Fixing the misassumptions on Seurat v5 and end of cycle of v4.

enblacar · Oct 4, 2023 · 6d64ae7 · 6d64ae7
1 parent da053bf
commit 6d64ae7
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diff --git a/NEWS.md b/NEWS.md
@@ -2,7 +2,7 @@
 
 ## General.
 -   Fixed a bug that prevented error messages stating the dependencies missing per function to show up properly.
--   Fixed assumptions on Seurat v4 and v5 and reverted to the use of cannonical `GetAssayData` functions.
+-   Fixed assumptions on Seurat v4 and v5 and reverted to the use of cannonical `GetAssayData` and `SetAssayData` functions.
 
 ## do_BeeSwarmPlot()
 -   Changed default continuous palette to `YlGnBu`.

diff --git a/R/do_AffinityAnalysisPlot.R b/R/do_AffinityAnalysisPlot.R
@@ -217,10 +217,11 @@ do_AffinityAnalysisPlot <- function(sample,
              dplyr::filter(.data$target != "deleteme")
 
   # Get expression data.
+  suppressWarnings({
   mat <- SeuratObject::GetAssayData(sample,
-                      assay = assay,
-                      slot = slot)
-
+                                    assay = assay,
+                                    slot = slot)
+  })
   # Compute activities.
   if(isTRUE(verbose)){message(paste0(add_info(), crayon_body("Computing "),
                                      crayon_key("activities"),
@@ -256,6 +257,7 @@ do_AffinityAnalysisPlot <- function(sample,
   counter <- 0
   for (group in group.by){
     counter <- counter + 1
+    suppressWarnings({
     data.use <- SeuratObject::GetAssayData(sample,
                              assay = "affinity",
                              slot = "scale.data") %>%
@@ -269,7 +271,7 @@ do_AffinityAnalysisPlot <- function(sample,
                 tidyr::pivot_longer(cols = -dplyr::all_of(c("cell", group)),
                                     names_to = "source",
                                     values_to = "score")
-
+    })
     # Clustering based on the median across all cells.
     data.cluster <- data.use %>%
                     tidyr::pivot_wider(id_cols = dplyr::all_of(c("cell", group)),

diff --git a/R/do_BeeSwarmPlot.R b/R/do_BeeSwarmPlot.R
@@ -193,7 +193,9 @@ do_BeeSwarmPlot <- function(sample,
     sample@meta.data$rank_me <- sample@meta.data[, feature_to_rank]
     sample@meta.data$rank <- rank(sample@meta.data$rank_me)
   } else if (feature_to_rank %in% rownames(sample)){
+    suppressWarnings({
     sample@meta.data$rank_me  <- SeuratObject::GetAssayData(object = sample, slot = slot, assay = assay)[feature_to_rank, ]
+    })
     sample@meta.data$rank <- rank(sample@meta.data$rank_me)
   } else if (feature_to_rank %in% dim_colnames){
     for(red in Seurat::Reductions(object = sample)){

diff --git a/R/do_CopyNumberVariantPlot.R b/R/do_CopyNumberVariantPlot.R
@@ -249,6 +249,7 @@ do_CopyNumberVariantPlot <- function(sample,
 
   list.data <- list()
   for (group in group.by){
+    suppressWarnings({
     data.use  <- SeuratObject::GetAssayData(object = sample,
                                assay = "CNV_scores",
                                slot = "data") %>%
@@ -266,6 +267,7 @@ do_CopyNumberVariantPlot <- function(sample,
 
                  dplyr::group_by(.data[[group]], .data$Event) %>%
                  dplyr::summarise("mean" = mean(.data$CNV_score, na.rm = TRUE))
+    })
 
     events <- c(as.character(seq(1, 22)), vapply(seq(1, 22), function(x){return(c(paste0(x, "p"), paste0(x, "q")))}, FUN.VALUE = character(2)))
     if (base::isFALSE(flip)){

diff --git a/R/do_CorrelationPlot.R b/R/do_CorrelationPlot.R
@@ -156,6 +156,7 @@ do_CorrelationPlot <- function(sample = NULL,
     variable_genes <- Seurat::VariableFeatures(sample)
 
