replace 1: with seq() or seq_along()

R/import_fxns.R

@@ -75,7 +75,7 @@ create_rl_map_cellphonedb <- function(
   }
   # Step through the interactions and build rl connections.
   rl_map <- NULL
-  for (i in 1:nrow(interactions)) {
+  for (i in seq(nrow(interactions))) {
     inter <- interactions[i, ]
     partner_a <- inter[["partner_a"]]
     partner_b <- inter[["partner_b"]]
@@ -338,7 +338,7 @@ create_domino <- function(
   }
   # Get genes for receptors
   rl_reading <- NULL
-  for (i in 1:nrow(rl_map)) {
+  for (i in seq(nrow(rl_map))) {
     rl <- list()
     inter <- rl_map[i, ]
     p <- ifelse(inter[["type_A"]] == "R", "A", "B")
@@ -366,7 +366,7 @@ create_domino <- function(
   dom@linkages$complexes <- NULL
   if (use_complexes) {
     complex_list <- list()
-    for (i in 1:nrow(rl_reading)) {
+    for (i in seq(nrow(rl_reading))) {
       inter <- rl_reading[i, ]
       if (grepl("\\,", inter[["L.gene"]])) {
         complex_list[[inter[["L.name"]]]] <- unlist(strsplit(inter[["L.gene"]], split = "\\,"))
@@ -502,7 +502,7 @@ create_domino <- function(
   dom@misc$rec_cor <- rho
   # assess correlation among genes in the same receptor complex
   cor_list <- list()
-  for (i in 1:length(names(dom@linkages$rec_lig))) {
+  for (i in seq_along(names(dom@linkages$rec_lig))) {
     r <- names(dom@linkages$rec_lig)[i]
     if (r %in% names(dom@linkages$complexes)) {
       r_genes <- dom@linkages$complexes[[r]]
@@ -623,7 +623,7 @@ add_rl_column <- function(map, map_ref, conv, new_name) {
     not_in_ref_map <- c()
   }
   new_map <- c()
-  for (r_id in 1:nrow(map)) {
+  for (r_id in seq(nrow(map))) {
     row <- map[r_id, ]
     conv_ids <- which(conv[, 1] == row[[map_ref]])
     for (id in conv_ids) {

R/plot_fxns.R

@@ -289,7 +289,7 @@ signaling_network <- function(
   }
   # Get vert angle for labeling circos plot
   if (layout == "circle") {
-    v_angles <- 1:length(igraph::V(graph))
+    v_angles <- seq(length(igraph::V(graph)))
     v_angles <- -2 * pi * (v_angles - 1) / length(v_angles)
     igraph::V(graph)$label.degree <- v_angles
   }
@@ -465,9 +465,9 @@ gene_network <- function(dom, clust = NULL, OutgoingSignalingClust = NULL,
     l[all_ligs, 1] <- -0.75
     l[all_recs, 1] <- 0
     l[all_tfs, 1] <- 0.75
-    l[all_ligs, 2] <- (1:length(all_ligs) / mean(1:length(all_ligs)) - 1) * 2
-    l[all_recs, 2] <- (1:length(all_recs) / mean(1:length(all_recs)) - 1) * 2
-    l[all_tfs, 2] <- (1:length(all_tfs) / mean(1:length(all_tfs)) - 1) * 2
+    l[all_ligs, 2] <- (seq_along(all_ligs) / mean(seq_along(all_ligs)) - 1) * 2
+    l[all_recs, 2] <- (seq_along(all_recs) / mean(seq_along(all_recs)) - 1) * 2
+    l[all_tfs, 2] <- (seq_along(all_tfs) / mean(seq_along(all_tfs)) - 1) * 2
     rownames(l) <- c()
   } else if (layout == "random") {
     l <- igraph::layout_randomly(graph)
@@ -814,7 +814,7 @@ circos_ligand_receptor <- function(
     names(cell_colors) <- cell_idents
   }
   grid_col <- c("#FFFFFF") # hide the arc corresponding to the receptor by coloring white
-  for (i in 1:length(ligands)) {
+  for (i in seq_along(ligands)) {
     grid_col <- c(grid_col, rep(lig_colors[i], length(cell_idents)))
   }
   names(grid_col) <- c(receptor, signaling_df$origin)
@@ -955,7 +955,7 @@ plot_differential_linkages <- function(
     group_palette <- ggplot_col_gen(length(g_names))
     names(group_palette) <- g_names
   }
-  for (i in 1:length(g_names)) {
+  for (i in seq_along(g_names)) {
     g <- g_names[i]
     g_count <- paste0(g, "_count")
     g_n <- paste0(g, "_n")
@@ -1003,5 +1003,5 @@ do_norm <- function(mat, dir) {
 #' 
 ggplot_col_gen <- function(n) {
   hues <- seq(15, 375, length = n + 1)
-  return(grDevices::hcl(h = hues, l = 65, c = 100)[1:n])
+  return(grDevices::hcl(h = hues, l = 65, c = 100)[seq(n)])
 }

R/processing_fxns.R

@@ -46,12 +46,12 @@ build_domino <- function(
           fcs <- c(fcs, fc)
         }
         names(fcs) <- zeros
-        sorted <- sort(fcs, decreasing = TRUE)[1:max_tf_per_clust]
+        sorted <- sort(fcs, decreasing = TRUE)[seq(max_tf_per_clust)]
       } else {
         sorted <- ordered[which(ordered < min_tf_pval)]
       }
       if (length(sorted) > max_tf_per_clust) {
-        sorted <- sorted[1:max_tf_per_clust]
+        sorted <- sorted[seq(max_tf_per_clust)]
       }
       clust_tf[[clust]] <- names(sorted)
     }
@@ -62,7 +62,7 @@ build_domino <- function(
       ordered <- sort(dom@cor[, tf], decreasing = TRUE)
       filtered <- ordered[which(ordered > rec_tf_cor_threshold)]
       if (length(filtered) > max_rec_per_tf) {
-        top_receptors <- names(filtered)[1:max_rec_per_tf]
+        top_receptors <- names(filtered)[seq(max_rec_per_tf)]
       } else {
         top_receptors <- names(filtered)
       }
@@ -192,7 +192,7 @@ build_domino <- function(
       ordered <- sort(dom@cor[, tf], decreasing = TRUE)
       filtered <- ordered[which(ordered > rec_tf_cor_threshold)]
       if (length(filtered) > max_rec_per_tf) {
-        top_receptors <- names(filtered)[1:max_rec_per_tf]
+        top_receptors <- names(filtered)[seq(max_rec_per_tf)]
       } else {
         top_receptors <- names(filtered)
       }