Hello all! I would like to preface this post by saying that I am not a bioinformatician and I need help with the clustering of a scRNA-seq PBMC dataset and generating a UMAP with different annotated cell types. I am running the the R script below and have annotated the cell-types with SingleR using the Monacco Immune Dataset to identify broad cell types (B cells, T cells, NK cells etc.). However in my UMAP all the cell types are intermixed and I would like them to form separate clusters.

# 1. Normalization -----
pbmc <- NormalizeData(pbmc)

# 2. Find variable features -----
pbmc <- FindVariableFeatures(pbmc, nfeatures = 5000)

# 3. Scale data -----
pbmc <- ScaleData(pbmc, vars.to.regress = "percent.mt")

# 4. Compute PCA -----
pbmc <- RunPCA(pbmc)

# 5. Determine dimentionality of the dataset (how many PCs to capture?) -----
ElbowPlot(pbmc, ndims = 50)

# 6. Compute neighbors and clusters -----
pbmc <- FindNeighbors(pbmc, dims = 1:20)

pbmc <- FindClusters(pbmc, resolution = 0.095, group.singletons = F, algorithm = 1)

# 7. Build Cluster tree -----
pbmc <- BuildClusterTree(pbmc, reorder = T, reorder.numeric = T)

# 8. Run UMAP -----
pbmc <- RunUMAP(pbmc, dims = 1:20, n.neighbors = 20)

# 9. Plot UMAP colored by SingleR labels -----
DimPlot(pbmc,
        reduction = "umap",
        raster = F,
        label = T,
        repel = T) | DimPlot(pbmc,
                             reduction = "umap",
                             raster = F,
                             repel = T,
                             group.by = "cell_type") + ggtitle("Cell type annotation")

Can someone please advice me what changes to make to the code to separate the clusters as it looks like there is B cell expression everywhere. I have tried playing around with the resolution and algorithm used in FIndClusters but so far nothing works for me.