     # Sort them in order (for ATAC experiments).
+    suppressWarnings({
     genes <- rownames(SeuratObject::GetAssayData(object = sample,
                                     assay = assay,
                                     slot = "data"))
@@ -165,14 +166,15 @@ do_CorrelationPlot <- function(sample = NULL,
              dplyr::filter(.data$Genes %in% variable_genes) %>%
              dplyr::arrange(.data$Position) %>%
              dplyr::pull(.data$Genes)
-
+    })
 
     # Subset sample according to the variable genes.
     sample <- sample[genes, ]
     # Scale the data
     sample <- Seurat::ScaleData(sample, features = genes, verbose = FALSE)
 
     # Retrieve correlation matrix.
+    suppressWarnings({
     out <- sample@meta.data %>%
            dplyr::select(dplyr::all_of(c(group.by))) %>%
            tibble::rownames_to_column(var = "cell") %>%
@@ -197,7 +199,7 @@ do_CorrelationPlot <- function(sample = NULL,
            as.matrix() %>%
            stats::cor() %>%
            round(digits = 2)
-
+    })
     # Compute hclust.
     if (isTRUE(cluster)){
       order <- rownames(out)[stats::hclust(stats::dist(out, method = "euclidean"), method = "ward.D")$order]

diff --git a/R/do_ExpressionHeatmap.R b/R/do_ExpressionHeatmap.R
@@ -193,7 +193,7 @@ do_ExpressionHeatmap <- function(sample,
     # Extract activities from object as a long dataframe
     suppressMessages({
       sample$group.by <- sample@meta.data[, group]
-
+      suppressWarnings({
       df <- SeuratObject::GetAssayData(sample,
                           assay = assay,
                           slot = slot)[features, , drop = FALSE] %>%
@@ -211,6 +211,7 @@ do_ExpressionHeatmap <- function(sample,
             dplyr::group_by(.data$group.by, .data$gene) %>%
             dplyr::summarise(mean = mean(.data$expression, na.rm = TRUE))
       df.order <- df
+      })
     })
     matrix.list[[group]][["df"]] <- df
     matrix.list[[group]][["df.order"]] <- df.order

diff --git a/R/do_FeaturePlot.R b/R/do_FeaturePlot.R
@@ -575,7 +575,9 @@ do_FeaturePlot <- function(sample,
         sample$dummy <- sample@meta.data[, feature]
         ## Or is a gene in the object.
       } else if (feature %in% rownames(sample)){
+        suppressWarnings({
         sample$dummy <- SeuratObject::GetAssayData(object = sample, slot = slot, assay = assay)[feature, ]
+        })
         ## Or is a dimensional reduction component.
       } else if (feature %in% dim_colnames){
         # Iterate over each dimensional reduction in the object.

diff --git a/R/do_GeyserPlot.R b/R/do_GeyserPlot.R
@@ -288,6 +288,7 @@ do_GeyserPlot <- function(sample,
               tibble::rownames_to_column(var = "cell") %>%
               tibble::as_tibble()
     } else if (isTRUE(feature %in% rownames(sample))){
+      suppressWarnings({
       data <- SeuratObject::GetAssayData(object = sample,
                                    assay = assay,
                                    slot = slot)[feature, , drop = FALSE] %>%
@@ -300,6 +301,7 @@ do_GeyserPlot <- function(sample,
                                     dplyr::select(dplyr::all_of(c(group.by))) %>%
                                     tibble::rownames_to_column(var = "cell")},
                                by = "cell")
+      })
     } else if (isTRUE(feature %in% dim_colnames)){
       data <- sample@reductions[[reduction]][[]][, feature, drop = FALSE] %>%
               as.data.frame() %>%

diff --git a/R/do_GroupwiseDEPlot.R b/R/do_GroupwiseDEPlot.R
@@ -283,6 +283,7 @@ do_GroupwiseDEPlot <- function(sample,
   list.exp <- list()
   for (group in group.by){
     order.use <- if (is.factor(sample@meta.data[, group])){levels(sample@meta.data[, group])} else {sort(unique(sample@meta.data[, group]))}
+    suppressWarnings({
     data <- SeuratObject::GetAssayData(sample,
                                  assay = assay,
                                  slot = slot)[genes.use, ] %>%
@@ -301,6 +302,7 @@ do_GroupwiseDEPlot <- function(sample,
                           "Group" = factor(.data[[group]], levels = rev(order.use)))
 
