold changes for the fluxes obtained by flux variability analysis. Also never sorted.

- fva
Only if type=='fva'. The flux bounds obtained from flux variability analysis.

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