1 Load libraries

2 Load Annotated Healthy Object

print(table(healthy$predicted.celltype.l2))


             ASDC    B intermediate          B memory           B naive         CD14 Mono         CD16 Mono           CD4 CTL         CD4 Naive 
                9               441               169               764              4117               290                12              2107 
CD4 Proliferating           CD4 TCM           CD4 TEM         CD8 Naive CD8 Proliferating           CD8 TCM           CD8 TEM              cDC1 
               13              9527               159              1488                 1               370               706                18 
             cDC2               dnT               gdT              HSPC               ILC              MAIT                NK  NK Proliferating 
              181                13               119                27                 3               325               540                 3 
    NK_CD56bright               pDC       Plasmablast          Platelet              Treg 
               25                92                28                57               230

3 Visualize Healthy cells Annotation

# Final validation tables
cat("=== ANNOTATION SUMMARY ===\n")
=== ANNOTATION SUMMARY ===
print(table(healthy$predicted.celltype.l1, healthy$dataset))
         
          CD4T_10x_S1 CD4T_10x_S2 CD4T_lab
  B               924         479        0
  CD4 T          3510        3445     5105
  CD8 T          1370        1200        1
  DC              204          96        0
  Mono           3073        1332        0
  NK              450         118        0
  other            46          41        0
  other T         290         150        0
print(table(healthy$predicted.celltype.l2, healthy$dataset))
                   
                    CD4T_10x_S1 CD4T_10x_S2 CD4T_lab
  ASDC                        5           4        0
  B intermediate            141         300        0
  B memory                   88          81        0
  B naive                   682          82        0
  CD14 Mono                2956        1161        0
  CD16 Mono                 119         171        0
  CD4 CTL                     1           0       11
  CD4 Naive                1367         381      359
  CD4 Proliferating           9           4        0
  CD4 TCM                  2011        2861     4655
  CD4 TEM                    29          55       75
  CD8 Naive                 987         501        0
  CD8 Proliferating           0           1        0
  CD8 TCM                   184         185        1
  CD8 TEM                   203         503        0
  cDC1                       13           5        0
  cDC2                      129          52        0
  dnT                         7           6        0
  gdT                        74          45        0
  HSPC                       12          15        0
  ILC                         3           0        0
  MAIT                      210         115        0
  NK                        435         105        0
  NK Proliferating            2           1        0
  NK_CD56bright              13          12        0
  pDC                        57          35        0
  Plasmablast                12          16        0
  Platelet                   31          26        0
  Treg                       87         138        5
print(table(healthy$predicted.celltype.l3, healthy$dataset))
                       
                        CD4T_10x_S1 CD4T_10x_S2 CD4T_lab
  ASDC_mDC                        2           3        0
  ASDC_pDC                        2           1        0
  B intermediate kappa           68          46        0
  B intermediate lambda          77         254        0
  B memory kappa                 44          44        0
  B memory lambda                42          32        0
  B naive kappa                 628          84        0
  B naive lambda                 52           2        0
  CD14 Mono                    2956        1162        0
  CD16 Mono                     119         172        0
  CD4 CTL                         3           0       11
  CD4 Naive                    1371         382      363
  CD4 Proliferating               9           4        0
  CD4 TCM_1                    1519        1863     4181
  CD4 TCM_2                     113         148      248
  CD4 TCM_3                     371         856      226
  CD4 TEM_1                      12          26        8
  CD4 TEM_2                      19          11       13
  CD4 TEM_3                       2           7       49
  CD8 Naive                     974         376        0
  CD8 Naive_2                    16         121        0
  CD8 Proliferating               0           1        0
  CD8 TCM_1                     116         118        1
  CD8 TCM_2                      45          39        0
  CD8 TCM_3                      16          12        0
  CD8 TEM_1                      69         273        2
  CD8 TEM_2                      64         179        0
  CD8 TEM_3                      19          12        0
  CD8 TEM_4                      15           0        0
  CD8 TEM_5                       1           5        0
  CD8 TEM_6                      32          38        0
  cDC1                           13           5        0
  cDC2_1                         43          25        0
  cDC2_2                         86          26        0
  dnT_1                           0           1        0
  dnT_2                           7           7        0
  gdT_1                          59          16        0
  gdT_3                          17          41        1
  HSPC                           12          15        0
  ILC                             3           0        0
  MAIT                          211         115        0
  NK Proliferating                2           1        0
  NK_1                            5           1        0
  NK_2                          375         102        0
  NK_3                           23           1        0
  NK_4                           32           2        0
  NK_CD56bright                  13          12        0
  pDC                            58          35        0
  Plasma                         12          16        0
  Platelet                       31          26        0
  Treg Memory                    58         134        3
  Treg Naive                     31           9        0
# Multi-annotation consensus plot
DimPlot(healthy, group.by = "singler.hpca", label = TRUE, repel = TRUE) + ggtitle("HPCA")

