use Annotated Robj including PBMC10x to remove NonCD4Tcells from Control and keep just CD4Tcells
Nasir Mahmood Abbasi
2025-01-20
##. In this script I will remove Non T cells from PBMC
1. load libraries
2. Load Seurat Object
#Load Seurat Object merged from cell lines and a control(PBMC) after filtration
load("../0-R_Objects/All_Samples_Merged_with_10x_Azitmuth_Annotated.robj")
All_samples_MergedAn object of class Seurat
36752 features across 59355 samples within 5 assays
Active assay: RNA (36601 features, 0 variable features)
2 layers present: data, counts
4 other assays present: ADT, prediction.score.celltype.l1, prediction.score.celltype.l2, prediction.score.celltype.l3
2 dimensional reductions calculated: integrated_dr, ref.umapCell type Distribution to check clusters
# We can apply it later on R obj to get these tables to compare it to decide about resolution.
# Azimuth l1
janitor::tabyl(All_samples_Merged@meta.data, predicted.celltype.l1, cell_line) predicted.celltype.l1 L1 L2 L3 L4 L5 L6 L7 PBMC PBMC_10x
B 0 0 12 131 35 85 121 1093 957
CD4 T 5771 3124 6351 5967 4914 4575 4634 5448 3639
CD8 T 13 25 0 1 4 5 3 814 1379
DC 0 0 1 4 29 12 41 71 196
Mono 0 0 1 13 3 0 5 754 3165
NK 38 2784 6 25 11 259 38 92 463
other 0 0 57 8 1025 208 487 19 46
other T 3 2 0 1 1 4 2 63 317# Azimuth l2
janitor::tabyl(All_samples_Merged@meta.data, predicted.celltype.l2, cell_line) predicted.celltype.l2 L1 L2 L3 L4 L5 L6 L7 PBMC PBMC_10x
ASDC 0 0 0 0 0 0 0 0 3
B intermediate 0 0 2 54 2 2 0 457 179
B memory 0 0 11 34 38 82 120 164 74
B naive 0 0 0 41 0 0 0 459 692
CD14 Mono 0 0 1 14 5 0 6 755 3042
CD16 Mono 0 0 0 0 0 0 0 2 124
CD4 CTL 0 0 0 0 0 0 0 16 1
CD4 Naive 0 0 0 7 0 0 0 524 1512
CD4 Proliferating 2461 2852 5452 5391 4732 4002 4115 0 6
CD4 TCM 3320 270 887 562 178 557 517 4609 1978
CD4 TEM 1 0 0 0 0 0 0 68 25
CD8 Naive 0 0 0 0 0 0 0 361 1012
CD8 Proliferating 0 0 0 0 0 1 1 0 0
CD8 TCM 1 16 0 0 0 0 0 286 174
CD8 TEM 1 8 0 0 2 3 1 181 195
cDC1 0 0 0 0 2 6 0 21 13
cDC2 0 0 0 4 11 3 35 52 124
dnT 2 3 0 1 2 5 2 38 29
gdT 0 0 0 0 0 0 0 26 67
HSPC 0 0 60 7 1035 213 490 17 12
ILC 0 0 0 1 0 0 0 3 3
MAIT 0 0 0 0 0 0 0 14 228
NK 0 0 0 1 0 0 0 89 444
NK Proliferating 38 2785 6 24 11 259 38 1 5
NK_CD56bright 0 0 0 0 0 0 0 1 15
pDC 0 0 0 0 0 0 0 0 56
Plasmablast 0 0 0 0 0 0 0 9 10
Platelet 0 0 0 0 0 0 0 1 31
Treg 1 1 9 9 4 15 6 200 108# Azimuth l3
janitor::tabyl(All_samples_Merged@meta.data, predicted.celltype.l3, cell_line) predicted.celltype.l3 L1 L2 L3 L4 L5 L6 L7 PBMC PBMC_10x
ASDC_pDC 0 0 0 0 0 0 0 0 4
B intermediate kappa 0 0 0 1 0 0 0 75 51
B intermediate lambda 0 0 2 48 4 2 3 371 129
B memory kappa 0 0 7 1 7 22 55 38 42
B memory lambda 0 0 3 41 7 29 26 131 30
