WNN analysis of CITE-seq, RNA + ADT

1. load libraries

2. Load Seurat Object

DimPlot(All_samples_Merged,group.by = "cell_line", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T)

DimPlot(All_samples_Merged,
        group.by = "SCT_snn_res.0.9", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T)


cluster_table <- table(Idents(All_samples_Merged))


DimPlot(All_samples_Merged, group.by = "predicted.celltype.l2", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T)

library(clustree)
clustree(All_samples_Merged, prefix = "SCT_snn_res.")

DimPlot(All_samples_Merged, group.by = "predicted.celltype.l1", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(All_samples_Merged, group.by = "predicted.celltype.l1", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = F)

DimPlot(All_samples_Merged, group.by = "predicted.celltype.l2", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(All_samples_Merged, group.by = "predicted.celltype.l2", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = F)

DimPlot(All_samples_Merged, group.by = "predicted.celltype.l2", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

table(All_samples_Merged$predicted.celltype.l2, All_samples_Merged$SCT_snn_res.0.9)

                   
                       0    1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16   17   18
  CD4 CTL              0    0    0    0    0    0    2    0    4    0    0    0    0    0    0    0    0    0    0
  CD4 Naive            0    0    0    0    0    0  595    0  100    0    0    1    0    0    0    0    6    0    4
  CD4 Proliferating 5124 5231 3823 2401 1748 2535    0 2547    0 1263 1329   20 1327  598   41  237    0  154    1
  CD4 TCM            978  158  501 1366   21   24 2170   22 1892  266  585 1859   41  111  406   57  235   35   61
  CD4 TEM              0    0    0    8    0    0   19    0   27    0    0   15    0    0    0    0    0    0    0
  CD8 Naive            6    0    2    0    0    0  304    0   61    0   19    0    1    3    2    0   12    0    3
  CD8 TCM              0    0    0   76    0    0  115    0  137   10    0   72    0    0    0    0    2    0    1
  CD8 TEM              0    0    0    0    0    0   27    0  182    8    0    0    0    0    0    0    0    0    0
  cDC2                 9    0  155    0    0   23    0    5    1    0   67    0    9   43    0   22    0    2    0
  dnT                  1    0    0    2    2    0    4    0   33    0    0    0    0    0    0    7   11    0    1
  gdT                  0    0    0    0    0    0    0    0   13    0    0    0    0    0    0    0    0    0    0
  HSPC               167    8  285    1    0  744    1  652    1    1   12    8  401   43    1   26    0    6    0
  ILC                  0    0    0    0    0    0    0    0    1    0    0    0    0    0    0    0    0    0    0
  MAIT                 0    0    0    0    0    0    4    0   50    0    0    0    0    0    0    0    2    0    0
  NK                   0    0    0    0    0    0    0    0   91    0    0    0    0    0    0    0    0    0    1
  NK Proliferating     7   11  162   20 1971    4    0    4    0  644   10    0    0    0    0   31    0    0    0
  Treg                11    0    2    1    0    0   50    0  103    0    0    0    0    1    0   20   12    0    5

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(1:20, 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

All_samples_Merged <- RunUMAP(All_samples_Merged, nn.name = "weighted.nn", reduction.name = "wnn.umap", reduction.key = "wnnUMAP_")

11:40:53 UMAP embedding parameters a = 0.9922 b = 1.112
11:40:54 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 20
11:40:55 Initializing from normalized Laplacian + noise (using RSpectra)
11:40:59 Commencing optimization for 200 epochs, with 1470228 positive edges
Using method 'umap'
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:41:16 Optimization finished

All_samples_Merged <- FindClusters(All_samples_Merged, graph.name = "wsnn", algorithm = 3, resolution = 0.5, verbose = FALSE)

p1 <- DimPlot(All_samples_Merged, reduction = 'wnn.umap', label = TRUE, repel = TRUE, label.size = 2.5) + NoLegend()
p2 <- DimPlot(All_samples_Merged, reduction = 'wnn.umap', group.by = 'predicted.celltype.l2', label = TRUE, repel = TRUE, label.size = 2.5) + NoLegend()
p1 + p2

DimPlot(All_samples_Merged, reduction = 'wnn.umap', group.by = "cell_line",label = TRUE, repel = TRUE, label.size = 2.5)

DimPlot(All_samples_Merged, reduction = 'wnn.umap', group.by = "wsnn_res.0.4",label = TRUE, repel = TRUE, label.size = 2.5)

DimPlot(All_samples_Merged, reduction = 'wnn.umap', group.by = 'predicted.celltype.l2', label = TRUE, repel = TRUE, label.size = 2.5)

All_samples_Merged <- RunUMAP(All_samples_Merged, reduction = 'pca', dims = 1:20, assay = 'RNA', 
              reduction.name = 'rna.umap', reduction.key = 'rnaUMAP_')

