Merge branch 'suite_dev' of https://github.com/drieslab/Giotto into s…

…uite_dev

R/cell_segmentation.R

@@ -51,10 +51,10 @@ doCellSegmentation <- function(raster_img,
     # sliding window
     start_x <- 0
     end_x <- start_x + tile_dim
-    for (i in 1:nxwindow) {
+    for (i in seq_len(nxwindow)) {
         start_y <- 0
         end_y <- start_y + tile_dim
-        for (j in 1:nywindow) {
+        for (j in seq_len(nywindow)) {
             ext_crop <- terra::ext(c(start_x, end_x, start_y, end_y))
             img_crop <- terra::crop(raster_img, ext_crop, snap = "in")
             img_crop_rescaled <- terra::aggregate(img_crop, reduce_resolution)

R/clustering.R

@@ -113,7 +113,7 @@ doLeidenCluster <- function(gobject,
     if (isTRUE(set_seed)) {
         seed_number <- as.integer(seed_number)
     } else {
-        seed_number <- as.integer(sample(x = 1:10000, size = 1))
+        seed_number <- as.integer(sample(x = seq_len(10000), size = 1))
     }
 
     ## extract NN network
@@ -551,7 +551,7 @@ doGiottoClustree <- function(gobject,
     if (isTRUE(set_seed)) {
         seed_number <- as.integer(seed_number)
     } else {
-        seed_number <- as.integer(sample(x = 1:10000, size = 1))
+        seed_number <- as.integer(sample(x = seq_len(10000), size = 1))
     }
 
     network_edge_dt <- data.table::as.data.table(igraph::as_data_frame(
@@ -1102,9 +1102,9 @@ doSNNCluster <- function(gobject,
     cell_id_numeric <- unique(x = c(igraph_DT$from, igraph_DT$to))
     names(cell_id_numeric) <- seq_along(cell_id_numeric)
     igraph_DT[, from_T := as.numeric(names(cell_id_numeric[
-        cell_id_numeric == from])), by = 1:nrow(igraph_DT)]
+        cell_id_numeric == from])), by = seq_len(nrow(igraph_DT))]
     igraph_DT[, to_T := as.numeric(names(cell_id_numeric[
-        cell_id_numeric == to])), by = 1:nrow(igraph_DT)]
+        cell_id_numeric == to])), by = seq_len(nrow(igraph_DT))]
     temp_igraph_DT <- igraph_DT[, .(from_T, to_T, weight, distance)]
     data.table::setnames(
         temp_igraph_DT, old = c("from_T", "to_T"), new = c("from", "to"))
@@ -1116,7 +1116,7 @@ doSNNCluster <- function(gobject,
     )
 
     ident_clusters_DT <- data.table::data.table(
-        "cell_ID" = cell_id_numeric[1:nrow(kNN_object$dist)],
+        "cell_ID" = cell_id_numeric[seq_len(nrow(kNN_object$dist))],
         "name" = sNN_clusters$cluster)
     data.table::setnames(ident_clusters_DT, "name", name)
 
@@ -1247,7 +1247,7 @@ doKmeans <- function(gobject,
         )
 
         dimensions_to_use <- dimensions_to_use[
-            dimensions_to_use %in% 1:ncol(dim_coord[])]
+            dimensions_to_use %in% seq_len(ncol(dim_coord[]))]
         matrix_to_use <- dim_coord[][, dimensions_to_use]
     } else {
         values <- match.arg(
@@ -1470,7 +1470,7 @@ doHclust <- function(gobject,
         )
 
         dimensions_to_use <- dimensions_to_use[
-            dimensions_to_use %in% 1:ncol(dim_coord)]
+            dimensions_to_use %in% seq_len(ncol(dim_coord))]
         matrix_to_use <- dim_coord[, dimensions_to_use]
     } else {
         ## using original matrix ##
@@ -2813,7 +2813,8 @@ getClusterSimilarity <- function(gobject,
     cor_table[, c("group1", "group2") := list(
         as.character(group1), as.character(group2))]
     cor_table[, unified_group := paste(
-        sort(c(group1, group2)), collapse = "--"), by = 1:nrow(cor_table)]
+        sort(c(group1, group2)), collapse = "--"), 
+        by = seq_len(nrow(cor_table))]
     cor_table <- cor_table[!duplicated(cor_table[, .(value, unified_group)])]
 
     cor_table <- merge(
@@ -2933,7 +2934,7 @@ mergeClusters <- function(gobject,
     ## get list of correlated groups
     finallist <- list()
     start_i <- 1
-    for (row in 1:nrow(filter_set)) {
+    for (row in seq_len(nrow(filter_set))) {
         first_clus <- filter_set[row][["group1"]]
         second_clus <- filter_set[row][["group2"]]
 