B naive kappa 0 0 0 0 0 0 0 267 645
B naive lambda 0 0 0 37 0 0 0 192 48
CD14 Mono 0 0 1 15 9 0 22 755 3043
CD16 Mono 0 0 0 0 0 0 0 2 124
CD4 CTL 0 0 0 0 0 0 0 27 1
CD4 Naive 0 0 0 9 0 0 0 528 1525
CD4 Proliferating 2462 2852 5452 5391 4732 4003 4115 0 6
CD4 TCM_1 1932 6 6 35 0 7 3 4057 1449
CD4 TCM_2 652 250 870 516 165 536 482 265 144
CD4 TCM_3 436 12 4 6 13 7 26 267 359
CD4 TEM_1 1 0 0 0 0 0 0 4 8
CD4 TEM_2 0 0 0 0 0 0 0 17 17
CD4 TEM_3 0 0 0 0 0 0 0 50 1
CD8 Naive 0 0 0 0 0 0 0 291 969
CD8 Naive_2 0 0 0 0 1 1 0 63 47
CD8 Proliferating 0 0 0 0 0 1 1 0 0
CD8 TCM_1 0 8 0 0 0 0 0 137 115
CD8 TCM_2 298 6 0 0 0 0 0 80 43
CD8 TCM_3 0 1 0 0 0 0 0 77 17
CD8 TEM_1 0 0 0 0 0 0 0 91 67
CD8 TEM_2 0 0 0 0 0 1 0 32 62
CD8 TEM_3 0 0 0 0 0 0 0 17 15
CD8 TEM_4 0 0 0 0 0 0 0 2 12
CD8 TEM_5 0 0 0 0 0 0 0 27 1
CD8 TEM_6 1 10 0 0 1 1 1 1 36
cDC1 0 0 0 1 17 36 16 28 13
cDC2_1 0 0 0 1 1 0 4 2 40
cDC2_2 0 0 1 3 12 4 33 46 83
dnT_2 2 3 0 2 2 6 2 42 31
gdT_1 0 0 0 0 0 0 0 33 52
gdT_3 0 0 0 0 0 0 0 1 15
HSPC 0 0 62 7 1036 214 493 17 12
ILC 0 0 1 3 0 0 0 4 3
MAIT 0 0 0 0 0 0 0 16 229
NK Proliferating 38 2785 6 24 11 259 38 1 5
NK_1 0 0 0 0 0 0 0 42 7
NK_2 0 0 0 0 0 0 0 18 386
NK_3 0 0 0 0 0 0 0 22 20
NK_4 0 0 0 0 0 0 0 3 30
NK_CD56bright 0 0 0 0 0 0 0 1 16
pDC 0 0 0 0 0 0 0 0 56
Plasma 0 0 0 0 0 0 0 9 10
Platelet 0 0 0 0 0 0 0 1 31
Treg Memory 3 2 13 9 4 19 11 200 89
Treg Naive 0 0 0 0 0 0 0 4 24Cell type Distribution barplot
library(ggplot2)
library(RColorBrewer)
# Assuming you have 10 different cell lines, generating a color palette with 10 colors
cell_line_colors <- brewer.pal(10, "Set3")
# Assuming All_samples_Merged$cell_line is a factor or character vector containing cell line names
data <- as.data.frame(table(All_samples_Merged$cell_line))
colnames(data) <- c("cell_line", "nUMI") # Change column name to nUMI
ncells <- ggplot(data, aes(x = cell_line, y = nUMI, fill = cell_line)) +
geom_col() +
theme_classic() +
geom_text(aes(label = nUMI),
position = position_dodge(width = 0.9),
vjust = -0.25) +
scale_fill_manual(values = cell_line_colors) +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)) + # Adjust the title position
ggtitle("Filtered cells per sample") +
xlab("Cell lines") + # Adjust x-axis label
ylab("Frequency") # Adjust y-axis label
print(ncells)
NA
NA3. filter cells just keep CD4 T cells
# Set identity to cell_line
Idents(All_samples_Merged) <- "cell_line"
# Identify CD4 T cells in PBMC and PBMC_10x
cd4_cells_pbmc <- WhichCells(All_samples_Merged, expression =
grepl("^CD4", All_samples_Merged$predicted.celltype.l1) &
grepl("^CD4", All_samples_Merged$predicted.celltype.l2) &
grepl("^CD4", All_samples_Merged$predicted.celltype.l3) &
