Merge branch 'master' into chilampoon-improve-documentation

.github/workflows/rworkflows.yml

@@ -1,7 +1,17 @@
 name: rworkflows
-on:
+'on':
   push:
+    branches:
+    - master
+    - main
+    - devel
+    - RELEASE_**
   pull_request:
+    branches:
+    - master
+    - main
+    - devel
+    - RELEASE_**
 jobs:
   rworkflows:
     permissions: write-all

NAMESPACE

@@ -202,7 +202,7 @@ importFrom(tidyr,replace_na)
 importFrom(tidyr,spread)
 importFrom(tidyr,unite)
 importFrom(tidyr,unnest)
-importFrom(tidyselect,one_of)
+importFrom(tidyselect,any_of)
 importFrom(ttservice,bind_cols)
 importFrom(ttservice,bind_rows)
 importFrom(utils,capture.output)

R/functions.R

@@ -120,7 +120,7 @@ create_tt_from_bam_sam_bulk <- function(file_names, genome = "hg38", ...) {
 							genes %>%
 							select(
 								suppressWarnings(
-									one_of("GeneID", "symbol")
+									any_of("GeneID", "symbol")
 								)
 								) %>%
 							as_tibble() %>%
@@ -434,7 +434,7 @@ get_differential_transcript_abundance_bulk <- function(data,
 		select(!!.transcript,
 					 !!.sample,
 					 !!.abundance,
-					 one_of(parse_formula(.formula))) %>%
+					 any_of(parse_formula(.formula))) %>%
 		distinct() %>%
 
 		# drop factors as it can affect design matrix
@@ -454,14 +454,14 @@ get_differential_transcript_abundance_bulk <- function(data,
 	# if (
 	# 	# If I have some discrete covariates
 	# 	df_for_edgeR %>%
-	# 	select(one_of(parse_formula(.formula))) %>%
+	# 	select(any_of(parse_formula(.formula))) %>%
 	# 	select_if(function(col)
 	# 		is.character(col) | is.factor(col) | is.logical(col)) %>%
 	# 	ncol %>% gt(0) &
 	#
 	# 	# If I have at least 2 samples per group
 	# 	df_for_edgeR %>%
-	# 	select(!!.sample, one_of(parse_formula(.formula))) %>%
+	# 	select(!!.sample, any_of(parse_formula(.formula))) %>%
 	# 	select_if(function(col) !is.numeric(col) & !is.integer(col) & !is.double(col) ) %>%
 	# 	distinct %>%
 	# 	group_by_at(vars(-!!.sample)) %>%
@@ -821,7 +821,7 @@ get_differential_transcript_abundance_glmmSeq <- function(.data,
   # Create design matrix for dispersion, removing random effects
   design =
     model.matrix(
-      object = .formula |> eliminate_random_effects(),
+      object = .formula |> lme4::nobars(),
       data = metadata
     )
 
@@ -950,7 +950,7 @@ get_differential_transcript_abundance_bulk_voom <- function(.data,
 		select(!!.transcript,
 					 !!.sample,
 					 !!.abundance,
-					 one_of(parse_formula(.formula))) %>%
+					 any_of(parse_formula(.formula))) %>%
 		distinct() %>%
 
 		# drop factors as it can affect design matrix
@@ -1215,7 +1215,7 @@ get_differential_transcript_abundance_deseq2 <- function(.data,
 		select(!!.transcript,
 					 !!.sample,
 					 !!.abundance,
-					 one_of(parse_formula(.formula))) %>%
+					 any_of(parse_formula(.formula))) %>%
 		distinct() %>%
 
 		# drop factors as it can affect design matrix
@@ -1639,15 +1639,15 @@ test_gene_enrichment_bulk_EGSEA <- function(.data,
 
 		# Prepare the data frame
 		select(!!.entrez, !!.sample, !!.abundance,
-					 one_of(parse_formula(.formula))) %>%
+					 any_of(parse_formula(.formula))) %>%
 		distinct() %>%
 
