Merge branch 'master' into chilampoon-improve-documentation

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: tidybulk
 Title: Brings transcriptomics to the tidyverse 
-Version: 1.17.3
+Version: 1.17.4
 Authors@R: c(person("Stefano", "Mangiola", email = "[email protected]",
                   role = c("aut", "cre")),
             person("Maria", "Doyle", email = "[email protected]",

R/functions_SE.R

@@ -794,7 +794,7 @@ get_differential_transcript_abundance_bulk_SE <- function(.data,
 	    "with `___` in the design matrix, in order to work with edgeR.")
 	  colnames(design) = design |> colnames() |> str_replace(":", "___") 
 	}
-	
+
 	# Print the design column names in case I want contrasts
 	message(
 		sprintf(
@@ -1137,6 +1137,7 @@ get_differential_transcript_abundance_bulk_voom_SE <- function(.data,
 #' "edgeR_likelihood_ratio" (i.e., LRT)
 #' @param scaling_method A character string. The scaling method passed to the 
 #' backend function (i.e., edgeR::calcNormFactors; "TMM","TMMwsp","RLE","upperquartile")
+#' @param .scaling_factor A tidyeval (column name) for the precalculated TMM scaling
 #' @param omit_contrast_in_colnames If just one contrast is specified you can 
 #' choose to omit the contrast label in the colnames.
 #' @param ... Additional arguments for glmmSeq
@@ -1151,15 +1152,16 @@ get_differential_transcript_abundance_glmmSeq_SE <- function(.data,
                                             method,
 
                                             test_above_log2_fold_change = NULL,
-
-                                            scaling_method = "TMM",
-                                            omit_contrast_in_colnames = FALSE,
-                                            prefix = "",
-                                            .dispersion = NULL,
-                                            ...) {
+                                                            scaling_method = "TMM",
+                                                            .scaling_factor = NULL,
+                                                            omit_contrast_in_colnames = FALSE,
+                                                            prefix = "",
+                                                            .dispersion = NULL,
+                                                            ...) {
 
   .abundance = enquo(.abundance)
   .dispersion = enquo(.dispersion)
+  .scaling_factor = enquo(.scaling_factor)
 
   # Check if contrasts are of the same form
   if(
@@ -1233,7 +1235,10 @@ get_differential_transcript_abundance_glmmSeq_SE <- function(.data,
   dispersion = dispersion[rownames(counts)]
 
   # Scaling
-  sizeFactors <- counts |> edgeR::calcNormFactors(method = scaling_method)
+  if(.scaling_factor |> quo_is_symbolic())
+    sizeFactors = .data |> pivot_sample() |> pull(!!.scaling_factor)
+  else
+    sizeFactors <- counts |> edgeR::calcNormFactors(method = scaling_method)
 
 
   glmmSeq_object =

R/methods.R

@@ -627,14 +627,14 @@ setMethod("scale_abundance", "tidybulk", .scale_abundance)
 #' The scaling inference is then applied back to all unfiltered data.
 #'
 #' Underlying method
-#' 
+#'
 #' If `limma_normalize_quantiles` is chosen
-#' 
+#'
 #' .data |>limma::normalizeQuantiles()
-#'  
+#'
 #'  If `preprocesscore_normalize_quantiles_use_target` is chosen
-#'  
-#' .data |> 
+#'
+#' .data |>
 #'    preprocessCore::normalize.quantiles.use.target(
 #'       target = preprocessCore::normalize.quantiles.determine.target(.data)
 #'    )
@@ -675,6 +675,7 @@ setGeneric("quantile_normalise_abundance", function(
 										  target_distribution = NULL,
                                           action = "add") {
 
+
   # Fix NOTEs
   . = NULL
 
@@ -727,7 +728,7 @@ setGeneric("quantile_normalise_abundance", function(
       BiocManager::install("preprocessCore", ask = FALSE)
     }
     if(is.null(target_distribution)) target_distribution = preprocessCore::normalize.quantiles.determine.target(.data_norm)
-    
+
     .data_norm_quant =
       .data_norm |>
       preprocessCore::normalize.quantiles.use.target(

R/methods_SE.R

@@ -186,17 +186,16 @@ setMethod("tidybulk", "RangedSummarizedExperiment", .tidybulk_se)
 
 	my_counts_scaled =
 		list(
-			assays(.data) %>%
-				as.list() %>%
-				.[[1]] %>%
-				multiply_by(
-					rep(multiplier, rep(nrow(.),length(multiplier)))
-				)
+		  assay(.data) %*%
+				diag(multiplier)
+
 			) %>%
 		setNames(value_scaled)
+  colnames(my_counts_scaled[[1]]) = assay(.data)  |> colnames()
+
 
 	# Add the assay
-	assays(.data) =  assays(.data) %>% c(my_counts_scaled)
+	assays(.data, withDimnames=FALSE) =  assays(.data) %>% c(my_counts_scaled)
 
 	.data %>%
 
@@ -309,7 +308,7 @@ setMethod("scale_abundance",
       as.matrix()
 
     if(is.null(target_distribution)) target_distribution = preprocessCore::normalize.quantiles.determine.target(.data_norm)
-    
+
     .data_norm =
       .data_norm |>
       preprocessCore::normalize.quantiles.use.target(
@@ -2922,6 +2921,8 @@ setMethod("describe_transcript", "RangedSummarizedExperiment", .describe_transcr
   combination_of_factors_of_NON_interest =
     # Factors
     se[1,1, drop=FALSE] |>
+    colData() |> 
+    as_tibble(rownames = ".sample") |> 
     select(...) |>
     suppressWarnings() |>
     colnames() |>

tests/testthat/test-bulk_methods_SummarizedExperiment.R

@@ -747,7 +747,7 @@ test_that("gene over representation",{
       species="Homo sapiens"
     )
 
-  expect_equal(	ncol(res),	10	)
+  expect_equal(	ncol(res),	13	)
 
 
 
@@ -854,8 +854,8 @@ test_that("Only reduced dimensions UMAP - no object",{
 test_that("resolve_complete_confounders_of_non_interest",{
 
 
-  library(tidySummarizedExperiment)
-  library(tidybulk)
+  #library(tidySummarizedExperiment)
+  library(SummarizedExperiment)
 
   # Sample annotations
   sample_annotations <- data.frame(
@@ -890,7 +890,9 @@ test_that("resolve_complete_confounders_of_non_interest",{
 
   se |>
     resolve_complete_confounders_of_non_interest(A, B, C) |>
-    distinct(.sample, A, B, C) |>
+    colData() |> 
+    _[, c("A", "B", "C")] |>
+    as_tibble(rownames = ".sample") |> 
     expect_identical(expected_tibble )