fold_change {structToolbox} | R Documentation

## Fold change

### Description

Fold change is the relative change in mean (or non-parametric equivalent) intensities of a feature between all pairs of levels in a factor.

### Usage

    fold_change(
      alpha = 0.05,
      factor_name,
      paired = FALSE,
      sample_name = character(0),
      threshold = 2,
      control_group = character(0),
      method = "geometric",
      ...
    )

### Arguments

`alpha` | (numeric) The p-value cutoff for determining significance. The default is `0.05`.
`factor_name` | (character) The name of a sample-meta column to use.
`paired` | (logical) Paired fold change. Allowed values are limited to the following: `"TRUE"`: Fold change is calculated taking into account paired sampling. `"FALSE"`: Fold change is calculated assuming there is no paired sampling. The default is `FALSE`.
`sample_name` | (character) The name of a sample_meta column containing sample identifiers for paired sampling. The default is `character(0)`.
`threshold` | (numeric) The fold change threshold for labelling features as significant. The default is `2`.
`control_group` | (character) The level name of the group used in the denominator (where possible) when computing fold change. The default is `character(0)`.
`method` | (character) Fold change method. Allowed values are limited to the following: `"geometric"`: A log transform and a t-test is used to calculate fold change and estimate confidence intervals. In the non-transformed space this is equivalent to fold change using geometric means. `"median"`: A log transform and the method described by Price and Bonett to calculate fold change and estimate confidence intervals. In the non-transformed space this is equivalent to using group medians to calculate fold change. The default is `"geometric"`.
`...` | Additional slots and values passed to `struct_class`.

### Value

A `fold_change` object.

### References

Price RM, Bonett DG (2002). “Distribution-Free Confidence Intervals for Difference and Ratio of Medians.” Journal of Statistical Computation and Simulation, 72(2), 119-124.

### Examples

    D = MTBLS79_DatasetExperiment()
    M = fold_change(factor_name='class')
    M = model_apply(M,D)

[Package structToolbox version 1.2.0 Index]