 		# Add entrez from symbol
 		filter(!!.entrez %>% is.na %>% not())
 
 	# Check if at least two samples for each group
 	if (df_for_edgeR %>%
-			select(!!.sample, one_of(parse_formula(.formula))) %>%
+			select(!!.sample, any_of(parse_formula(.formula))) %>%
 			distinct %>%
 			count(!!as.symbol(parse_formula(.formula))) %>%
 			distinct(n) %>%

R/methods_SE.R

@@ -1157,7 +1157,7 @@ setMethod("adjust_abundance",
     new_range_data = new_range_data %>%
 
       # I have to use this trick because rowRanges() and rowData() share @elementMetadata
-      select(-one_of(colnames(new_row_data) %>% outersect(quo_name(.transcript)))) %>%
+      select(-any_of(colnames(new_row_data) %>% outersect(quo_name(.transcript)))) %>%
       suppressWarnings()
 
 

R/tidySummarizedExperiment.R

@@ -1,14 +1,14 @@
 eliminate_GRanges_metadata_columns_also_present_in_Rowdata = function(.my_data, se){
   .my_data %>%
-    select(-one_of(colnames(rowData(se)))) %>%
+    select(-any_of(colnames(rowData(se)))) %>%
 
     # In case there is not metadata column
     suppressWarnings()
 }
 
 
 #' @importFrom dplyr select
-#' @importFrom tidyselect one_of
+#' @importFrom tidyselect any_of
 #' @importFrom tibble as_tibble
 #' @importFrom tibble tibble
 #' @importFrom SummarizedExperiment rowRanges
@@ -146,7 +146,7 @@ subset_tibble_output = function(.data, count_info, sample_info, gene_info, range
     sample_info %>%
     when(
       colnames(.) %>% intersect(output_colnames) %>% length() %>% equals(0) ~ NULL,
-      select(., one_of(s_(.data)$name, output_colnames)) %>%
+      select(., any_of(s_(.data)$name, output_colnames)) %>%
         suppressWarnings()
     )
 
@@ -155,7 +155,7 @@ subset_tibble_output = function(.data, count_info, sample_info, gene_info, range
     range_info %>%
     when(
       colnames(.) %>% intersect(output_colnames) %>% length() %>% equals(0) ~ NULL,
-      select(., one_of(f_(.data)$name, output_colnames)) %>%
+      select(., any_of(f_(.data)$name, output_colnames)) %>%
         suppressWarnings()
     )
 
@@ -164,7 +164,7 @@ subset_tibble_output = function(.data, count_info, sample_info, gene_info, range
     gene_info %>%
     when(
       colnames(.) %>% intersect(output_colnames) %>% length() %>% equals(0) ~ NULL,
-      select(., one_of(f_(.data)$name, output_colnames)) %>%
+      select(., any_of(f_(.data)$name, output_colnames)) %>%
         suppressWarnings()
     )
 
@@ -173,7 +173,7 @@ subset_tibble_output = function(.data, count_info, sample_info, gene_info, range
     count_info %>%
     when(
       colnames(.) %>% intersect(output_colnames) %>% length() %>% equals(0) ~ NULL,
-      select(., one_of(f_(.data)$name, s_(.data)$name, output_colnames)) %>%
+      select(., any_of(f_(.data)$name, s_(.data)$name, output_colnames)) %>%
         suppressWarnings()
     )
 
@@ -206,7 +206,7 @@ subset_tibble_output = function(.data, count_info, sample_info, gene_info, range
   output_df %>%
 
     # Cleanup
-    select(one_of(output_colnames)) %>%
+    select(any_of(output_colnames)) %>%
     suppressWarnings()
 
 }

R/utilities.R

@@ -306,7 +306,7 @@ scale_design = function(df, .formula) {
     ungroup() %>%
     spread(cov, value) %>%
     arrange(as.integer(sample_idx)) %>%
-    select(`(Intercept)`, one_of(parse_formula(.formula)))
+    select(`(Intercept)`, any_of(parse_formula(.formula)))
 }
 
 get_tt_columns = function(.data){