DimPlot(healthy, group.by = "singler.immune", label = TRUE, repel = TRUE) + ggtitle("Immune")

DimPlot(healthy, group.by = "predicted.celltype.l1", label = TRUE) + ggtitle("Azimuth level1")

DimPlot(healthy, group.by = "predicted.celltype.l2", label = TRUE) + ggtitle("Azimuth level1")

DimPlot(healthy, group.by = "predicted.celltype.l3", label = TRUE) + ggtitle("Azimuth level1")

3.1 # Subset CD4+ T-cells Only

# ============================================================
# Verify: no contamination remains
# ============================================================
cat("=== AFTER STRICT L2 + L3 CONSENSUS FILTER ===\n")

=== AFTER STRICT L2 + L3 CONSENSUS FILTER ===
cat("Total CD4 cells retained:", ncol(healthy_cd4), "\n\n")
Total CD4 cells retained: 12034 
cat("--- Azimuth L2 distribution ---\n")
--- Azimuth L2 distribution ---
print(table(healthy_cd4$predicted.celltype.l2, healthy_cd4$dataset))
                   
                    CD4T_10x_S1 CD4T_10x_S2 CD4T_lab
  CD4 CTL                     1           0       11
  CD4 Naive                1367         381      359
  CD4 Proliferating           9           4        0
  CD4 TCM                  2010        2856     4654
  CD4 TEM                    29          51       73
  Treg                       87         137        5
cat("\n--- Azimuth L3 distribution ---\n")

--- Azimuth L3 distribution ---
print(table(healthy_cd4$predicted.celltype.l3, healthy_cd4$dataset))
                   
                    CD4T_10x_S1 CD4T_10x_S2 CD4T_lab
  CD4 CTL                     1           0       11
  CD4 Naive                1370         382      363
  CD4 Proliferating           9           4        0
  CD4 TCM_1                1518        1859     4181
  CD4 TCM_2                 113         148      248
  CD4 TCM_3                 370         849      226
  CD4 TEM_1                  12          26        8
  CD4 TEM_2                  19          11       13
  CD4 TEM_3                   2           7       49
  Treg Memory                58         134        3
  Treg Naive                 31           9        0
# Visual check
p1 <- DimPlot(healthy_cd4, group.by = "predicted.celltype.l2",
              label = TRUE, repel = TRUE, label.size = 3) +
      NoLegend() + ggtitle("CD4 T-cells — Azimuth L2")

p2 <- DimPlot(healthy_cd4, group.by = "predicted.celltype.l3",
              label = TRUE, repel = TRUE, label.size = 3) +
      NoLegend() + ggtitle("CD4 T-cells — Azimuth L3")

p3 <- DimPlot(healthy_cd4, group.by = "dataset") +
      ggtitle("By Dataset")

(p1 | p2) / p3


# Final cell count per dataset
cat("\n--- Final cell counts ---\n")

--- Final cell counts ---
cat("CD4T_10x_S1 :", sum(healthy_cd4$dataset == "CD4T_10x_S1"), "\n")
CD4T_10x_S1 : 3503 
cat("CD4T_10x_S2 :", sum(healthy_cd4$dataset == "CD4T_10x_S2"), "\n")
CD4T_10x_S2 : 3429 
cat("CD4T_lab    :", sum(healthy_cd4$dataset == "CD4T_lab"),    "\n")
CD4T_lab    : 5102