(cell_line %in% c("PBMC", "PBMC_10x"))
)
# Select all cells from other cell lines
other_cells_seurat <- subset(All_samples_Merged, subset = cell_line %in% c("L1", "L2", "L3", "L4", "L5", "L6", "L7"))
# Get the cell names from the subset
other_cells <- colnames(other_cells_seurat)
# Combine the cell lists
cells_to_keep <- c(cd4_cells_pbmc, other_cells)
# Create the final filtered Seurat object
filtered_seurat <- subset(All_samples_Merged, cells = cells_to_keep)
library(ggplot2)
library(RColorBrewer)
# Assuming you have 10 different cell lines, generating a color palette with 10 colors
cell_line_colors <- brewer.pal(10, "Set3")
# Assuming All_samples_Merged$cell_line is a factor or character vector containing cell line names
data <- as.data.frame(table(filtered_seurat$cell_line))
colnames(data) <- c("cell_line", "nUMI") # Change column name to nUMI
ncells <- ggplot(data, aes(x = cell_line, y = nUMI, fill = cell_line)) +
geom_col() +
theme_classic() +
geom_text(aes(label = nUMI),
position = position_dodge(width = 0.9),
vjust = -0.25) +
scale_fill_manual(values = cell_line_colors) +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)) + # Adjust the title position
ggtitle("Filtered cells per sample") +
xlab("Cell lines") + # Adjust x-axis label
ylab("Frequency") # Adjust y-axis label
print(ncells)
NA
NACell type Distribution to check clusters
# We can apply it later on R obj to get these tables to compare it to decide about resolution.
janitor::tabyl(filtered_seurat@meta.data, predicted.celltype.l1, cell_line) predicted.celltype.l1 L1 L2 L3 L4 L5 L6 L7 PBMC PBMC_10x
B 0 0 12 131 35 85 121 0 0
CD4 T 5771 3124 6351 5967 4914 4575 4634 5171 3505
CD8 T 13 25 0 1 4 5 3 0 0
DC 0 0 1 4 29 12 41 0 0
Mono 0 0 1 13 3 0 5 0 0
NK 38 2784 6 25 11 259 38 0 0
other 0 0 57 8 1025 208 487 0 0
other T 3 2 0 1 1 4 2 0 0janitor::tabyl(filtered_seurat@meta.data, predicted.celltype.l2, cell_line) predicted.celltype.l2 L1 L2 L3 L4 L5 L6 L7 PBMC PBMC_10x
B intermediate 0 0 2 54 2 2 0 0 0
B memory 0 0 11 34 38 82 120 0 0
B naive 0 0 0 41 0 0 0 0 0
CD14 Mono 0 0 1 14 5 0 6 0 0
CD4 CTL 0 0 0 0 0 0 0 12 1
CD4 Naive 0 0 0 7 0 0 0 523 1512
CD4 Proliferating 2461 2852 5452 5391 4732 4002 4115 0 6
CD4 TCM 3320 270 887 562 178 557 517 4576 1963
CD4 TEM 1 0 0 0 0 0 0 60 23
CD8 Proliferating 0 0 0 0 0 1 1 0 0
CD8 TCM 1 16 0 0 0 0 0 0 0
CD8 TEM 1 8 0 0 2 3 1 0 0
cDC1 0 0 0 0 2 6 0 0 0
cDC2 0 0 0 4 11 3 35 0 0
dnT 2 3 0 1 2 5 2 0 0
HSPC 0 0 60 7 1035 213 490 0 0
ILC 0 0 0 1 0 0 0 0 0
NK 0 0 0 1 0 0 0 0 0
NK Proliferating 38 2785 6 24 11 259 38 0 0
Treg 1 1 9 9 4 15 6 0 0janitor::tabyl(filtered_seurat@meta.data, predicted.celltype.l3, cell_line) predicted.celltype.l3 L1 L2 L3 L4 L5 L6 L7 PBMC PBMC_10x
