linting infix operators

R/callback.R

@@ -1,12 +1,12 @@
-#' @title Runs a typical Seurat workflow on a Seurat object (up to 
+#' @title Runs a typical Seurat workflow on a Seurat object (up to
 #' dimensionality reduction and clustering).
 #'
 #' @description Given a Seurat object, returns a new Seurat that has been
 #' normalized, had variable features identified, scaled, had principal
 #' components computed, hadclusters identified, and had tSNE and UMAP
 #' embeddings determined.
 #'
-#' @details 
+#' @details
 #'
 #' @param seurat_obj The Seurat object that will be analyzed.
 #' @param resolution_start The starting resolution to be used for the
@@ -20,22 +20,22 @@
 #' @param assay The assay to generate knockoffs from.
 #' @param cores The number of cores to compute marker genes in parallel.
 #' @returns Returns a Seurat object where the idents have been updated with the
-#' clusters determined via the callback algorithm. 
+#' clusters determined via the callback algorithm.
 #' @param verbose Whether or not to show all logging.
 #' Latest clustering results will be stored in the object metadata under
 #' callback_clusters'. Note that 'callback_clusters' will be overwritten ever
 #' time FindClustersKC is run.
 #' @name FindClustersCallback
 #' @export
 FindClustersCallback <- function(seurat_obj,
-                            resolution_start=0.8,
-                            reduction_percentage=0.2,
-                            num_clusters_start=20,
-                            dims=1:10,
-                            algorithm="louvain", # todo implement all algos
-                            assay="RNA",
-                            cores=1,
-                            verbose=TRUE) {
+                            resolution_start = 0.8,
+                            reduction_percentage = 0.2,
+                            num_clusters_start = 20,
+                            dims = 1:10,
+                            algorithm = "louvain", # todo implement all algos
+                            assay = "RNA",
+                            cores = 1,
+                            verbose = TRUE) {
 
   # todo check function arguments for validity
 

R/seurat_workflow.R

@@ -11,7 +11,7 @@
 #' @param assay The assay to generate knockoffs from.
 #' @returns A Seurat object that contains the original variable features and an equal number of knockoff features.
 #' @name get_seurat_obj_with_knockoffs
-get_seurat_obj_with_knockoffs <- function(seurat_obj, assay="RNA") {
+get_seurat_obj_with_knockoffs <- function(seurat_obj, assay = "RNA") {
   var.features <- Seurat::VariableFeatures(seurat_obj)
   #seurat_obj_data <- as.data.frame(t(as.matrix(seurat_obj@assays$RNA@counts)))
 
@@ -47,9 +47,9 @@ get_seurat_obj_with_knockoffs <- function(seurat_obj, assay="RNA") {
 cluster_optimal_louvain_resolution_parameter <- function(seurat_obj,
                                                          original_num_clusters,
                                                          num_variable_features,
-                                                         res_start=0.1,
-                                                         res_end=3,
-                                                         res_increment=0.05) {
+                                                         res_start = 0.1,
+                                                         res_end = 3,
+                                                         res_increment = 0.05) {
 
   # todo make this more efficient
   # todo move this function into  flow so that it happens automatically and doesn't repeat computation
@@ -108,7 +108,12 @@ cluster_optimal_louvain_resolution_parameter <- function(seurat_obj,
 #' @returns A Seurat object containing the relevant analysis results.
 #' @export
 #' @name seurat_workflow
-seurat_workflow <- function(seurat_obj, num_variable_features, resolution_param=0.5, visualization_method="umap", num_dims=10, algorithm="louvain") {
+seurat_workflow <- function(seurat_obj,
+                            num_variable_features,
+                            resolution_param = 0.5,
+                            visualization_method = "umap",
+                            num_dims = 10,
+                            algorithm = "louvain") {
   seurat_obj <- Seurat::NormalizeData(seurat_obj)
 
   seurat_obj <- Seurat::FindVariableFeatures(seurat_obj, selection.method = "vst", nfeatures = num_variable_features)