3.2 After-filter QC Tables (Azimuth)

cat("=== ANNOTATION SUMMARY ===\n")
=== ANNOTATION SUMMARY ===
cat("CD4 after L1+L2+L3 consensus:", ncol(healthy_cd4), "\n")
CD4 after L1+L2+L3 consensus: 12034 
print(table(healthy_cd4$predicted.celltype.l1, healthy_cd4$dataset))
       
        CD4T_10x_S1 CD4T_10x_S2 CD4T_lab
  CD4 T        3503        3423     5102
  CD8 T           0           6        0
print(table(healthy_cd4$predicted.celltype.l2, healthy_cd4$dataset))
                   
                    CD4T_10x_S1 CD4T_10x_S2 CD4T_lab
  CD4 CTL                     1           0       11
  CD4 Naive                1367         381      359
  CD4 Proliferating           9           4        0
  CD4 TCM                  2010        2856     4654
  CD4 TEM                    29          51       73
  Treg                       87         137        5
print(table(healthy_cd4$predicted.celltype.l3, healthy_cd4$dataset))
                   
                    CD4T_10x_S1 CD4T_10x_S2 CD4T_lab
  CD4 CTL                     1           0       11
  CD4 Naive                1370         382      363
  CD4 Proliferating           9           4        0
  CD4 TCM_1                1518        1859     4181
  CD4 TCM_2                 113         148      248
  CD4 TCM_3                 370         849      226
  CD4 TEM_1                  12          26        8
  CD4 TEM_2                  19          11       13
  CD4 TEM_3                   2           7       49
  Treg Memory                58         134        3
  Treg Naive                 31           9        0

3.3 MARKER VALIDATION: Check NK / Mono / CD8 contamination

# ============================================================
# MARKER VALIDATION: Check NK / Mono / CD8 contamination
# ============================================================

DefaultAssay(healthy_cd4) <- "RNA"

# Key marker panel
contamination_markers <- list(
  CD4_markers  = c("CD4", "CD3D", "CD3E"),
  CD8_markers  = c("CD8A", "CD8B"),
  NK_markers   = c("NKG7", "GNLY", "NCAM1", "KLRB1"),
  Mono_markers = c("CD14", "LYZ", "S100A8", "CST3")
)

# 1. VlnPlot: expression distribution per Azimuth L2 cluster
VlnPlot(healthy_cd4,
        features = c("CD4", "CD8A", "NKG7", "CD14", "GNLY", "LYZ", "CD14"),
        group.by = "predicted.celltype.l2",
        pt.size  = 0, ncol = 3) +
  plot_annotation(title = "Contamination Check — Key Markers per CD4 Subtype")


# 2. FeaturePlot: spatial distribution on UMAP
FeaturePlot(healthy_cd4,
            features = c("CD4", "CD8A", "NKG7", "CD14", "GNLY", "NCAM1"),reduction = "umap",
            ncol = 3, max.cutoff = "q95") +
  plot_annotation(title = "Marker UMAP — Contamination Check")


# 3. Quantitative: mean expression per L2 cluster
marker_means <- AverageExpression(
  healthy_cd4,
  features = c("CD4","CD8A","CD8B","NKG7","GNLY","NCAM1","CD14","LYZ","S100A8"),
  group.by = "predicted.celltype.l2",
  assays   = "RNA"
)$RNA

print(round(marker_means, 3))
9 x 6 sparse Matrix of class "dgCMatrix"
       CD4 CTL CD4 Naive CD4 Proliferating CD4 TCM CD4 TEM  Treg
S100A8  19.708     0.142             0.102   0.232   0.051 0.323
GNLY    63.721     0.011             0.026   0.098   0.519 0.021
CD8A     .         0.006             2.169   0.071   0.553 0.038
CD8B     .         0.092             1.088   0.091   0.430 0.050
CD14     0.147     0.026             .       0.033   .     0.016
NCAM1    .         0.002             .       0.002   .     0.006
CD4      0.147     1.194             0.869   1.093   1.147 1.099
LYZ      1.471     0.071             0.024   0.120   0.038 0.214
NKG7    11.440     0.013             3.125   0.049   1.111 .

4 Save the object and move to part3

# SAVE for trajectory RMD
saveRDS(healthy, file = "healthy_CD4_Subsetted_full_annotated_harmony.rds")

```

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