11:56:15 UMAP embedding parameters a = 0.9922 b = 1.112
11:56:15 Read 46976 rows and found 20 numeric columns
11:56:15 Using Annoy for neighbor search, n_neighbors = 30
11:56:15 Building Annoy index with metric = cosine, n_trees = 50
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:56:18 Writing NN index file to temp file /tmp/RtmpNFcsIF/filed20558ad9341
11:56:18 Searching Annoy index using 1 thread, search_k = 3000
11:56:29 Annoy recall = 100%
11:56:29 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
11:56:31 Initializing from normalized Laplacian + noise (using RSpectra)
11:56:38 Commencing optimization for 200 epochs, with 1992390 positive edges
Using method 'umap'
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:56:54 Optimization finished

All_samples_Merged <- RunUMAP(All_samples_Merged, reduction = 'apca', dims = 1:18, assay = 'ADT', 
              reduction.name = 'adt.umap', reduction.key = 'adtUMAP_')

11:56:54 UMAP embedding parameters a = 0.9922 b = 1.112
11:56:54 Read 46976 rows and found 18 numeric columns
11:56:54 Using Annoy for neighbor search, n_neighbors = 30
11:56:54 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:56:57 Writing NN index file to temp file /tmp/RtmpNFcsIF/filed20553965a86
11:56:57 Searching Annoy index using 1 thread, search_k = 3000
11:57:09 Annoy recall = 100%
11:57:09 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
11:57:11 Initializing from normalized Laplacian + noise (using RSpectra)
11:57:13 Commencing optimization for 200 epochs, with 2051920 positive edges
Using method 'umap'
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:57:28 Optimization finished

p3 <- DimPlot(All_samples_Merged, reduction = 'rna.umap', group.by = 'predicted.celltype.l2', label = TRUE, 
              repel = TRUE, label.size = 2.5) + NoLegend()
p4 <- DimPlot(All_samples_Merged, reduction = 'adt.umap', group.by = 'predicted.celltype.l2', label = TRUE, 
              repel = TRUE, label.size = 2.5) + NoLegend()
p3 + p4

DimPlot(All_samples_Merged, reduction = 'rna.umap', group.by = "cell_line",label = TRUE, repel = TRUE, label.size = 2.5)

DimPlot(All_samples_Merged, reduction = 'rna.umap', group.by = "seurat_clusters",label = TRUE, repel = TRUE, label.size = 2.5)

DimPlot(All_samples_Merged, reduction = 'rna.umap', group.by = 'predicted.celltype.l2', label = TRUE, 
              repel = TRUE, label.size = 2.5)

DimPlot(All_samples_Merged, reduction = 'adt.umap', group.by = "cell_line",label = TRUE, repel = TRUE, label.size = 2.5)

DimPlot(All_samples_Merged, reduction = 'adt.umap', group.by = "seurat_clusters",label = TRUE, repel = TRUE, label.size = 2.5)

DimPlot(All_samples_Merged, reduction = 'adt.umap', group.by = 'predicted.celltype.l2', label = TRUE, 
              repel = TRUE, label.size = 2.5) + NoLegend()

FeaturePlot(All_samples_Merged, features = c("adt_CD45RA","adt_CD45RO","adt_CD5","adt_CD274", "adt_CD95"),
                  reduction = 'wnn.umap', max.cutoff = 2, 
                  cols = c("lightgrey","darkgreen"), ncol = 3)

FeaturePlot(All_samples_Merged, features = c("adt_TCRab", "adt_CD7", "adt_CD3", "adt_CD28"),
                  reduction = 'wnn.umap', max.cutoff = 2, 
                  cols = c("lightgrey","darkgreen"), ncol = 3)

            
 FeaturePlot(All_samples_Merged, features = c("adt_CD26", "adt_CD44", "adt_CD62L","adt_CXCR3", "adt_CD127", "adt_CD45"),
                  reduction = 'wnn.umap', max.cutoff = 2, 
                  cols = c("lightgrey","darkgreen"), ncol = 3)     

 
 FeaturePlot(All_samples_Merged, features = c("adt_CCR6","adt_CCR7","adt_CCR8","adt_CCR10"),
                  reduction = 'wnn.umap', max.cutoff = 2, 
                  cols = c("lightgrey","darkgreen"), ncol = 3) 

 
 FeaturePlot(All_samples_Merged, features = c("adt_CD30","adt_CD40","adt_CCR4","adt_CD4" ,"adt_CD25"),
                  reduction = 'wnn.umap', max.cutoff = 2, 
                  cols = c("lightgrey","darkgreen"), ncol = 3) 

 
 FeaturePlot(All_samples_Merged, features = c("adt_PD1","adt_CD62L","adt_CD95","adt_TCRab", "adt_CXCR4"),
                  reduction = 'wnn.umap', max.cutoff = 2, 
                  cols = c("lightgrey","darkgreen"), ncol = 3)  

 FeaturePlot(All_samples_Merged, features = c("adt_CD2","adt_CD28","adt_CD127","adt_CD19"),
                  reduction = 'wnn.umap', max.cutoff = 2, 
                  cols = c("lightgrey","darkgreen"), ncol = 3)  

