Cosmetic changes to satisfy lintR.

R/preprocess_data.R

@@ -73,7 +73,8 @@ preprocess_data <- function(input_data_dir, output_data_file,
                                                clean_samples    = clean_samples,
                                                verbose          = verbose)
   } else {
-    message(paste0("[", Sys.time(), "] Platform ", platform, " not yet supported (no preprocessing done)."))
+    message(paste0("[", Sys.time(), "] Platform ", platform,
+                   " not yet supported (no preprocessing done)."))
   }
 
   # We return the created ESET.

R/preprocess_data_affymetrix_rma.R

@@ -41,7 +41,8 @@ preprocess_data_affymetrix_rma <- function(input_data_dir, output_data_file,
   # If necessary, we remove the samples that do not have clinical data.
   if (clean_samples) {
     # We load the clinical data as to get the samples to keep.
-    samples <- rownames(pData(ArrayUtils::load_clinical_data(input_data_dir, verbose = FALSE)))
+    samples <- rownames(Biobase::pData(ArrayUtils::load_clinical_data(input_data_dir,
+                                                                      verbose = FALSE)))
     # We only keep the samples with clinical data.
     eset <- eset[, samples]
   }

R/preprocess_data_agilent_limma.R

@@ -39,7 +39,7 @@ preprocess_data_agilent_limma <- function(input_data_dir, output_data_file,
                                 path       = raw_data_input_dir,
                                 green.only = TRUE,
                                 verbose    = TRUE)
-  batch_data = log2(batch$E)
+  batch_data <- log2(batch$E)
   remove(raw_data_input_dir, batch)
 
   # We run the LIMMA pre-processing method on the data.
@@ -55,7 +55,8 @@ preprocess_data_agilent_limma <- function(input_data_dir, output_data_file,
   # If necessary, we remove the samples that do not have clinical data.
   if (clean_samples) {
     # We load the clinical data as to get the samples to keep.
-    samples <- rownames(pData(ArrayUtils::load_clinical_data(input_data_dir, verbose = FALSE)))
+    samples <- rownames(Biobase::pData(ArrayUtils::load_clinical_data(input_data_dir,
+                                                                      verbose = FALSE)))
     # We only keep the samples with clinical data.
     batch_data_norm <- batch_data_norm[, samples]
   }

R/preprocess_data_illumina_beadarray.R

@@ -68,17 +68,17 @@ preprocess_data_illumina_beadarray <- function(input_data_dir, output_data_file,
 
     # Additional cleaning (after normalization - also Illumina specific).
     ids  <- as.character(Biobase::featureNames(gse_data_norm))
-    #qual <- unlist(mget(ids, illuminaHumanv3.db::illuminaHumanv3PROBEQUALITY, ifnotfound = NA))
     qual <- unlist(mget(ids, get("illuminaHumanv3PROBEQUALITY"), ifnotfound = NA))
     rem  <- qual == "No match" | qual == "Bad" | is.na(qual)
-    gse_data_filt <- Biobase::exprs(gse_data_norm[!rem,])
+    gse_data_filt <- Biobase::exprs(gse_data_norm[!rem, ])
     rm(gse_data, gse_data_norm)
   }
 
   # If necessary, we remove the samples that do not have clinical data.
   if (clean_samples) {
     # We load the clinical data as to get the samples to keep.
-    samples <- rownames(pData(ArrayUtils::load_clinical_data(input_data_dir, verbose = FALSE)))
+    samples <- rownames(Biobase::pData(ArrayUtils::load_clinical_data(input_data_dir,
+                                                                      verbose = FALSE)))
     # We only keep the samples with clinical data.
     gse_data_filt <- gse_data_filt[, samples]
   }

R/run_quality_control_on_raw_agilent.R

@@ -36,8 +36,8 @@ run_quality_control_on_raw_agilent <- function(input_data_dir,
                                 path       = raw_data_input_dir,
                                 green.only = TRUE,
                                 verbose    = TRUE)
-  batch$targets <- pheno_data
-  batch_log_data = log2(batch$E)
+  batch$targets  <- pheno_data
+  batch_log_data <- log2(batch$E)
 
   # We clean up and log information.
   rm(raw_file_list, raw_data_input_dir, pheno_data_raw)
@@ -47,7 +47,7 @@ run_quality_control_on_raw_agilent <- function(input_data_dir,
 
   # We do some kind of quality control (ourselves - no external packages).
   dir.create(output_data_dir)
-  # A- heatmap.
+  # A- sample heatmap with dendograms.
   # Compute correlations.
   cor_mat <- round(cor(batch_log_data, method = "pearson"), 2)
   # Create the heatmap.
@@ -76,7 +76,7 @@ run_quality_control_on_raw_agilent <- function(input_data_dir,
   dev.off()
   pca_gd_filename <- paste0(output_data_dir, "pca_gender.png")
   png(pca_gd_filename)
-  pca_plot_gd <- ggplot2::autoplot(pca, colour = gd_colors, size=5)
+  pca_plot_gd <- ggplot2::autoplot(pca, colour = gd_colors, size = 5)
   print(pca_plot_gd)
   dev.off()
   # We clean up.
@@ -113,7 +113,7 @@ run_quality_control_on_raw_agilent <- function(input_data_dir,
   i <- (rows - 1) * ncols + cols
   # We now plot the arrays one by one.
   for (j in seq_len(dim(batch_log_data)[2])) {
-    array_data[i]  <- batch_log_data[ ,j]
+    array_data[i]  <- batch_log_data[, j]
     array_filename <- paste0(output_data_dir, "array_", j, ".png")
     png(array_filename)
     limma::imageplot(array_data,