I used SCTnormalization and Seurat integration to integrate 3 scRNA-seq datasets. After manual annotation using RNA assay, I have one particular cluster of cells with overexpressed different T cells markers.
So, to find heterogeneity of T cells I subclustered that particular cluster:
hcc_alv_Tcells <- subset(hcc_alv_sct, subset = cell_type == "T cells") hcc_alv_Tcells <- CreateSeuratObject(hcc_alv_Tcells@assays$RNA@counts) |> NormalizeData(normalization.method = "LogNormalize", scale.factor = 10000) |> FindVariableFeatures(selection.method = "vst", nfeatures = 2000) |> ScaleData(features = rownames(hcc_alv_Tcells)) hcc_alv_Tcells <- Seurat::RunPCA(hcc_alv_Tcells, verbose = FALSE) hcc_alv_Tcells <- Seurat::RunUMAP(hcc_alv_Tcells, reduction = "pca", dims = 1:15) hcc_alv_Tcells <- Seurat::FindNeighbors(hcc_alv_Tcells, reduction = "pca", dims = 1:15) hcc_alv_Tcells <- Seurat::FindClusters(hcc_alv_Tcells, resolution = 0.1)
The resulting clusters were separated by samples:
What is the best way to subcluster T cells cluster to find T cell subtypes in this case?