Resolution-0.1-to-1-Notebook

1. load libraries

2. Load Seurat Object

#Load Seurat Object merged from cell lines and a control(PBMC) after filtration
SS_All_samples_Merged <- load("Different_Resolution_on_my_UMAP-0.1-to-1.Robj")

All_samples_Merged

An object of class Seurat 
63098 features across 49193 samples within 3 assays 
Active assay: SCT (26469 features, 3000 variable features)
 3 layers present: counts, data, scale.data
 2 other assays present: RNA, ADT
 2 dimensional reductions calculated: pca, umap

3. Normalize data

# Apply SCTransform
#All_samples_Merged <- SCTransform(All_samples_Merged, verbose = TRUE)
                                      

3. Perform PCA

# Variables_genes <- All_samples_Merged@assays$SCT@var.features
# 
# # Exclude genes starting with "HLA-" or "Xist"
# Variables_genes_after_exclusion <- Variables_genes[!grepl("^HLA-|^Xist", Variables_genes)]
# 
# 
# # These are now standard steps in the Seurat workflow for visualization and clustering
# All_samples_Merged <- RunPCA(All_samples_Merged,
#                         features = Variables_genes_after_exclusion,
#                         do.print = TRUE, 
#                         pcs.print = 1:5, 
#                         genes.print = 15)

# determine dimensionality of the data
ElbowPlot(All_samples_Merged)

4. Clustering

# All_samples_Merged <- FindNeighbors(All_samples_Merged, 
#                                 dims = 1:8, 
#                                 verbose = FALSE)
# 
# # understanding resolution
# All_samples_Merged <- FindClusters(All_samples_Merged, 
#                                     resolution = c(0.1, 0.2, 0.3, 0.4, 0.5, 0.7, 1, 1.2, 1.5, 1.7, 2))

# non-linear dimensionality reduction --------------
# All_samples_Merged <- RunUMAP(All_samples_Merged, 
#                           dims = 1:8,
#                           verbose = FALSE)
                                  

# note that you can set `label = TRUE` or use the LabelClusters function to help label
# individual clusters
UMAPPlot(All_samples_Merged,group.by = "cell_line", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T)

UMAPPlot(All_samples_Merged,
        group.by = "SCT_snn_res.0.1", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T)

UMAPPlot(All_samples_Merged,
        group.by = "SCT_snn_res.0.2", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T)

UMAPPlot(All_samples_Merged,
        group.by = "SCT_snn_res.0.3", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T)

UMAPPlot(All_samples_Merged,
        group.by = "SCT_snn_res.0.4", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T)

UMAPPlot(All_samples_Merged,
        group.by = "SCT_snn_res.0.5", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T)

UMAPPlot(All_samples_Merged,
        group.by = "SCT_snn_res.0.7", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T)

UMAPPlot(All_samples_Merged,
        group.by = "SCT_snn_res.1", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T)

cluster_table <- table(Idents(All_samples_Merged))

cluster_table

   0    1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16   17   18   19   20 
5246 4353 3799 3712 3690 3542 3490 2851 2511 2436 2414 2105 1983 1583 1172 1103 1093  986  837  200   87