Fix bug no default min_cells some functions

DESCRIPTION

@@ -63,4 +63,4 @@ VignetteBuilder:
   knitr
 Remotes: 
   github::saeyslab/nichenetr
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1

R/condition_specific_celltypes.R

@@ -23,7 +23,7 @@
 #' batches = NA
 #' contrasts_oi = c("'High-Low','Low-High'")
 #' contrast_tbl = tibble(contrast = c("High-Low","Low-High"), group = c("High","Low"))
-#' 
+#' min_cells = 10
 #' metadata_abundance = SummarizedExperiment::colData(sce)[,c(sample_id, group_id, celltype_id)] 
 #' colnames(metadata_abundance) =c("sample_id", "group_id", "celltype_id")
 #' abundance_data = metadata_abundance %>% tibble::as_tibble() %>% dplyr::group_by(sample_id , celltype_id) %>% dplyr::count() %>% dplyr::inner_join(metadata_abundance %>% tibble::as_tibble() %>% dplyr::distinct(sample_id , group_id ))
@@ -251,7 +251,7 @@ generate_prioritization_tables_condition_specific_celltypes_sender = function(se
 #' batches = NA
 #' contrasts_oi = c("'High-Low','Low-High'")
 #' contrast_tbl = tibble(contrast = c("High-Low","Low-High"), group = c("High","Low"))
-#' 
+#' min_cells = 10
 #' metadata_abundance = SummarizedExperiment::colData(sce)[,c(sample_id, group_id, celltype_id)] 
 #' colnames(metadata_abundance) =c("sample_id", "group_id", "celltype_id")
 #' abundance_data = metadata_abundance %>% tibble::as_tibble() %>% dplyr::group_by(sample_id , celltype_id) %>% dplyr::count() %>% dplyr::inner_join(metadata_abundance %>% tibble::as_tibble() %>% dplyr::distinct(sample_id , group_id ))
@@ -482,7 +482,7 @@ generate_prioritization_tables_condition_specific_celltypes_receiver = function(
 #' batches = NA
 #' contrasts_oi = c("'High-Low','Low-High'")
 #' contrast_tbl = tibble(contrast = c("High-Low","Low-High"), group = c("High","Low"))
-#' 
+#' min_cells = 10
 #' metadata_abundance = SummarizedExperiment::colData(sce)[,c(sample_id, group_id, celltype_id)] 
 #' colnames(metadata_abundance) =c("sample_id", "group_id", "celltype_id")
 #' abundance_data = metadata_abundance %>% tibble::as_tibble() %>% dplyr::group_by(sample_id , celltype_id) %>% dplyr::count() %>% dplyr::inner_join(metadata_abundance %>% tibble::as_tibble() %>% dplyr::distinct(sample_id , group_id ))
@@ -649,7 +649,7 @@ prioritize_condition_specific_sender <- function(
 #' batches = NA
 #' contrasts_oi = c("'High-Low','Low-High'")
 #' contrast_tbl = tibble(contrast = c("High-Low","Low-High"), group = c("High","Low"))
-#' 
+#' min_cells = 10
 #' metadata_abundance = SummarizedExperiment::colData(sce)[,c(sample_id, group_id, celltype_id)] 
 #' colnames(metadata_abundance) =c("sample_id", "group_id", "celltype_id")
 #' abundance_data = metadata_abundance %>% tibble::as_tibble() %>% dplyr::group_by(sample_id , celltype_id) %>% dplyr::count() %>% dplyr::inner_join(metadata_abundance %>% tibble::as_tibble() %>% dplyr::distinct(sample_id , group_id ))

R/expression_processing.R

@@ -786,6 +786,7 @@ process_info_to_ic = function(info_object, ic_type = "sender", lr_network){
 #' sample_id = "tumor"
 #' group_id = "pEMT"
 #' celltype_id = "celltype"
+#' min_cells = 10
 #' metadata_abundance = SummarizedExperiment::colData(sce)[,c(sample_id, group_id, celltype_id)]
 #' colnames(metadata_abundance) =c("sample_id", "group_id", "celltype_id")
 #' abundance_data = metadata_abundance %>% tibble::as_tibble() %>% dplyr::group_by(sample_id , celltype_id) %>% dplyr::count() %>% dplyr::inner_join(metadata_abundance %>% dplyr::distinct(sample_id , group_id ))

R/lr_target_correlation.R

@@ -32,7 +32,7 @@
 #' batches = NA
 #' contrasts_oi = c("'High-Low','Low-High'")
 #' contrast_tbl = tibble(contrast = c("High-Low","Low-High"), group = c("High","Low"))
-#' 
+#' min_cells = 10
 #' metadata_abundance = SummarizedExperiment::colData(sce)[,c(sample_id, group_id, celltype_id)] 
 #' colnames(metadata_abundance) =c("sample_id", "group_id", "celltype_id")
 #' abundance_data = metadata_abundance %>% tibble::as_tibble() %>% dplyr::group_by(sample_id , celltype_id) %>% dplyr::count() %>% dplyr::inner_join(metadata_abundance %>% tibble::as_tibble() %>% dplyr::distinct(sample_id , group_id ))

