Merge pull request #658 from satijalab/checkmatrix

Add back check.matrix
satijalab · Sep 21, 2022 · c53699b · c53699b
2 parents 38af263 + ef07a34
commit c53699b
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Showing 5 changed files with 33 additions and 112 deletions.
diff --git a/R/clustering.R b/R/clustering.R
@@ -259,9 +259,7 @@ PredictAssay <- function(
   )
   colnames(x = predicted) <- Cells(x = object)
   if (return.assay) {
-    # TODO: restore once check.matrix is implemented in SeuratObject
-    # predicted.assay <- CreateAssayObject(data = predicted, check.matrix = FALSE)
-    predicted.assay <- CreateAssayObject(data = predicted)
+    predicted.assay <- CreateAssayObject(data = predicted, check.matrix = FALSE)
     return (predicted.assay)
   } else {
     return (predicted)

diff --git a/R/integration.R b/R/integration.R
@@ -1461,19 +1461,12 @@ IntegrateData <- function(
   } else {
     active.assay <- DefaultAssay(object = ref[[1]])
     reference.integrated[[active.assay]] <- NULL
-    # TODO: restore once check.matrix is in SeuratObject
-    # reference.integrated[[active.assay]] <- CreateAssayObject(
-    #   data = GetAssayData(
-    #     object = reference.integrated[[new.assay.name]],
-    #     slot = 'data'
-    #   ),
-    #   check.matrix = FALSE
-    # )
     reference.integrated[[active.assay]] <- CreateAssayObject(
       data = GetAssayData(
         object = reference.integrated[[new.assay.name]],
         slot = 'data'
-      )
+      ),
+      check.matrix = FALSE
     )
     DefaultAssay(object = reference.integrated) <- active.assay
     reference.integrated[[new.assay.name]] <- NULL
@@ -1496,13 +1489,9 @@ IntegrateData <- function(
     )
 
     # Construct final assay object
-    # TODO: restore once check.matrix is in SeuratObject
-    # integrated.assay <- CreateAssayObject(
-    #   data = integrated.data,
-    #   check.matrix = FALSE
-    # )
     integrated.assay <- CreateAssayObject(
-      data = integrated.data
+      data = integrated.data,
+      check.matrix = FALSE
     )
     if (normalization.method == "SCT") {
       integrated.assay <- CreateSCTAssayObject(
@@ -1594,13 +1583,9 @@ IntegrateEmbeddings.IntegrationAnchorSet <- function(
     embeddings <- t(x = Embeddings(object = reductions)[cell.names.map[Cells(x = object.list[[i]])], dims.to.integrate])
     rownames(x = embeddings) <- dims.names
     fake.assay <- suppressWarnings(
-      # TODO: restore once check.matrix is in SeuratObject
-      # expr = CreateAssayObject(
-      #   data = embeddings,
-      #   check.matrix = FALSE
-      # )
       expr = CreateAssayObject(
-        data = embeddings
+        data = embeddings,
+        check.matrix = FALSE
       )
     )
     object.list[[i]][['drtointegrate']] <- fake.assay
@@ -1636,18 +1621,11 @@ IntegrateEmbeddings.IntegrationAnchorSet <- function(
   }
   active.assay <- DefaultAssay(object = object.list[reference.datasets][[1]])
   reference.integrated[[active.assay]] <- NULL
-  # TODO: restore once check.matrix is in SeuratObject
-  # reference.integrated[[active.assay]] <- CreateAssayObject(
-  #   data = GetAssayData(
-  #     object = reference.integrated[[new.reduction.name.safe]],
-  #     slot = 'data',
-  #     check.matrix = FALSE
-  #   )
-  # )
   reference.integrated[[active.assay]] <- CreateAssayObject(
     data = GetAssayData(
       object = reference.integrated[[new.reduction.name.safe]],
-      slot = 'data'
+      slot = 'data',
+      check.matrix = FALSE
     )
   )
   DefaultAssay(object = reference.integrated) <- active.assay
@@ -1734,13 +1712,9 @@ IntegrateEmbeddings.TransferAnchorSet <- function(
     )[ , dims.to.integrate])
     rownames(x = embeddings) <- dims.names
     fake.assay <- suppressWarnings(
-      # TODO restore once check.matrix is in SeuratObject
-      # expr = CreateAssayObject(
-      #   data = embeddings,
-      #   check.matrix = FALSE
-      # )
       expr = CreateAssayObject(
-        data = embeddings
+        data = embeddings,
+        check.matrix = FALSE
       )
     )
     object.list[[i]][['drtointegrate']] <- fake.assay
@@ -3014,9 +2988,9 @@ TransferData <- function(
         stringsAsFactors = FALSE
       )
       if (prediction.assay || !is.null(x = query)) {
-        # TODO: restore once check.matrix is in SeuratObject
-        # predictions <- CreateAssayObject(data = t(x = as.matrix(x = prediction.scores)), check.matrix = FALSE)
-        predictions <- CreateAssayObject(data = t(x = as.matrix(x = prediction.scores)))
+        predictions <- CreateAssayObject(
+          data = t(x = as.matrix(x = prediction.scores)), check.matrix = FALSE
+        )
         Key(object = predictions) <- paste0("predictionscore", rd.name, "_")
       }
       if (is.null(x = query)) {
@@ -3040,13 +3014,9 @@ TransferData <- function(
         new.data <- as.sparse(x = new.data)
       }
       if (slot == "counts") {
-        # TODO: restore once check.matrix is in SeuratObject
-        # new.assay <- CreateAssayObject(counts = new.data, check.matrix = FALSE)
-        new.assay <- CreateAssayObject(counts = new.data)
+        new.assay <- CreateAssayObject(counts = new.data, check.matrix = FALSE)
       } else if (slot == "data") {
-        # TODO: restore once check.matrix is in SeuratObject
-        # new.assay <- CreateAssayObject(data = new.data, check.matrix = FALSE)
-        new.assay <- CreateAssayObject(data = new.data)
+        new.assay <- CreateAssayObject(data = new.data, check.matrix = FALSE)
       }
       Key(object = new.assay) <- paste0(rd.name, "_")
       if (is.null(x = query)) {
@@ -4096,13 +4066,9 @@ PairwiseIntegrateReference <- function(
   features.to.integrate <- features.to.integrate %||% features
   if (length(x = reference.objects) == 1) {
     ref.obj <- object.list[[reference.objects]]
-    # TODO: restore once check.matrix is in SeuratObject
-    # ref.obj[[new.assay.name]] <- CreateAssayObject(
-    #   data = GetAssayData(ref.obj, slot = 'data')[features.to.integrate, ],
-    #   check.matrix = FALSE
-    # )
     ref.obj[[new.assay.name]] <- CreateAssayObject(
-      data = GetAssayData(ref.obj, slot = 'data')[features.to.integrate, ]
+      data = GetAssayData(ref.obj, slot = 'data')[features.to.integrate, ],
+      check.matrix = FALSE
     )
     DefaultAssay(object = ref.obj) <- new.assay.name
     return(ref.obj)
@@ -4225,9 +4191,7 @@ PairwiseIntegrateReference <- function(
       verbose = verbose
     )
     integrated.matrix <- cbind(integrated.matrix, GetAssayData(object = object.1, slot = 'data')[features.to.integrate, ])
-    # TODO: restore once check.matrix is in SeuratObject
-    # merged.obj[[new.assay.name]] <- CreateAssayObject(data = integrated.matrix, check.matrix = FALSE)
-    merged.obj[[new.assay.name]] <- CreateAssayObject(data = integrated.matrix)
+    merged.obj[[new.assay.name]] <- CreateAssayObject(data = integrated.matrix, check.matrix = FALSE)
     DefaultAssay(object = merged.obj) <- new.assay.name
     object.list[[as.character(x = ii)]] <- merged.obj
     object.list[[merge.pair[[1]]]] <- NULL

