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Perform Permutation-Based Linear Modeling with Covariates using Limma

Source: R/Limma_permutating.R
Limma_permutating.Rd

This 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.

Usage

Limma_permutating(
  x,
  group.name,
  meta.info,
  formula.str,
  trend,
  robust,
  permutating.group
)

Arguments

x

A list containing two or more 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.

group.name

A character string indicating the name of the group variable in meta.info to be used in the analysis.

meta.info

A data frame containing the metadata for the samples. This includes sample grouping and any covariates to be included in the model.

formula.str

A string specifying the formula to be used in model fitting. It should follow the standard R formula syntax (e.g., ~ covariate1 + covariate2).

trend

A logical value indicating whether to allow for an intensity-dependent trend in the prior variance.

robust

A logical value indicating whether to use a robust fitting procedure to protect against outliers.

permutating.group

Logical, If TRUE, the permutation for calculating the null distribution is performed by permuting the target group only specified in group.name. If FALSE, the entire meta.info will be permuted (recommended to be set to TRUE).

Value

A list containing the following elements:

d

A vector of the test statistics (log-fold changes or F-statistics) for each feature.

s

A vector of the standard deviations for each feature, adjusted by the empirical Bayes procedure.

Details

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.

See also

lmFit, eBayes, topTable, makeContrasts