View source: R/fold_change_class.R
fold_change | R Documentation |
Fold change is the relative change in mean (or non-parametric equivalent) intensities of a feature between all pairs of levels in a factor.
fold_change(
factor_name,
paired = FALSE,
sample_name = character(0),
threshold = 2,
control_group = character(0),
method = "geometric",
conf_level = 0.95,
...
)
factor_name |
(character) The name of a sample-meta column to use. |
paired |
(logical) Paired fold change. Allowed values are limited to the following:
The default is |
sample_name |
(character) The name of a sample_meta column containing sample identifiers for paired sampling. The default is |
threshold |
(numeric) The fold change threshold for labelling features as significant. The default is |
control_group |
(character) The level name of the group used in the denominator (where possible) when computing fold change. The default is |
method |
(character) Fold change method. Allowed values are limited to the following:
The default is |
conf_level |
(numeric) The confidence level of the interval. The default is |
... |
Additional slots and values passed to |
A fold_change
object with the following output
slots:
fold_change | (data.frame) The fold change between groups. |
lower_ci | (data.frame) Lower confidence interval for fold change. |
upper_ci | (data.frame) Upper confidence interval for fold change. |
significant | (data.frame) A logical indictor of whether the calculated fold change including the estimated confidence limits is greater than the selected threshold. |
A fold_change
object inherits the following struct
classes:
[fold_change]
>> [model]
>> [struct_class]
Price Jr RM, Bonett DG (2020). "Confidence Intervals for Ratios of Means and Medians." Journal of Educational and Behavioral Statistics, 45(6), 750-770.
M = fold_change(
factor_name = "V1",
sample_name = character(0),
paired = FALSE,
threshold = 2,
control_group = character(0),
method = "geometric",
conf_level = 0.95)
D = MTBLS79_DatasetExperiment()
M = fold_change(factor_name='Class')
M = model_apply(M,D)
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