diff --git a/R/mixscape.R b/R/mixscape.R
@@ -106,17 +106,11 @@ CalcPerturbSig <- function(
   slot(object = object, name = "tools")[[paste("CalcPerturbSig", assay, reduction, sep = ".")]] <- all_neighbors
   all_diff <- do.call(what = cbind, args = all_diff)
   prtb.assay <- suppressWarnings(
-    # TODO: restore once check.matrix is in SeuratObject
-    # expr = CreateAssayObject(
-    #   data =  all_diff[, colnames(x = object)],
-    #   min.cells = -Inf,
-    #   min.features = -Inf,
-    #   check.matrix = FALSE
-    # )
     expr = CreateAssayObject(
       data =  all_diff[, colnames(x = object)],
       min.cells = -Inf,
-      min.features = -Inf
+      min.features = -Inf,
+      check.matrix = FALSE
     )
   )
   object[[new.assay.name]] <- prtb.assay
@@ -1028,7 +1022,7 @@ PlotPerturbScore <- function(
   gd <- setdiff(x = unique(x = prtb_score[, target.gene.class]), y = target.gene.ident)
   colnames(x = prtb_score)[2] <- "gene"
   prtb_score$cell.bc <- sapply(rownames(prtb_score), FUN = function(x) substring(x, regexpr("[.]", x) + 1))
-  
+
   if (isTRUE(x = before.mixscape)) {
     cols <- setNames(
       object = c("grey49", col),
@@ -1217,23 +1211,14 @@ GetMissingPerturb <- function(object, assay, features, verbose = TRUE) {
   }
   all_diff <- do.call(what = cbind, args = all_diff)
   all_diff <- all_diff[, colnames(x = object[[assay]]), drop = FALSE]
-  # TODO: restore once check.matrix is in SeuratObject
-  # new.assay <- CreateAssayObject(
-  #   data = rbind(
-  #     GetAssayData(object = object[[assay]], slot = "data"),
-  #     all_diff
-  #   ),
-  #   min.cells = 0,
-  #   min.features = 0,
-  #   check.matrix = FALSE
-  # )
   new.assay <- CreateAssayObject(
     data = rbind(
       GetAssayData(object = object[[assay]], slot = "data"),
       all_diff
     ),
     min.cells = 0,
-    min.features = 0
+    min.features = 0,
+    check.matrix = FALSE
   )
   new.assay <- SetAssayData(
     object = new.assay,

