R/Limma_permutating.R
Limma_permutating.RdThis function performs linear modeling using the Limma package with permutation of the covariates to evaluate the test statistics under random assignments. It handles two-group comparisons and multi-group settings.
Limma_permutating(x, group.name, meta.info, formula.str)A data matrices where rows represent features (e.g., genes, proteins) and columns represent samples. The list should contain at least two matrices for pairwise group comparison.
A character string indicating the name of the group
variable in meta.info to be used in the analysis.
A data frame containing the metadata for the samples. This includes sample grouping and any covariates to be included in the model.
A string specifying the formula to be used in model
fitting. It should follow the standard R formula syntax
(e.g., ~ covariate1 + covariate2).
A list containing the following elements:
A vector of the test statistics (log-fold changes or F-statistics) for each feature.
A vector of the standard deviations for each feature, adjusted by the empirical Bayes procedure.
This function combines the data matrices from different groups and permutes
the covariates from meta.infobefore fitting a linear model using Limma.
Permutation helps assess how the covariates behave under random conditions,
providing a null distribution of the test statistics. For two-group
comparisons, the function computes contrasts between the two groups and
applies empirical Bayes moderation. For multi-group analysis with a single
covariate, pairwise contrasts are computed, and the moderated F-statistic is
calculated for each feature.