Data Visualization (Ragas)-L3

1. load libraries

2. Load Seurat Object

L3 <- readRDS("../0-RDS_Cell_lines/L3_clustered.rds")

L3 <- NormalizeData(L3, normalization.method = "LogNormalize", scale.factor = 10000)

Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

UMAP colored by cell type and expression - dittoDimPlot

spe <- L3

 DefaultAssay(spe) = "RNA"

library(Ragas)


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.0.2",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

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("IL3RA", "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.0.2")

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 6428 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.0.2")

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

2. Marker gene Visualization

p1 <- RunMatrixPlot(my.pbmc.pi,
              markers.key = "Markers|SCT_snn_res.0.2|AllMarkers|test.use=wilcox", 
              column.anno.name.rot = 45, 
              heatmap.height = 8)

Set active identity to SCT_snn_res.0.2
Performing relative-counts-normalization
Centering and scaling data matrix

  |                                                                                                                               
  |                                                                                                                         |   0%
  |                                                                                                                               
  |=========================================================================================================================| 100%

p1