@@ -2971,7 +2972,7 @@ mergeClusters <- function(gobject,
         as.character(get(cluster_column)) %in% finalvec,
         names(finalvec[finalvec == as.character(get(cluster_column))]),
         as.character(get(cluster_column))
-    ), by = 1:nrow(metadata)]
+    ), by = seq_len(nrow(metadata))]
 
 
 
@@ -3205,7 +3206,7 @@ getDendrogramSplits <- function(gobject,
     splitDT <- data.table::as.data.table(t_flex(
         data.table::as.data.table(splitList[[2]])))
     colnames(splitDT) <- c("node_h", "tree_1", "tree_2")
-    splitDT[, nodeID := paste0("node_", 1:.N)]
+    splitDT[, nodeID := paste0("node_", seq_len(.N))]
 
     return(splitDT)
 }
@@ -3317,7 +3318,7 @@ doClusterProjection <- function(target_gobject,
 
     dim_coord <- dim_obj[]
     dimensions_to_use <- dimensions_to_use[
-        dimensions_to_use %in% 1:ncol(dim_coord)]
+        dimensions_to_use %in% seq_len(ncol(dim_coord))]
     matrix_to_use <- dim_coord[, dimensions_to_use]
 
     ## create the training and testset from the matrix

R/cross_section.R

@@ -520,7 +520,7 @@ createCrossSection <- function(gobject,
 
     spatial_locations <- as.matrix(spatial_locations)
     rownames(spatial_locations) <- cell_IDs
-    cell_ID_vec <- c(1:nrow(spatial_locations))
+    cell_ID_vec <- seq_len(nrow(spatial_locations))
     names(cell_ID_vec) <- rownames(spatial_locations)
 
     # generate section plane equation
@@ -542,7 +542,7 @@ createCrossSection <- function(gobject,
             message("either point or norm vector was not provided.")
         } else {
             plane_equation <- c()
-            plane_equation[1:3] <- normVector
+            plane_equation[seq_len(3)] <- normVector
             plane_equation[4] <- -point1 %*% normVector
         }
     } else if (method == "point and two plane vectors") {
@@ -551,7 +551,7 @@ createCrossSection <- function(gobject,
                     provided.")
         } else {
             normVector <- crossprod(planeVector1, planeVector2)
-            plane_equation[1:3] <- normVector
+            plane_equation[seq_len(3)] <- normVector
             plane_equation[4] <- -point1 %*% normVector
         }
     } else if (method == "3 points") {
@@ -561,7 +561,7 @@ createCrossSection <- function(gobject,
             planeVector1 <- point2 - point1
             planeVector2 <- point3 - point1
             normVector <- crossprod(planeVector1, planeVector2)
-            plane_equation[1:3] <- normVector
+            plane_equation[seq_len(3)] <- normVector
             plane_equation[4] <- -point1 %*% normVector
         }
     }
@@ -584,7 +584,7 @@ createCrossSection <- function(gobject,
         spatial_locations, as.matrix(rep(1, dim(spatial_locations)[1])))
     norm_vec <- function(x) sqrt(sum(x^2))
     distance_to_plane_vector <- abs(spatial_locations_mat %*% as.matrix(
-        plane_equation) / norm_vec(plane_equation[1:3]))
+        plane_equation) / norm_vec(plane_equation[seq_len(3)]))
 
     # select cells within section ###
     cell_subset <- distance_to_plane_vector <= max_distance_to_section_plane
@@ -604,7 +604,7 @@ createCrossSection <- function(gobject,
     cell_subset_projection_locations <- t(apply(
         cell_subset_spatial_locations, 1, 
         function(x) projection_fun(x, plane_point = plane_point, 
-                                plane_norm = plane_equation[1:3])))
+                                plane_norm = plane_equation[seq_len(3)])))
 
     # get the local coordinates of selected cells on the section plane
     cell_subset_projection_PCA <- stats::prcomp(

R/differential_expression.R

@@ -577,7 +577,7 @@ findGiniMarkers <- function(gobject,
     aggr_sc[, comb_score := (expression_gini * expression_rank) * (
         detection_gini * detection_rank)]
     setorder(aggr_sc, cluster, -comb_score)
-    aggr_sc[, comb_rank := 1:.N, by = cluster]
+    aggr_sc[, comb_rank := seq_len(.N), by = cluster]
 
     top_feats_scores <- aggr_sc[comb_rank <= min_feats | (
         expression_rank <= rank_score & detection_rank <= rank_score)]