Data Visualization (Ragas)-L5

1. load libraries

2. Load Seurat Object

L5 <- readRDS("../0-RDS_Cell_lines/L5_clustered.rds")

L5 <- NormalizeData(L5, normalization.method = "LogNormalize", scale.factor = 10000)

Avis : The `slot` argument of `SetAssayData()` is deprecated as of SeuratObject 5.0.0.
Please use the `layer` argument instead.Avis : The `slot` argument of `GetAssayData()` is deprecated as of SeuratObject 5.0.0.
Please use the `layer` argument instead.Performing log-normalization
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UMAP colored by cell type and expression - dittoDimPlot

spe <- L5

 DefaultAssay(spe) = "RNA"

library(Ragas)

Registered S3 methods overwritten by 'treeio':
  method              from    
  MRCA.phylo          tidytree
  MRCA.treedata       tidytree
  Nnode.treedata      tidytree
  Ntip.treedata       tidytree
  ancestor.phylo      tidytree
  ancestor.treedata   tidytree
  child.phylo         tidytree
  child.treedata      tidytree
  full_join.phylo     tidytree
  full_join.treedata  tidytree
  groupClade.phylo    tidytree
  groupClade.treedata tidytree
  groupOTU.phylo      tidytree
  groupOTU.treedata   tidytree
  is.rooted.treedata  tidytree
  nodeid.phylo        tidytree
  nodeid.treedata     tidytree
  nodelab.phylo       tidytree
  nodelab.treedata    tidytree
  offspring.phylo     tidytree
  offspring.treedata  tidytree
  parent.phylo        tidytree
  parent.treedata     tidytree
  root.treedata       tidytree
  rootnode.phylo      tidytree
  sibling.phylo       tidytree

RunDimPlot(object = spe, group.by = "SCT_snn_res.0.2")

RunDimPlot(object = spe, group.by = "SCT_snn_res.0.3")

DimPlot(spe, reduction = "umap", group.by = "cell_line",label = T, label.box = T)

DimPlot(spe, reduction = "umap", group.by = "SCT_snn_res.0.3",label = T, label.box = T)

DimPlot(spe, reduction = "umap", group.by = "predicted.celltype.l2",label = T, label.box = T, repel = T)

FeaturePlot(spe, features = "CCR7", reduction = "umap") ## TCM

Avis : The `slot` argument of `FetchData()` is deprecated as of SeuratObject 5.0.0.
Please use the `layer` argument instead.

FeaturePlot(spe, features = "GZMK", reduction = "umap") ## Th1

Avis : All cells have the same value (0) of “GZMK”

FeaturePlot(spe, features = "IL17RB", reduction = "umap") ## Th2

FeaturePlot(spe, features = "CTSH", reduction = "umap") ## Th17

FeaturePlot(spe, features = "CCR10", reduction = "umap") ## Th22

FeaturePlot(spe, features = c("IL2RA", "FOXP3"), reduction = "umap") ## Th22

NA
NA

Create a Pi object

#rm(All_samples_Merged)

my.pbmc.pi <- CreatePostIntegrationObject(object = spe)

Post-integration object created

RunDimPlot(object = my.pbmc.pi, group.by = "SCT_snn_res.0.3")

my.pbmc.pi

An object of class Pi 
6 fields in the object: seurat.obj, exp.freq, markers, ds, cell.prop, parent.meta.data.
The following field has been processed:
    seurat.obj: A Seurat object of 36601 features and 6022 cells.
        6 assays: RNA, ADT, prediction.score.celltype.l1, prediction.score.celltype.l2, prediction.score.celltype.l3, SCT, and 5 reductions: integrated_dr, ref.umap, pca, umap, harmony
Metadata from the parent object provided? No 
Subclusters integrated? No

2. Marker gene identification

#rm(spe)

my.pbmc.pi <- RunFindAllMarkers(my.pbmc.pi, 
                               logfc.threshold = 0.1,
                               min.pct = 0.1,
                               min.diff.pct = 0.2,
                               only.pos = TRUE,
                                assay = "RNA",
                               ident = "SCT_snn_res.0.3")

Calculating cluster 0
Avis : `PackageCheck()` was deprecated in SeuratObject 5.0.0.
Please use `rlang::check_installed()` instead.Calculating cluster 1
Calculating cluster 2
Calculating cluster 3
Calculating cluster 4
Calculating cluster 5
Calculating cluster 6

3. Marker gene Visualization

p1 <- RunMatrixPlot(my.pbmc.pi,
              markers.key = "Markers|SCT_snn_res.0.3|AllMarkers|test.use=wilcox", 
              column.anno.name.rot = 45, 
              heatmap.height = 8)

Set active identity to SCT_snn_res.0.3
Performing relative-counts-normalization
Centering and scaling data matrix

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p1