src/03_ERCC_analysis_function.R: linter changes

src/03_ERCC_analysis_function.R

@@ -56,7 +56,9 @@ get_count_matrix <- function(directory, condition_pattern, selection_vec = "",
     dds <- DESeq(dds_input) # nolint
     cts <- counts(dds, norm = F) # nolint
     if (filtering_transcript) {
-        coding_gene <- read_tsv("results/coding_genes/coding_gene.txt")$gene_id
+        coding_gene <- read_tsv(
+            "results/coding_genes/coding_gene.txt"
+        )$gene_id # nolint
         cts <- cts[which(rownames(cts) %in% coding_gene), ]
     }
     if (read_number_filter > 0) {
@@ -64,7 +66,7 @@ get_count_matrix <- function(directory, condition_pattern, selection_vec = "",
         sample_size <- cts %>%
             as_tibble() %>%
             summarise_if(is.numeric, sum)
-        cts <- cts[, colnames(cts)[(sample_size > 5e6)]]
+        cts <- cts[, colnames(cts)[(sample_size > read_number_filter)]]
     }
     return(cts)
 }
@@ -89,15 +91,19 @@ get_relative_count <- function(data_count, ercc_count) {
         by = c("name", "samples"),
         suffix = c("", "_ercc")
     )
-    res <- res %>% mutate(relative_count = counts_ercc / counts)
+    res <- res %>% mutate(
+        relative_count = counts_ercc / counts
+    ) # nolint
     return(res)
 }
 
 #' get ERCC correlation plot for one sample
 #'
 #' @param ercc_raw a dataframe containing raw ERCC count
-#' @param col The selected sample for which we want to display the correlation figure
-#' @param size_list a named vector containing the number of reads  in coding gene for each samples
+#' @param col The selected sample for which we want to display
+#' the correlation figure
+#' @param size_list a named vector containing the number of reads
+#' in coding gene for each samples
 create_ercc_correlation_plots <- function(ercc_raw, col, size_list) {
     selected <- which(ercc_raw[[col]] > 0 & ercc_raw$concentration > 0)
     size <- size_list[col]
@@ -105,9 +111,15 @@ create_ercc_correlation_plots <- function(ercc_raw, col, size_list) {
     colc <- ercc_raw$concentration[selected]
     cor_val <- cor.test(log2(colc), log2(coli))$estimate
     ercc <- nrow(ercc_raw)
-    p <- ggplot(ercc_raw, mapping = aes(x = log2(concentration), y = log2(!!as.symbol(col)))) +
+    p <- ggplot(ercc_raw, mapping = aes(
+        x = log2(concentration),
+        y = log2(!!as.symbol(col))
+    )) +
         geom_point() +
-        ggtitle(paste("R = ", round(cor_val, 3), "- n =", ercc, "- s = ", round(size / 1e6, 1), "M")) +
+        ggtitle(paste(
+            "R = ", round(cor_val, 3), "- n =", ercc,
+            "- s = ", round(size / 1e6, 1), "M"
+        )) +
         stat_smooth(method = "lm", se = FALSE, color = "red")
     p$my_cor <- cor_val
     return(p)
@@ -116,29 +128,40 @@ create_ercc_correlation_plots <- function(ercc_raw, col, size_list) {
 #' Create ERCC correlation figure for all samples
 #'
 #' @param ercc_count_matrix The ercc count matrix
-#' @param count_threshold The average count threshold an ercc must have to be displayed in the figure
+#' @param count_threshold The average count threshold an ercc must have
+#' to be displayed in the figure
+#' @import tidyverse
 create_correlation_figures <- function(ercc_count_matrix, count_threshold) {
     ercc_raw <- ercc_count_matrix
-    ercc_raw <- as_tibble(data.frame(gene = rownames(ercc_count_matrix), ercc_raw))
-    ercc_raw <- ercc_raw %>% left_join(ercc_real_concentration, by = "gene")
+    ercc_raw <- as_tibble(data.frame(
+        gene = rownames(ercc_count_matrix), ercc_raw
+    )) # nolint
+    ercc_raw <- ercc_raw %>%
+        left_join(ercc_real_concentration, by = "gene") # nolint
     ercc_raw2 <- ercc_raw %>%
         select(-concentration) %>%
         pivot_longer(-gene, names_to = "sample", values_to = "counts") %>%
         group_by(gene) %>%
         summarise(min_count = mean(counts))
-    ercc_raw <- left_join(ercc_raw, ercc_raw2, by = "gene") %>% filter(min_count > count_threshold)
+    ercc_raw <- left_join(ercc_raw, ercc_raw2, by = "gene") %>% filter(min_count > count_threshold) # nolint
 
-    sample_size <- apply(data_count_matrix %>% as_tibble() %>% select(-gene), 2, sum)
+    sample_size <- apply(data_count_matrix %>%
+        as_tibble() %>% select(-gene), 2, sum) # nolint
     names_c <- ercc_raw %>%
         select(-gene, -concentration, -min_count) %>%
         colnames()
     sum(names(sample_size) == names_c) # 96 they are in the same order
-    my_plots <- lapply(names_c, create_ercc_correlation_plots, ercc_raw = ercc_raw, size_list = sample_size)
+    my_plots <- lapply(names_c, create_ercc_correlation_plots,
+        ercc_raw = ercc_raw, size_list = sample_size
+    ) # nolint
     correlation_sup20 <- sapply(my_plots, function(x) {
         return(x$my_cor)
     })
     dir.create("./results/ERCC_analysis")
-    pdf(paste0("./results/ERCC_analysis/correlations_ERCC-meancount>", count_threshold, "_quant_vs_concentration.pdf"), width = 12, height = 100)
+    pdf(paste0(
+        "./results/ERCC_analysis/correlations_ERCC-meancount>",
+        count_threshold, "_quant_vs_concentration.pdf"
+    ), width = 12, height = 100)
     print(do.call("grid.arrange", c(my_plots, ncol = 3)))
     dev.off()
     return(correlation_sup20)