1. load libraries

2. Load Seurat Object

  All_samples_Merged

An object of class Seurat 
62625 features across 46976 samples within 6 assays 
Active assay: SCT (25901 features, 3000 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
 4 dimensional reductions calculated: pca, umap, integrated_dr, ref.umap

3. Data PREPERATION

DefaultAssay(All_samples_Merged) <- 'ADT'

VariableFeatures(All_samples_Merged) <- rownames(All_samples_Merged[["ADT"]])

# we will use all ADT features for dimensional reduction
# we set a dimensional reduction name to avoid overwriting the 
All_samples_Merged <- NormalizeData(All_samples_Merged, normalization.method = 'CLR', margin = 2) %>% 
  ScaleData() %>% RunPCA(reduction.name = 'apca', npcs =28, maxit = 5000)
Normalizing across cells

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Centering and scaling data matrix

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Warning: You're computing too large a percentage of total singular values, use a standard svd instead.Warning: Requested number is larger than the number of available items (28). Setting to 28.Warning: Requested number is larger than the number of available items (28). Setting to 28.Warning: Requested number is larger than the number of available items (28). Setting to 28.Warning: Requested number is larger than the number of available items (28). Setting to 28.Warning: Requested number is larger than the number of available items (28). Setting to 28.PC_ 1 
Positive:  CD5, CD274, TCRab, CD7, CD3, CD28, CD45RO, CD26, CD44, CD62L 
       CXCR3, CD127, CD45, CCR8 
Negative:  CD25, CD30, CD2, CD95, CD45RA, CD4, CCR6, CCR4, CXCR4, CD40 
       CD19, CCR7, PD1, CCR10 
PC_ 2 
Positive:  CD45, CD44, CD45RO, CD4, CD3, CD40, CCR4, CD45RA, CD26, CD95 
       CD2, CD5, CD62L, CD28 
Negative:  CD127, CD19, PD1, CCR7, CCR10, CXCR3, CCR8, CCR6, CD7, CD274 
       CD30, CXCR4, CD25, TCRab 
PC_ 3 
Positive:  CD62L, TCRab, CD28, CD3, CD4, CXCR4, CCR7, CD45RA, CD127, CD5 
       PD1, CCR4, CD95, CD2 
Negative:  CD26, CXCR3, CD45RO, CD274, CD40, CD44, CCR10, CCR6, CD7, CD30 
       CD19, CD25, CD45, CCR8 
PC_ 4 
Positive:  CCR4, CD5, CD274, CD4, CD30, CCR6, CD44, CD28, CD25, CD45RA 
       CD19, CCR10, CXCR3, PD1 
Negative:  CD40, CD45, CD2, CD62L, CD7, CD26, CXCR4, CCR7, CD127, CD95 
       CD45RO, CCR8, CD3, TCRab 
PC_ 5 
Positive:  CD45RO, CD28, CCR8, CD26, CCR4, PD1, CD4, CCR10, CD40, CD127 
       CD19, TCRab, CD2, CCR7 
Negative:  CD7, CD45RA, CD5, CD44, CCR6, CXCR3, CXCR4, CD274, CD62L, CD30 
       CD3, CD95, CD45, CD25 
Warning: Key 'PC_' taken, using 'apca_' instead
# Identify multimodal neighbors. These will be stored in the neighbors slot, 
# and can be accessed using bm[['weighted.nn']]
# The WNN graph can be accessed at bm[["wknn"]], 
# and the SNN graph used for clustering at bm[["wsnn"]]
# Cell-specific modality weights can be accessed at bm$RNA.weight

All_samples_Merged <- FindMultiModalNeighbors(
  All_samples_Merged, reduction.list = list("pca", "apca"), 
  dims.list = list(1:30, 1:18), modality.weight.name = "RNA.weight"
)
Calculating cell-specific modality weights
Finding 20 nearest neighbors for each modality.

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Calculating kernel bandwidths

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Warning: The number of provided modality.weight.name is not equal to the number of modalities. SCT.weight ADT.weight are used to store the modality weightsFinding multimodal neighbors

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Constructing multimodal KNN graph
Constructing multimodal SNN graph

4. Visualization RNA+ADT


p5 <- FeaturePlot(All_samples_Merged, features = c("adt_CD45RA","adt_CD45RO","adt_CD90"),
                  reduction = 'wnn.umap', max.cutoff = 2, 
                  cols = c("lightgrey","darkgreen"), ncol = 3)
Warning: The following features could not be found adt_CD90Warning: The following requested variables were not found: adt_CD90
p6 <- FeaturePlot(All_samples_Merged, features = c("rna_TRDC","rna_MPO","rna_AVP"), 
                  reduction = 'wnn.umap', max.cutoff = 3, ncol = 3)
p5 / p6


 VlnPlot(All_samples_Merged, features = "SCT.weight", group.by = 'predicted.celltype.l2', sort = TRUE, pt.size = 0.1) +
  NoLegend()

 VlnPlot(All_samples_Merged, features = "ADT.weight", group.by = 'predicted.celltype.l2', sort = TRUE, pt.size = 0.1) +
  NoLegend()

5. Save the Seurat object as an Robj file


save(All_samples_Merged, file = "../0-imp_OBJ_SS/All_samples_Merged_WNN.Robj")
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dCA9IFRSVUUsIHB0LnNpemUgPSAwLjEpICsKICBOb0xlZ2VuZCgpCgpgYGAKIyA1LiBTYXZlIHRoZSBTZXVyYXQgb2JqZWN0IGFzIGFuIFJvYmogZmlsZQpgYGB7ciBzYXZlUk9CSn0KCnNhdmUoQWxsX3NhbXBsZXNfTWVyZ2VkLCBmaWxlID0gIi4uLzAtaW1wX09CSl9TUy9BbGxfc2FtcGxlc19NZXJnZWRfV05OLlJvYmoiKQoKCmBgYAoKCgoK