Fix github check notes

susansjy22 · Apr 4, 2024 · e2c769d · e2c769d
1 parent 272129c
commit e2c769d
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Showing 9 changed files with 146 additions and 37 deletions.
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -61,7 +61,9 @@ Imports:
   lifecycle,
   SingleCellExperiment,
   knitr,
-  digest
+  digest, 
+  cowplot, 
+  igraph
 Encoding: UTF-8
 LazyData: true
 RoxygenNote: 7.3.1

diff --git a/NAMESPACE b/NAMESPACE
@@ -4,7 +4,6 @@ export(add_RNA_assay)
 export(alive_identification)
 export(annotation_consensus)
 export(annotation_label_transfer)
-export(calc_UMAP)
 export(cell_cycle_scoring)
 export(create_pseudobulk)
 export(doublet_identification)
@@ -24,7 +23,6 @@ export(run_targets_pipeline)
 export(seurat_to_ligand_receptor_count)
 export(test_differential_abundance_hpc)
 import(scDblFinder)
-import(sctransform)
 import(targets)
 import(tidySingleCellExperiment)
 import(tidySummarizedExperiment)
@@ -43,6 +41,7 @@ importFrom(CellChat,projectData)
 importFrom(CellChat,scPalette)
 importFrom(CellChat,searchPair)
 importFrom(CellChat,setIdent)
+importFrom(CellChat,subsetCommunication)
 importFrom(CellChat,subsetDB)
 importFrom(CellChat,subsetData)
 importFrom(CellChat,triMean)
@@ -63,6 +62,7 @@ importFrom(Seurat,GetAssayData)
 importFrom(Seurat,NormalizeData)
 importFrom(Seurat,PercentageFeatureSet)
 importFrom(Seurat,RunPCA)
+importFrom(Seurat,SCTransform)
 importFrom(Seurat,ScaleData)
 importFrom(Seurat,VariableFeatures)
 importFrom(Seurat,as.SingleCellExperiment)
@@ -76,8 +76,11 @@ importFrom(cowplot,as_grob)
 importFrom(crew,crew_controller_local)
 importFrom(digest,digest)
 importFrom(dplyr,"%>%")
+importFrom(dplyr,add_count)
+importFrom(dplyr,bind_rows)
 importFrom(dplyr,case_when)
 importFrom(dplyr,count)
+importFrom(dplyr,distinct)
 importFrom(dplyr,filter)
 importFrom(dplyr,if_else)
 importFrom(dplyr,left_join)
@@ -89,14 +92,19 @@ importFrom(dplyr,select)
 importFrom(dplyr,with_groups)
 importFrom(edgeR,estimateDisp)
 importFrom(glue,glue)
+importFrom(grid,grid.grab)
+importFrom(gridGraphics,grid.echo)
 importFrom(igraph,graph_from_adjacency_matrix)
 importFrom(igraph,layout_)
 importFrom(magrittr,extract2)
 importFrom(patchwork,wrap_elements)
 importFrom(purrr,map)
 importFrom(purrr,map2)
 importFrom(purrr,map2_dbl)
+importFrom(purrr,map_chr)
+importFrom(purrr,map_int)
 importFrom(purrr,when)
+importFrom(readr,read_csv)
 importFrom(readr,write_lines)
 importFrom(rlang,enquo)
 importFrom(rlang,is_symbolic)
@@ -118,6 +126,7 @@ importFrom(targets,tar_option_set)
 importFrom(targets,tar_script)
 importFrom(tibble,as_tibble)
 importFrom(tibble,enframe)
+importFrom(tibble,rowid_to_column)
 importFrom(tibble,tibble)
 importFrom(tidybulk,as_SummarizedExperiment)
 importFrom(tidyr,gather)

diff --git a/R/CellChat.R b/R/CellChat.R
@@ -624,7 +624,17 @@ draw_cellchat_circle_plot = function (net, color.use = NULL, title.name = NULL,
 
 }
 
+#' @importFrom dplyr distinct
+#' 
 select_genes_for_circle_plot = function(x, pathway){
+  # Fix GChecks 
+  CellChatDB.human <- NULL
+  pathway_name <- NULL
+  ligand <- NULL
+  . <- NULL
+  receptor <- NULL
+
+
   paste(
     c(
       x@data.signaling[rownames(x@data.signaling) %in% (CellChatDB.human$interaction %>% filter(pathway_name == pathway) %>% distinct(ligand) %>% pull(1)),, drop=F] %>% rowSums() %>% .[(.)>100] %>% names(),
@@ -635,6 +645,8 @@ select_genes_for_circle_plot = function(x, pathway){
 