 
 
 FeaturePlot(All_samples_Merged, features = c("rna_TP53","rna_CARD11","rna_ARID1A","rna_FAS","rna_CCR4"), 
                  reduction = 'wnn.umap', max.cutoff = 3, ncol = 3)

 
 FeaturePlot(All_samples_Merged, features = c("rna_RHOA","rna_TNFRSF1B"),
                  reduction = 'wnn.umap', max.cutoff = 2, 
                  cols = c("lightgrey","darkgreen"), ncol = 3)  

 
 FeaturePlot(All_samples_Merged, features = c("rna_PLS3","rna_STAT4","rna_GATA3","rna_TRAIL","rna_CD1D"),
                  reduction = 'wnn.umap', max.cutoff = 2, 
                  cols = c("lightgrey","darkgreen"), ncol = 3)  

Warning: The following features could not be found rna_TRAILWarning: The following requested variables were not found: rna_TRAIL

 
 FeaturePlot(All_samples_Merged, features = c("rna_KIR3DL2","rna_NKp46","rna_IL2RA","rna_TOX","rna_STAT5A"),
                  reduction = 'wnn.umap', max.cutoff = 2, 
                  cols = c("lightgrey","darkgreen"), ncol = 3)  

Warning: The following features could not be found rna_NKp46Warning: The following requested variables were not found: rna_NKp46

 
 FeaturePlot(All_samples_Merged, features = c("rna_MYC","rna_MNT","rna_EPHA4","rna_DNM3","rna_TWIST1"),
                  reduction = 'wnn.umap', max.cutoff = 2, 
                  cols = c("lightgrey","darkgreen"), ncol = 3)  

 
 FeaturePlot(All_samples_Merged, features = c("rna_TRAF2","rna_SELL","rna_miR21"),
                  reduction = 'wnn.umap', max.cutoff = 2, 
                  cols = c("lightgrey","darkgreen"), ncol = 3)  

Warning: The following features could not be found rna_miR21Warning: The following requested variables were not found: rna_miR21

 
 FeaturePlot(All_samples_Merged, features = c("rna_TRAF2","rna_SELL","rna_miR21"),
                  reduction = 'wnn.umap', max.cutoff = 2, 
                  cols = c("lightgrey","darkgreen"), ncol = 3)

Warning: The following features could not be found rna_miR21Warning: The following requested variables were not found: rna_miR21

 
 FeaturePlot(All_samples_Merged, features = c("rna_FCL3","rna_PDCD1","rna_CXCL13"),
                  reduction = 'wnn.umap', max.cutoff = 2, 
                  cols = c("lightgrey","darkgreen"), ncol = 3)  

Warning: The following features could not be found rna_FCL3Warning: The following requested variables were not found: rna_FCL3

 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()

 
 table(All_samples_Merged$predicted.celltype.l2, All_samples_Merged$wsnn_res.0.4)

                   
                       0    1   10   11   12   13   14   15   16   17    2    3    4    5    6    7    8    9
  CD4 CTL              0    0    0    0    0    1    0    0    0    0    0    0    0    0    0    5    0    0
  CD4 Naive            0    0    0    0    0   21    0    0    0    4    0    0    1    0    0    1  679    0
  CD4 Proliferating 5113 3002 1293 1302  578    0  280   31  144    0 5242 3797 1491 2579 2607    0    0  920
  CD4 TCM            976  286  602   46  114  550   82  428   40   61  132  477 2172   29   25 2093 1656 1019
  CD4 TEM              0    0    0    0    0    1    0    0    0    0    0    0   23    0    0   45    0    0
  CD8 Naive            6    0   19    1    3   41    0    2    0    3    0    2    0    0    0   20  316    0
  CD8 TCM              0   10    0    0    0    9    0    0    0    1    0    0   94    1    0  232   13   53
  CD8 TEM              0    8    0    0    0    0    0    0    0    0    0    0    0    0    0  209    0    0
  cDC2                 9    0   68    9   43    0   25    0    2    0    0  148    0   27    5    0    0    0
  dnT                  0    0    0    0    0   33    7    0    0    1    0    1    3    0    0   14    2    0
  gdT                  0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   13    0    0
  HSPC               161    1   10  399   36    2   35    3    5    0    9  285    0  757  653    0    1    0
  ILC                  0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    1    0    0
  MAIT                 0    0    0    0    0    2    0    0    0    0    0    0    0    0    0   54    0    0
  NK                   0    0    0    0    0    0    0    0    0    1    0    0    0    0    0   91    0    0
  NK Proliferating     7 2611    8    0    0    0   32    0    0    0   11  166   11    5    5    0    0    8
  Treg                 3    0    0    0    1   29   30    0    0    4    0    1    1    0    0   97   39    0

 
 clustree(All_samples_Merged, prefix = "wsnn_res.")

5. Save the Seurat object as an Robj file

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