Data Visualization (Ragas)-All_samples_findAllMarkers

1. load libraries

2. Load Seurat Object

pbmc

An object of class Seurat 
62900 features across 49305 samples within 6 assays 
Active assay: SCT (26176 features, 2912 variable features)
 3 layers present: counts, data, scale.data
 5 other assays present: RNA, ADT, prediction.score.celltype.l1, prediction.score.celltype.l2, prediction.score.celltype.l3
 5 dimensional reductions calculated: integrated_dr, ref.umap, pca, umap, harmony

3. The post-integration (Pi) object

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 26176 features and 49305 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

4. run UMAP and check clusters of all the cells in the object.

RunDimPlot(object = my.pbmc.pi)

4. run UMAP and check Prediction of all the cells in the object.

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

5. Marker analysis and matrix plot(Clusters)

my.pbmc.pi <- RunFindAllMarkers(my.pbmc.pi,
                                ident = "seurat_clusters",logfc.threshold = 0.25,min.pct = 0.25,   only.pos = TRUE,return.thresh = 0.05)

Calculating cluster 0
Calculating cluster 1
Calculating cluster 2
Calculating cluster 3
Calculating cluster 4
Calculating cluster 5
Calculating cluster 6
Calculating cluster 7
Calculating cluster 8
Calculating cluster 9
Calculating cluster 10
Calculating cluster 11
Calculating cluster 12
Calculating cluster 13

##. Matrix plot(Clusters)

6. Marker analysis and matrix plot (Predictions)

my.pbmc.pi <- RunFindAllMarkers(my.pbmc.pi,
                                ident = "Prediction",logfc.threshold = 0.25,min.pct = 0.25,   only.pos = TRUE,return.thresh = 0.05)

Calculating cluster CD4 Temra
Calculating cluster CD4 Tcm
Calculating cluster CD4 Tc
Calculating cluster CD4 Tem
Calculating cluster CD4 Trm cell-death
Calculating cluster CD4 Tisg
Calculating cluster CD4 Th17
Calculating cluster CD4 proliferation
Calculating cluster CD4 Treg naive-like
Calculating cluster None T
Calculating cluster CD4 Trm
Calculating cluster CD4 Tfh
Calculating cluster CD4 Treg
Calculating cluster CD4 Tn
Calculating cluster CD4 Tex
Calculating cluster CD4 activated
Calculating cluster CD4 Tisg cell-death
Calculating cluster CD4 Tstr
Calculating cluster CD4 Tn adhesion

##. Matrix plot (Predictions)

p2 <- RunMatrixPlot(my.pbmc.pi,
              markers.key = "Markers|Prediction|AllMarkers|test.use=wilcox", 
              column.anno.name.rot = 45, 
              heatmap.height = 10, heatmap.width = 25,)

Set active identity to Prediction
Performing relative-counts-normalization
Centering and scaling data matrix

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p2 

7. Marker analysis and matrix plot (Predictions)

my.pbmc.pi <- RunFindAllMarkers(my.pbmc.pi,
                                ident = "Prediction",logfc.threshold = 0.5,min.pct = 0.5,   only.pos = TRUE,return.thresh = 0.01)

Calculating cluster CD4 Temra
Calculating cluster CD4 Tcm
Calculating cluster CD4 Tc
Calculating cluster CD4 Tem
Calculating cluster CD4 Trm cell-death
Calculating cluster CD4 Tisg
Calculating cluster CD4 Th17
Calculating cluster CD4 proliferation
Calculating cluster CD4 Treg naive-like
Calculating cluster None T
Calculating cluster CD4 Trm
Calculating cluster CD4 Tfh
Calculating cluster CD4 Treg
Calculating cluster CD4 Tn
Calculating cluster CD4 Tex
Calculating cluster CD4 activated
Calculating cluster CD4 Tisg cell-death
Calculating cluster CD4 Tstr
Calculating cluster CD4 Tn adhesion
PiData Markers|Prediction|AllMarkers|test.use=wilcox already exisits. Overwriting...

##. Matrix plot (Predictions)