R/pipeline_wrappers.R

@@ -1,7 +1,7 @@
 #' @title get_abundance_info
 #'
 #' @description \code{get_abundance_info} Visualize cell type abundances.
-#' @usage get_abundance_info(sce, sample_id, group_id, celltype_id, min_cells, senders_oi, receivers_oi, batches = NA)
+#' @usage get_abundance_info(sce, sample_id, group_id, celltype_id, min_cells = 10, senders_oi, receivers_oi, batches = NA)
 #'
 #' @inheritParams multi_nichenet_analysis
 #' @inheritParams combine_sender_receiver_info_ic
@@ -27,7 +27,7 @@
 #'
 #' @export
 #'
-get_abundance_info = function(sce, sample_id, group_id, celltype_id, min_cells, senders_oi, receivers_oi, batches = NA){
+get_abundance_info = function(sce, sample_id, group_id, celltype_id, min_cells = 10, senders_oi, receivers_oi, batches = NA){
 
   requireNamespace("dplyr")
   requireNamespace("ggplot2")
@@ -211,7 +211,7 @@ get_abundance_info = function(sce, sample_id, group_id, celltype_id, min_cells,
 #' @title process_abundance_expression_info
 #'
 #' @description \code{process_abundance_expression_info} Visualize cell type abundances. Calculate the average and fraction of expression of each gene per sample and per group. Calculate relative abundances of cell types as well. Under the hood, the following functions are used: `get_avg_frac_exprs_abund`, `process_info_to_ic`, `combine_sender_receiver_info_ic`
-#' @usage process_abundance_expression_info(sce, sample_id, group_id, celltype_id, min_cells, senders_oi, receivers_oi, lr_network, batches = NA, frq_list, abundance_info)
+#' @usage process_abundance_expression_info(sce, sample_id, group_id, celltype_id, min_cells = 10, senders_oi, receivers_oi, lr_network, batches = NA, frq_list, abundance_info)
 #'
 #' @inheritParams multi_nichenet_analysis
 #' @inheritParams combine_sender_receiver_info_ic
@@ -243,7 +243,7 @@ get_abundance_info = function(sce, sample_id, group_id, celltype_id, min_cells,
 #'
 #' @export
 #'
-process_abundance_expression_info = function(sce, sample_id, group_id, celltype_id, min_cells, senders_oi, receivers_oi, lr_network, batches = NA, frq_list, abundance_info){
+process_abundance_expression_info = function(sce, sample_id, group_id, celltype_id, min_cells = 10, senders_oi, receivers_oi, lr_network, batches = NA, frq_list, abundance_info){
 
   requireNamespace("dplyr")
   requireNamespace("ggplot2")

R/prioritization.R

@@ -37,7 +37,7 @@ scale_quantile_adapted = function(x, outlier_cutoff = 0){
 #' batches = NA
 #' contrasts_oi = c("'High-Low','Low-High'")
 #' contrast_tbl = tibble(contrast = c("High-Low","Low-High"), group = c("High","Low"))
-#' 
+#' min_cells = 10
 #' metadata_abundance = SummarizedExperiment::colData(sce)[,c(sample_id, group_id, celltype_id)] 
 #' colnames(metadata_abundance) =c("sample_id", "group_id", "celltype_id")
 #' abundance_data = metadata_abundance %>% tibble::as_tibble() %>% dplyr::group_by(sample_id , celltype_id) %>% dplyr::count() %>% dplyr::inner_join(metadata_abundance %>% tibble::as_tibble() %>% dplyr::distinct(sample_id , group_id ))
@@ -273,7 +273,7 @@ generate_prioritization_tables = function(sender_receiver_info, sender_receiver_
 #' batches = NA
 #' contrasts_oi = c("'High-Low','Low-High'")
 #' contrast_tbl = tibble(contrast = c("High-Low","Low-High"), group = c("High","Low"))
-#' 
+#' min_cells = 10
 #' metadata_abundance = SummarizedExperiment::colData(sce)[,c(sample_id, group_id, celltype_id)] 
 #' colnames(metadata_abundance) =c("sample_id", "group_id", "celltype_id")
 #' abundance_data = metadata_abundance %>% tibble::as_tibble() %>% dplyr::group_by(sample_id , celltype_id) %>% dplyr::count() %>% dplyr::inner_join(metadata_abundance %>% tibble::as_tibble() %>% dplyr::distinct(sample_id , group_id ))
@@ -381,7 +381,7 @@ get_top_n_lr_pairs = function(prioritization_tables, top_n, groups_oi = NULL, se
 #' batches = NA
 #' contrasts_oi = c("'High-Low','Low-High'")
 #' contrast_tbl = tibble(contrast = c("High-Low","Low-High"), group = c("High","Low"))
-#' 
+#' min_cells = 10
 #' metadata_abundance = SummarizedExperiment::colData(sce)[,c(sample_id, group_id, celltype_id)] 
 #' colnames(metadata_abundance) =c("sample_id", "group_id", "celltype_id")
 #' abundance_data = metadata_abundance %>% tibble::as_tibble() %>% dplyr::group_by(sample_id , celltype_id) %>% dplyr::count() %>% dplyr::inner_join(metadata_abundance %>% tibble::as_tibble() %>% dplyr::distinct(sample_id , group_id ))
@@ -672,7 +672,7 @@ generate_prioritization_tables_tests = function(sender_receiver_info, sender_rec
 #' batches = NA
 #' contrasts_oi = c("'High-Low','Low-High'")
 #' contrast_tbl = tibble(contrast = c("High-Low","Low-High"), group = c("High","Low"))
-#' 
+#' min_cells = 10
 #' metadata_abundance = SummarizedExperiment::colData(sce)[,c(sample_id, group_id, celltype_id)] 
 #' colnames(metadata_abundance) =c("sample_id", "group_id", "celltype_id")
 #' abundance_data = metadata_abundance %>% tibble::as_tibble() %>% dplyr::group_by(sample_id , celltype_id) %>% dplyr::count() %>% dplyr::inner_join(metadata_abundance %>% tibble::as_tibble() %>% dplyr::distinct(sample_id , group_id ))