     list.exp[[group]] <- data
+    })
   }
 
 

diff --git a/R/do_LoadingsPlot.R b/R/do_LoadingsPlot.R
@@ -201,7 +201,7 @@ do_LoadingsPlot <- function(sample,
               dplyr::left_join(y = loadings,
                                by = "PC",
                                relationship = "many-to-many")
-
+  suppressWarnings({
   data.use <- data.use %>%
               dplyr::left_join(y = {SeuratObject::GetAssayData(sample,
                                                          assay = assay,
@@ -215,7 +215,7 @@ do_LoadingsPlot <- function(sample,
                                                         values_to = "Expression")},
                                by = c("Gene", "Cell")) %>%
               dplyr::mutate("Gene" = factor(.data$Gene, levels = genes.use))
-
+  })
   data.loading <- data.use %>%
                   dplyr::group_by(.data$Gene, .data$PC) %>%
                   dplyr::reframe("mean_Loading_Score" = mean(.data$Loading_Score, na.rm = TRUE))

diff --git a/R/do_PathwayActivityPlot.R b/R/do_PathwayActivityPlot.R
@@ -181,7 +181,8 @@ do_PathwayActivityPlot <- function(sample,
     # Extract activities from object as a long dataframe
     suppressMessages({
       sample$group.by <- sample@meta.data[, group]
-
+
+      suppressWarnings({
       df <- t(as.matrix(SeuratObject::GetAssayData(object = sample,
                                       assay = "progeny",
                                       slot = slot))) %>%
@@ -196,7 +197,7 @@ do_PathwayActivityPlot <- function(sample,
                                 values_to = "score") %>%
             dplyr::group_by(.data$group.by, .data$source) %>%
             dplyr::summarise(mean = mean(.data$score, na.rm = TRUE))
-
+      })
       df.order <- df
       df.order[is.na(df.order)] <- 0
 
@@ -206,7 +207,7 @@ do_PathwayActivityPlot <- function(sample,
 
       if (!is.null(split.by)){
         sample$split.by <- sample@meta.data[, split.by]
-
+        suppressWarnings({
         df.split <- t(as.matrix(SeuratObject::GetAssayData(object = sample,
                                               assay = "progeny",
                                               slot = slot))) %>%
@@ -222,6 +223,7 @@ do_PathwayActivityPlot <- function(sample,
                     dplyr::group_by(.data$split.by, .data$group.by, .data$source) %>%
                     dplyr::summarise(mean = mean(.data$score, na.rm = TRUE))
         matrix.list[[group]][["df.split"]] <- df.split
+        })
       }
     })
   }

diff --git a/R/do_SCExpressionHeatmap.R b/R/do_SCExpressionHeatmap.R
@@ -174,8 +174,11 @@ do_SCExpressionHeatmap <- function(sample,
   }
 
   `%>%` <- magrittr::`%>%`
+
+  suppressWarnings({
   genes.avail <- rownames(SeuratObject::GetAssayData(sample, slot = slot, assay = assay))
-
+  })
+
   assertthat::assert_that(sum(features %in% genes.avail) > 0,
                           msg = paste0(add_cross(), crayon_body("None of the features are present in the row names of the assay "),
                                        crayon_key(assay),
@@ -212,12 +215,12 @@ do_SCExpressionHeatmap <- function(sample,
                                          crayon_body(".")))
   }
 
-
+  suppressWarnings({
   matrix <- SeuratObject::GetAssayData(sample,
                                  assay = assay,
                                  slot = slot)[features, , drop = FALSE] %>%
             as.matrix()
-
+  })
   # Check group.by.
   out <- check_group_by(sample = sample,
                         group.by = group.by,

