Work with Seurat objects with split layers

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: UCell
 Type: Package
 Title: Rank-based signature enrichment analysis for single-cell data
-Version: 2.7.2
+Version: 2.7.3
 Authors@R: c(
   person('Massimo', 'Andreatta', 
      email = '[email protected]',
@@ -17,7 +17,7 @@ Description: UCell is a package for evaluating gene signatures in single-cell da
     demands less computing time and memory than other available methods, enabling the processing of large datasets in a few minutes even
     on machines with limited computing power. UCell can be applied to any single-cell data matrix, and includes functions to directly
     interact with SingleCellExperiment and Seurat objects.
-Depends: R(>= 4.2.0)
+Depends: R(>= 4.3.0)
 Imports: methods,
     data.table(>= 1.13.6),
     Matrix,
@@ -26,7 +26,7 @@ Imports: methods,
     BiocNeighbors,
     SingleCellExperiment,
     SummarizedExperiment
-Suggests: Seurat,
+Suggests: Seurat(>= 5.0.0),
     scater,
     scRNAseq,
     reshape2,

R/AddModuleScore_UCell.R

@@ -27,8 +27,7 @@
 #'     importance to negative genes
 #' @param assay Pull out data from this assay of the Seurat object
 #'     (if NULL, use \code{DefaultAssay(obj)})
-#' @param slot Pull out data from this slot of the Seurat object (will
-#'     become "layer" in Seurat v5)
+#' @param slot Pull out data from this slot (layer in v5) of the Seurat object
 #' @param chunk.size Number of cells to be processed simultaneously (lower
 #'     size requires slightly more computation but reduces memory demands)
 #' @param BPPARAM A [BiocParallel::bpparam()] object that tells UCell
@@ -70,52 +69,46 @@ AddModuleScore_UCell <- function(obj, features, maxRank=1500,
         chunk.size=1000, BPPARAM=NULL, ncores=1, storeRanks=FALSE,
         w_neg=1, assay=NULL, slot="counts", ties.method="average",
         force.gc=FALSE, name="_UCell") {
-
     if (!requireNamespace("Seurat", quietly = TRUE)) {
         stop("Function 'AddModuleScore_UCell' requires the Seurat package.
             Please install it.", call. = FALSE)
     }  
-
     features <- check_signature_names(features)
-
     if (is.null(assay)) {
         assay <- Seurat::DefaultAssay(obj)
     }
-
-
     # If rank matrix was pre-computed, evaluate the new signatures
-    # from these ranks. Else, calculate new ranks to score signatures
-    # (optionally storing ranks, takes up more memory but become very
-    # fast to evaluate further signatures)
+    # from these ranks.
     if ("UCellRanks" %in% Seurat::Assays(obj)) {
         meta.list <- rankings2Uscore(
-            Seurat::GetAssayData(obj, "counts", assay="UCellRanks"),
+            Seurat::GetAssayData(obj, layer="counts", assay="UCellRanks"),
             features=features, chunk.size=chunk.size, w_neg=w_neg,
             ncores=ncores, BPPARAM=BPPARAM, force.gc=force.gc, name=name)
-
     } else {
-        meta.list <- calculate_Uscore(
-            Seurat::GetAssayData(obj, slot, assay=assay),
+        layers <- SeuratObject::Layers(obj, assay=assay, search = slot)
+        if (is.null(layers)) {
+          stop(sprintf("Cannot find layer %s in assay %s", slot, assay))
+        }
+        meta.list <- lapply(layers, function(x) {
+          calculate_Uscore(
+            Seurat::GetAssayData(obj, layer=x, assay=assay),
             features=features, maxRank=maxRank,
             chunk.size=chunk.size, w_neg=w_neg,
             ncores=ncores, BPPARAM=BPPARAM, ties.method=ties.method,
             force.gc=force.gc, storeRanks=storeRanks, name=name)
-
+        })
+        meta.list <- unlist(meta.list, recursive = FALSE)
         #store ranks matrix?
         if (storeRanks==TRUE){
             cells_rankings.merge <- lapply(meta.list,
                 function(x) rbind(x[["cells_rankings"]]))
             cells_rankings.merge <- Reduce(cbind, cells_rankings.merge)
-
             obj[["UCellRanks"]] <- Seurat::CreateAssayObject(
                 cells_rankings.merge)
         }
     }
-
     meta.merge <- lapply(meta.list,function(x) rbind(x[["cells_AUC"]]))
     meta.merge <- Reduce(rbind, meta.merge)
-
     obj <- Seurat::AddMetaData(obj, as.data.frame(meta.merge))
-
     return(obj)
 }

R/HelperFunctions.R

@@ -419,10 +419,8 @@ SmoothKNN.Seurat <- function(
     }
     m <- obj[[found]]
   } else {  # Work directly on features
-
-    exp <- Seurat::GetAssayData(obj, assay=assay, slot=slot)
+    exp <- Seurat::GetAssayData(obj, layer=slot, assay=assay)
     feats <- rownames(exp)
-
     found <- intersect(signature.names, feats)
     notfound <- setdiff(signature.names, found)
 
@@ -435,7 +433,6 @@ SmoothKNN.Seurat <- function(
                       assay, nf)
       warning(mess, immediate.=TRUE, call.=FALSE, noBreaks.=TRUE)
     }
-
     m <- t(exp[found, , drop=FALSE])
   }
   ncells <- ncol(obj)