diff --git a/R/preprocessing.R b/R/preprocessing.R
@@ -1790,14 +1790,10 @@ SCTransform <- function(
     if (verbose) {
       message('Place corrected count matrix in counts slot')
     }
-    # TODO: restore once check.matrix is in SeuratObject
-    # assay.out <- CreateAssayObject(counts = vst.out$umi_corrected, check.matrix = FALSE)
-    assay.out <- CreateAssayObject(counts = vst.out$umi_corrected,)
+    assay.out <- CreateAssayObject(counts = vst.out$umi_corrected, check.matrix = FALSE)
     vst.out$umi_corrected <- NULL
   } else {
-    # TODO: restore once check.matrix is in SeuratObject
-    # assay.out <- CreateAssayObject(counts = umi, check.matrix = FALSE)
-    assay.out <- CreateAssayObject(counts = umi)
+    assay.out <- CreateAssayObject(counts = umi, check.matrix = FALSE)
   }
   # set the variable genes
   VariableFeatures(object = assay.out) <- residual.features %||% top.features

diff --git a/R/utilities.R b/R/utilities.R
@@ -1339,18 +1339,11 @@ PseudobulkExpression <- function(
     if (slot[1] == 'scale.data') {
       na.matrix <- data.return[[1]]
       na.matrix[1:length(x = na.matrix)] <- NA
-      # TODO: restore once check.matrix is in SeuratObject
-      # toRet <- CreateSeuratObject(
-      #   counts = na.matrix,
-      #   project = if (pb.method == "average") "Average" else "Aggregate",
-      #   assay = names(x = data.return)[1],
-      #   check.matrix = FALSE,
-      #   ...
-      # )
       toRet <- CreateSeuratObject(
         counts = na.matrix,
         project = if (pb.method == "average") "Average" else "Aggregate",
         assay = names(x = data.return)[1],
+        check.matrix = FALSE,
         ...
       )
       toRet <- SetAssayData(
@@ -1372,18 +1365,11 @@ PseudobulkExpression <- function(
         new.data = data.return[[1]]
       )
     } else {
-      # TODO: restore once check.matrix is in SeuratObject
-      # toRet <- CreateSeuratObject(
-      #   counts = data.return[[1]],
-      #   project = if (pb.method == "average") "Average" else "Aggregate",
-      #   assay = names(x = data.return)[1],
-      #   check.matrix = FALSE,
-      #   ...
-      # )
       toRet <- CreateSeuratObject(
         counts = data.return[[1]],
         project = if (pb.method == "average") "Average" else "Aggregate",
         assay = names(x = data.return)[1],
+        check.matrix = FALSE,
         ...
       )
       toRet <- SetAssayData(
@@ -1399,9 +1385,7 @@ PseudobulkExpression <- function(
         if (slot[i] == 'scale.data') {
           na.matrix <- data.return[[i]]
           na.matrix[1:length(x = na.matrix)] <- NA
-          # TODO: restore once check.matrix is in SeuratObject
-          # toRet[[names(x = data.return)[i]]] <- CreateAssayObject(counts = na.matrix, check.matrix = FALSE)
-          toRet[[names(x = data.return)[i]]] <- CreateAssayObject(counts = na.matrix)
+          toRet[[names(x = data.return)[i]]] <- CreateAssayObject(counts = na.matrix, check.matrix = FALSE)
           toRet <- SetAssayData(
             object = toRet,
             assay = names(x = data.return)[i],
@@ -1421,9 +1405,7 @@ PseudobulkExpression <- function(
             new.data = as.matrix(x = data.return[[i]])
           )
         } else {
-          # TODO: restore once check.matrix is in SeuratObject
-          # toRet[[names(x = data.return)[i]]] <- CreateAssayObject(counts = data.return[[i]], check.matrix = FALSE)
-          toRet[[names(x = data.return)[i]]] <- CreateAssayObject(counts = data.return[[i]])
+          toRet[[names(x = data.return)[i]]] <- CreateAssayObject(counts = data.return[[i]], check.matrix = FALSE)
           toRet <- SetAssayData(
             object = toRet,
             assay = names(x = data.return)[i],
@@ -1897,13 +1879,9 @@ CreateDummyAssay <- function(assay) {
   cm <- as.sparse(x = cm)
   rownames(x = cm) <- rownames(x = assay)
   colnames(x = cm) <- colnames(x = assay)
-  # TODO: restore once check.matrix is in SeuratObject
-  # return(CreateAssayObject(
-  #   counts = cm,
-  #   check.matrix = FALSE
-  # ))
   return(CreateAssayObject(
-    counts = cm
+    counts = cm,
+    check.matrix = FALSE
   ))
 }
 
@@ -2336,7 +2314,7 @@ RowSumSparse <- function(mat) {
   names(x = output) <- rownames(x = mat)
   return(output)
 }
- 
+
 # Calculate row variance of a sparse matrix
 #
 # @param mat sparse matrix