Randomized CIBRA signal measure calculation

Usage

randomization(
  data,
  n_cases,
  n_control,
  iterations,
  confidence,
  case,
  control,
  covariates = c(),
  covariate_matrix = NULL,
  parallel = FALSE,
  speedup = FALSE,
  column = "rand",
  method = "DESeq2",
  permutation = "sample"
)

Arguments

data: RNA count dataframe with genes as rows and samples as columns (dataframe)
n_cases: number of cases in the data (num)
n_control: number of controls in the data (num)
iterations: number of iterations to run the permutation (num)
confidence: confidence (Tau) for the proportion calculation (num)
case: case definition (str)
control: control definition (str)
covariates: list of column names from the definition matrix to use as covariates (supported only with DESeq2)
covariate_matrix: design dataframe of the covariates, columns to take along as covariate values and samples as rownames.
parallel: boolean value indicating if the method should be run in parallel (boolean)
speedup: boolean value if the DESeq2 sould be run in speedup mode (boolean)
column: column name to give to the permutated sample column (string)
method: DE analysis method to use (options: DESeq2, edgeR and limma-voom)
permutation: permutatin appraoch to use, either sample or full (string)

Value

list containing 6 variables: the mean random proportion (float), the standard deviation of the calculated proportions (float), the mean random significant area (float), the standard deviation of the calculated significant area (float), signal_data: dataframe of the results containing the proportion, and significant area for each iteration, pvalue matrix of the differential expression analysis for each iteration as a column and genes as rows, adjusted pvalue matrix of the differential expression analysis for each iteration as a column and genes as rows, foldchange matrix of the differential expression analysis for each iteration as a column and genes as rows

Examples

# Internal function