diff --git a/R/do_TFActivityPlot.R b/R/do_TFActivityPlot.R
@@ -186,7 +186,7 @@ do_TFActivityPlot <- function(sample,
       # Extract activities from object as a long dataframe
       suppressMessages({
         sample$group.by <- sample@meta.data[, group]
-
+        suppressWarnings({
         df <- t(as.matrix(SeuratObject::GetAssayData(object = sample,
                                         assay = "dorothea",
                                         slot = slot))) %>%
@@ -202,10 +202,10 @@ do_TFActivityPlot <- function(sample,
               dplyr::group_by(.data$group.by, .data$source) %>%
               dplyr::summarise(mean = mean(.data$score, na.rm = TRUE))
         df.order <- df
-
+        })
         if (!is.null(split.by)){
           sample$split.by <- sample@meta.data[, split.by]
-
+          suppressWarnings({
           df.split <- t(as.matrix(SeuratObject::GetAssayData(object = sample,
                                                 assay = "dorothea",
                                                 slot = slot))) %>%
@@ -221,6 +221,7 @@ do_TFActivityPlot <- function(sample,
                       dplyr::group_by(.data$split.by, .data$group.by, .data$source) %>%
                       dplyr::summarise(mean = mean(.data$score, na.rm = TRUE))
           matrix.list[[group]][["df.split"]] <- df.split
+          })
         }
 
 

diff --git a/R/utils.R b/R/utils.R
@@ -1026,9 +1026,11 @@ compute_scale_limits <- function(sample, feature, assay = NULL, reduction = NULL
   }
 
   if (feature %in% rownames(sample)){
+    suppressWarnings({
     data.check <- SeuratObject::GetAssayData(sample,
                                        assay = assay,
                                        slot = slot)[feature, ]
+    })
     scale.begin <- min(data.check, na.rm = TRUE)
     scale.end <- max(data.check, na.rm = TRUE)
   } else if (feature %in% colnames(sample@meta.data)){
@@ -1950,8 +1952,10 @@ compute_enrichment_scores <- function(sample,
     if (is.null(slot)){
       slot <- "data"
     }
+    suppressWarnings({
     scores <- AUCell::AUCell_run(exprMat = SeuratObject::GetAssayData(sample, assay = assay, slot = slot),
                                  geneSets = input_gene_list)
+    })
     scores <- scores@assays@data$AUC %>%
               as.matrix() %>%
               t() %>%
@@ -2096,6 +2100,7 @@ get_data_column <- function(sample,
                       tibble::rownames_to_column(var = "cell") %>%
                       dplyr::rename("feature" = dplyr::all_of(c(feature)))
   } else if (isTRUE(feature %in% rownames(sample))){
+    suppressWarnings({
     feature_column <- SeuratObject::GetAssayData(object = sample,
                                     assay = assay,
                                     slot = slot)[feature, , drop = FALSE] %>%
@@ -2104,6 +2109,7 @@ get_data_column <- function(sample,
                       as.data.frame() %>%
                       tibble::rownames_to_column(var = "cell") %>%
                       dplyr::rename("feature" = dplyr::all_of(c(feature)))
+    })
   } else if (isTRUE(feature %in% dim_colnames)){
     feature_column <- sample@reductions[[reduction]][[]][, feature, drop = FALSE] %>%
                       as.data.frame() %>%
@@ -3259,50 +3265,6 @@ get_SCpubr_colors <- function(){
   return(colors)
 }
 
-#' Set assay data depending on the version of SeuratObject
-#'
-#' @param sample Seurat object.
-#' @param assay Assay name.
-#' @param slot slot name.
-#' @param data data to set.
-#'
-#' @return The assay data.
-#' @noRd
-#' @examples
-#' \donttest{
-#' TBD
-#' }
-.SetAssayData <- function(sample,
-                          assay,
-                          slot,
-                          data){
-
-  # Check version of SeuratObject.
-  version <- utils::packageVersion("SeuratObject")
-  # nocov start
-  if (version > "4.1.3"){
-    if (slot == "counts"){
-      sample@assays[[assay]]$counts <- data
-    } else if (slot == "data"){
-      sample@assays[[assay]]$data <- data
-    } else if (slot == "scale.data"){
-      sample@assays[[assay]]$scale.data <- data
-    }
-
-    # Uncomment once the version is on CRAN.
-    # SeuratObject::LayerData(object = sample,
-    #                         assay = assay,
-    #                         layer = slot) <- data
-
-    # nocov end
-  } else {
-    sample <- SeuratObject::SetAssayData(object = sample,
-                                         assay = assay,
-                                         slot = slot,
-                                         new.data = data)
-  }
-  return(sample)
-}
 
 #' Check the group.by parameter
 #'