Limma anlaysis: integration of co-factor and block key usage (for datasets with paired samples).

R/preprocess_data_agilent_limma.R

@@ -42,7 +42,7 @@ preprocess_data_agilent_limma <- function(input_data_dir, output_data_file,
   batch_data <- log2(batch$E)
 
   # We change the probe ids (1 to 45015) to be the probe names instead (A_XX_PXXXX).
-  rownames(batch_data) <- (batch$genes)$ProbeName
+  rownames(batch_data) <- (batch$genes)$ProbeName # nolint
   remove(raw_data_input_dir, batch)
 
   # We run the LIMMA pre-processing method on the data.

R/run_limma.R

@@ -25,25 +25,25 @@ run_limma <- function(pheno_data, eset, limma_parameters,
   clinical_factor    <- limma_parameters[[1]]
   coefficients       <- limma_parameters[[2]]
   has_block          <- !is.null(cofactor_name)
-  is_factorial_limma <- length(coefficients) == 3
 
   # We create the design matrix (regular case - no block).
   clinical         <- factor(pheno_data[[clinical_factor]])
   design           <- stats::model.matrix(~ 0 + clinical)
   colnames(design) <- levels(clinical)
+
   # If necessary we add a block based on a co-factor.
   # For instance, if we have paired samples.
   if (has_block) {
     clinical_cofactor <- factor(pheno_data[[cofactor_name]])
     design            <- stats::model.matrix(~ 0 + clinical + clinical_cofactor)
-    colnames(design)  <- c(levels(clinical), clinical_cofactor)
+    colnames(design)  <- c(levels(clinical), cofactor_name)
   }
 
   # We build the contrast (default only one).
   contrast <- eval(substitute(limma::makeContrasts(coeff1 = c1, levels = design),
                               list(c1 = coefficients[1])))
   # For larger lists of coefficients, we add them all.
-  if (is_factorial_limma) {
+  if (limma_parameters$factorial) {
     contrast <- eval(substitute(limma::makeContrasts(coeff1 = c1,
                                                      coeff2 = c2,
                                                      coeff3 = c3,
@@ -59,8 +59,8 @@ run_limma <- function(pheno_data, eset, limma_parameters,
   lmfit <- limma::lmFit(eset, design)
   # We create a block if necessary.
   if (has_block) {
-    corfit       <- limma::duplicateCorrelation(eset, design, block = pheno_data[[block_key]])
-    lmfit_gender <- limma::lmFit(eset, design, block = pheno_data[[block_key]],
+    corfit <- limma::duplicateCorrelation(eset, design, block = pheno_data[[block_key]])
+    lmfit  <- limma::lmFit(eset, design, block = pheno_data[[block_key]],
                                  correlation = corfit$consensus)
   }
   fit <- limma::contrasts.fit(lmfit, contrast)