 }
 
+#' @importFrom CellChat subsetCommunication
+#' 
 get_table_for_cell_vs_axis_bubble_plot = function (object, sources.use = NULL, targets.use = NULL, signaling = NULL,
                                                    pairLR.use = NULL, color.heatmap = c("Spectral", "viridis"),
                                                    n.colors = 10, direction = -1, thresh = 0.05, comparison = NULL,
@@ -645,6 +657,9 @@ get_table_for_cell_vs_axis_bubble_plot = function (object, sources.use = NULL, t
                                                    show.legend = TRUE, grid.on = TRUE, color.grid = "grey90",
                                                    angle.x = 90, vjust.x = NULL, hjust.x = NULL, return.data = FALSE)
 {
+
+  # Fix GChecks 
+  prob.original = NULL 
 
   # cells.level <- levels(object@idents)
   # source.use.numerical = which(cells.level == source.use)
@@ -688,7 +703,7 @@ get_table_for_cell_vs_axis_bubble_plot = function (object, sources.use = NULL, t
     # TRY CATCH
     df.net <- 	tryCatch(
       expr = {
-        subsetCommunication(object, slot.name = "net",
+        CellChat::subsetCommunication(object, slot.name = "net",
                             sources.use = sources.use, targets.use = targets.use,
                             signaling = signaling, pairLR.use = pairLR.use,
                             thresh = thresh)
@@ -751,7 +766,7 @@ get_table_for_cell_vs_axis_bubble_plot = function (object, sources.use = NULL, t
   }
   else {
     dataset.name <- names(object@net)
-    df.net.all <- subsetCommunication(object, slot.name = "net",
+    df.net.all <- CellChat::subsetCommunication(object, slot.name = "net",
                                       sources.use = sources.use, targets.use = targets.use,
                                       signaling = signaling, pairLR.use = pairLR.use,
                                       thresh = thresh)
@@ -900,6 +915,9 @@ get_table_for_cell_vs_axis_bubble_plot = function (object, sources.use = NULL, t
   df
 }
 
+#' @importFrom gridGraphics grid.echo
+#' @importFrom grid grid.grab
+#' 
 grab_grob <- function(){
   grid.echo()
   grid.grab()

diff --git a/R/execute_pipeline.R b/R/execute_pipeline.R
@@ -33,6 +33,37 @@ run_targets_pipeline <- function(
     cell_type_annotation_column = "Cell_type_in_each_tissue"
 ){
 
+  # Fix GCHECKS 
+  read_file <- NULL
+  reference_file <- NULL
+  tissue_file <- NULL
+  filtered_file <- NULL
+  sample_column_file <- NULL
+  cell_type_annotation_column_file <- NULL
+  reference_label_coarse <- NULL
+  reference_label_fine <- NULL
+  input_read <- NULL
+  unique_tissues <- NULL
+  reference_read <- NULL
+  empty_droplets_tbl <- NULL
+  cell_cycle_score_tbl <- NULL
+  annotation_label_transfer_tbl <- NULL
+  alive_identification_tbl <- NULL
+  doublet_identification_tbl <- NULL
+  non_batch_variation_removal_S <- NULL
+  preprocessing_output_S <- NULL
+  create_pseudobulk_sample <- NULL
+  sampleName <- NULL
+  cellAnno <- NULL
+  pseudobulk_merge_all_samples <- NULL
+  calc_UMAP_dbl_report <- NULL
+  variable_gene_list <- NULL
+  tar_render <- NULL
+  empty_droplets_report <- NULL
+  doublet_identification_report <- NULL
+  Technical_variation_report <- NULL
+  pseudobulk_processing_report <- NULL
+
   sample_column = enquo(sample_column)
   # cell_type_annotation_column = enquo(cell_type_annotation_column)
 

diff --git a/R/functions.R b/R/functions.R
@@ -182,7 +182,7 @@ annotation_label_transfer <- function(input_read_RNA_assay,
       input_read_RNA_assay |>
 
       # Normalise RNA - not informed by smartly selected variable genes
-      SCTransform(assay=assay) |>
+      Seurat::SCTransform(assay=assay) |>
       ScaleData(assay = "SCT") |>
       RunPCA(assay = "SCT")
 
@@ -539,14 +539,22 @@ cell_cycle_scoring <- function(input_read_RNA_assay,
 #'
 #' @importFrom dplyr left_join
 #' @importFrom dplyr filter
-#' @importFrom Seurat NormalizeData
-#' @import sctransform
+#' @importFrom Seurat NormalizeData SCTransform
 #' @export
 non_batch_variation_removal <- function(input_read_RNA_assay, 
                                         empty_droplets_tbl, 
                                         alive_identification_tbl, 
                                         cell_cycle_score_tbl,
                                         assay = NULL){
+  #Fix GChecks 
+  empty_droplet = NULL 
+  .cell <- NULL 
+  subsets_Ribo_percent <- NULL  
+  subsets_Mito_percent <- NULL  
+  G2M.Score = NULL 
+
+  # Your code for non_batch_variation_removal function here
+
 
