FindAllMarkers not working (Error (data layers are not joined. Please run JoinLayersWarning: When testing 1 versus all)

Question

[Nitin]

Looking for some suggestion

I have an integrated object. I removed 4 clusters and want to re-cluster . Below are the steps I ran, however, I am getting error while trying to run the FindAllMarkers

DefaultAssay(Integrated_object) <- "RNA"

cells_to_remove <- WhichCells(Integrated_object, idents = c("CLUSTER_6", "CLUSTER_9", "CLUSTER_10", "CLUSTER_11"))

##############
Integrated_object_Subset <- subset(Integrated_object, cells = setdiff(Cells(Integrated_object), cells_to_remove))
Integrated_object_Subset_list <- SplitObject(Integrated_object_Subset, split.by = "Sample")
Integrated_object_Subset_list <- lapply(X = Integrated_object_Subset_list, FUN = function(x) {
    x <- NormalizeData(x)
    x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})
features <- SelectIntegrationFeatures(object.list =Integrated_object_Subset_list)
anchors <- FindIntegrationAnchors(object.list =Integrated_object_Subset_list, anchor.features = features)

#########
combined1 <- IntegrateData(anchorset = anchors)
DefaultAssay(tcell_combined1) <- "integrated"
combined1 <- ScaleData(combined1, verbose = FALSE)
combined1 <- RunPCA(combined1, npcs = 30, verbose = FALSE)
combined1 <- RunUMAP(combined1, reduction = "pca", dims = 1:10)
combined1 <- FindNeighbors(combined1, reduction = "pca", dims = 1:10)
combined1 <- FindClusters(combined1, resolution = c(0.2, 0.3,0.4,0.5, 0.7))

########
sel.clust = "integrated_snn_res.0.3"
combined1  <- SetIdent(combined1 , value = sel.clust)
table(combined1 @active.ident)

###### DE #####
#Compute differentiall expression
markers_genes <- FindAllMarkers(
    combined1 ,
    log2FC.threshold = 0.2,
    test.use = "wilcox",
    min.pct = 0.1,
    min.diff.pct = 0.2,
    only.pos = TRUE,
    max.cells.per.ident = 50,
    assay = "RNA"
)

OUTPUT

Calculating cluster 0
Calculating cluster 1
Calculating cluster 2
Calculating cluster 3
Calculating cluster 4
Calculating cluster 5
Calculating cluster 6
Calculating cluster 7
Warning: No DE genes identifiedWarning: The following tests were not performed: Warning: When testing 0 versus all:
    data layers are not joined. Please run JoinLayersWarning: When testing 1 versus all:
    data layers are not joined. Please run JoinLayersWarning: When testing 2 versus all:
    data layers are not joined. Please run JoinLayersWarning: When testing 3 versus all:
    data layers are not joined. Please run JoinLayersWarning: When testing 4 versus all:
    data layers are not joined. Please run JoinLayersWarning: When testing 5 versus all:
    data layers are not joined. Please run JoinLayersWarning: When testing 6 versus all:
    data layers are not joined. Please run JoinLayersWarning: When testing 7 versus all:
    data layers are not joined. Please run JoinLayers

[kayah]

You should run JoinLayers() before FindAllMarkers()!!

[Nitin]

I tried that too. It did not work.

[Nitin]

I did run the JoinLayers() even then it is not working, here is the output before and after

DefaultAssay(combined1) <- 'RNA'

combined1

#####
An object of class Seurat 
50788 features across 48215 samples within 3 assays 
Active assay: RNA (24468 features, 2000 variable features)
 25 layers present: data.1, data.2, data.3, data.4, data.5, data.6, data.7, data.8, counts.1, scale.data.1, counts.2, scale.data.2, counts.3, scale.data.3, counts.4, scale.data.4, counts.5, scale.data.5, counts.6, scale.data.6, counts.7, scale.data.7, counts.8, scale.data.8, scale.data
 2 other assays present: SCT, integrated
 2 dimensional reductions calculated: pca, umap
#####

JoinLayers(alldata)

#######
An object of class Seurat 
50788 features across 48215 samples within 3 assays 
Active assay: RNA (24468 features, 2000 variable features)
 11 layers present: data, counts, scale.data.1, scale.data.2, scale.data.3, scale.data.4, scale.data.5, scale.data.6, scale.data.7, scale.data.8, scale.data
 2 other assays present: SCT, integrated
 2 dimensional reductions calculated: pca, umap
#######