   # Get assay
   if(is.null(assay)) assay = input_read_RNA_assay@assays |> names() |> extract2(1)
@@ -626,6 +634,16 @@ preprocessing_output <- function(tissue,
                                  cell_cycle_score_tbl, 
                                  annotation_label_transfer_tbl, 
                                  doublet_identification_tbl){
+  #Fix GCHECKS 
+  .cell <- NULL
+  alive <- NULL
+  subsets_Mito_percent <- NULL
+  subsets_Ribo_percent <- NULL
+  high_mitochondrion <- NULL
+  high_ribosome <- NULL
+  scDblFinder.class <- NULL
+  predicted.celltype.l2 <- NULL
+
   processed_data <- non_batch_variation_removal_S |>
     # Filter dead cells
     left_join(
@@ -728,6 +746,8 @@ create_pseudobulk <- function(preprocessing_output_S , assays ,x ,...) {
 #' @export
 #' 
 pseudobulk_merge <- function(create_pseudobulk_sample, assays, x , ...) {
+  # Fix GCHECKS 
+  . = NULL 
   #browser()
   x = enquo(x)
   # Select only common columns
@@ -803,9 +823,20 @@ pseudobulk_merge <- function(create_pseudobulk_sample, assays, x , ...) {
 #' @importFrom purrr map2
 #' @importFrom purrr map
 #' @importFrom purrr map2_dbl
+#' @importFrom dplyr distinct add_count
 #' @export
 seurat_to_ligand_receptor_count = function(counts, .cell_group, assay, sample_for_plotting = ""){
 
+  #Fix GChecks 
+  cell_type_harmonised <- NULL
+  n_cells <- NULL
+  DB <- NULL
+  cell_vs_all_cells_per_pathway <- NULL
+  gene <- NULL
+
+  # Your code for seurat_to_ligand_receptor_count function here
+
+
   .cell_group = enquo(.cell_group)
 
   # If only one cell, return empty
@@ -1135,6 +1166,7 @@ map_split_se_by_number_of_genes = function(se_df, .col, chunk_size = 100){
 
 #' @importFrom digest digest
 #' @importFrom rlang enquo
+#' @importFrom purrr map_chr
 #'
 #' @export
 map_split_sce_by_gene = function(sce_df, .col, how_many_chunks_base = 10, max_cells_before_split = 4763){
@@ -1155,31 +1187,10 @@ map_split_sce_by_gene = function(sce_df, .col, how_many_chunks_base = 10, max_ce
       }
     )) |>
     unnest(!!.col) |>
-    mutate(sce_md5 = map_chr(!!.col, digest))
+    mutate(sce_md5 = purrr::map_chr(!!.col, digest))
 }
 
 
-#' Calculate UMAP
-#' Scales the input data, performing PCA, clustering the cells and running UMAP and constructs a tibble in preparation for plotting in 
-#' the doublet identification report 
-#' 
-#' @param input_seurat Single Seurat object (Input data)
-#' 
-#' @export
-calc_UMAP <- function(input_seurat){
-  find_var_genes <- FindVariableFeatures(input_seurat)
-  var_genes<- find_var_genes@assays$originalexp@var.features
-
-  x<- ScaleData(input_seurat) |>
-    # Calculate UMAP of clusters
-    RunPCA(features = var_genes) |>
-    FindNeighbors(dims = 1:30) |>
-    FindClusters(resolution = 0.5) |>
-    RunUMAP(dims = 1:30, spread    = 0.5,min.dist  = 0.01, n.neighbors = 10L) |> 
-    as_tibble()
-  return(x)
-}
-
 #' Get unique tissues 
 #' Obtain unique tissues/ samples from input dataset 
 #' 

diff --git a/R/targets_functions.R b/R/targets_functions.R
@@ -25,6 +25,8 @@
 #' @import targets
 #' @importFrom rlang quo_is_symbolic
 #' @importFrom SummarizedExperiment assays
+#' @importFrom tidyseurat quo_names
+#' @importFrom tibble rowid_to_column
 #' 
 #' @export
 map2_test_differential_abundance_hpc = function(
@@ -39,6 +41,22 @@ map2_test_differential_abundance_hpc = function(
     ...
   ){
 
+  #Fix GChecks 
+  file_data = NULL 
+  file_formula = NULL
+  abundance = NULL
+  number_of_workers = NULL
+  number_of_datasets = NULL
+  pseudobulk_df_tissue = NULL
+  name = NULL 
+  pseudobulk_df_tissue_dispersion = NULL 
+  pseudobulk_df_tissue_split_by_gene = NULL
+  pseudobulk_df_tissue_split_by_gene_grouped = NULL
+  se_md5 = NULL
+  estimates_chunk = NULL
+  my_group = NULL
+  assay_name = NULL 
+
   .abundance = enquo(.abundance)
 
   if(quo_is_symbolic(.abundance)) .abundance = quo_names(.abundance)