B intermediate kappa 0 0 0 1 0 0 0 0 0
B intermediate lambda 0 0 2 48 4 2 3 0 0
B memory kappa 0 0 7 1 7 22 55 0 0
B memory lambda 0 0 3 41 7 29 26 0 0
B naive lambda 0 0 0 37 0 0 0 0 0
CD14 Mono 0 0 1 15 9 0 22 0 0
CD4 CTL 0 0 0 0 0 0 0 14 1
CD4 Naive 0 0 0 9 0 0 0 526 1524
CD4 Proliferating 2462 2852 5452 5391 4732 4003 4115 0 6
CD4 TCM_1 1932 6 6 35 0 7 3 4038 1447
CD4 TCM_2 652 250 870 516 165 536 482 265 144
CD4 TCM_3 436 12 4 6 13 7 26 265 359
CD4 TEM_1 1 0 0 0 0 0 0 4 7
CD4 TEM_2 0 0 0 0 0 0 0 15 16
CD4 TEM_3 0 0 0 0 0 0 0 44 1
CD8 Naive_2 0 0 0 0 1 1 0 0 0
CD8 Proliferating 0 0 0 0 0 1 1 0 0
CD8 TCM_1 0 8 0 0 0 0 0 0 0
CD8 TCM_2 298 6 0 0 0 0 0 0 0
CD8 TCM_3 0 1 0 0 0 0 0 0 0
CD8 TEM_2 0 0 0 0 0 1 0 0 0
CD8 TEM_6 1 10 0 0 1 1 1 0 0
cDC1 0 0 0 1 17 36 16 0 0
cDC2_1 0 0 0 1 1 0 4 0 0
cDC2_2 0 0 1 3 12 4 33 0 0
dnT_2 2 3 0 2 2 6 2 0 0
HSPC 0 0 62 7 1036 214 493 0 0
ILC 0 0 1 3 0 0 0 0 0
NK Proliferating 38 2785 6 24 11 259 38 0 0
Treg Memory 3 2 13 9 4 19 11 0 04. filter B cells from L4
# Set identity to cell_line
Idents(filtered_seurat) <- "cell_line"
# Load necessary libraries
library(Seurat)
library(ggplot2)
library(RColorBrewer)
# Identify B cells in L4 to exclude
b_cells_l4 <- WhichCells(filtered_seurat, expression =
grepl("^B", filtered_seurat$predicted.celltype.l1) &
grepl("^B", filtered_seurat$predicted.celltype.l2) &
grepl("^B", filtered_seurat$predicted.celltype.l3) &
cell_line == "L4"
)
# Identify cells to keep (excluding B cells in L4)
cells_to_keep <- setdiff(Cells(filtered_seurat), b_cells_l4)
# Subset the Seurat object with selected cells
filtered_seurat <- subset(filtered_seurat, cells = cells_to_keep)
# Plot the filtered data
# Define cell line colors
cell_line_colors <- brewer.pal(length(unique(filtered_seurat$cell_line)), "Set3")
# Generate data frame for plotting
data <- as.data.frame(table(filtered_seurat$cell_line))
colnames(data) <- c("cell_line", "nUMI")
# Create the bar plot
ncells <- ggplot(data, aes(x = cell_line, y = nUMI, fill = cell_line)) +
geom_col() +
theme_classic() +
geom_text(aes(label = nUMI), position = position_dodge(width = 0.9), vjust = -0.25) +
scale_fill_manual(values = cell_line_colors) +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)) +
ggtitle("Filtered Cells per Sample") +
xlab("Cell Lines") +
ylab("Frequency")
print(ncells)
Cell type Distribution to check clusters
# We can apply it later on R obj to get these tables to compare it to decide about resolution.
# Azimuth l1
janitor::tabyl(filtered_seurat@meta.data, predicted.celltype.l1, cell_line) predicted.celltype.l1 L1 L2 L3 L4 L5 L6 L7 PBMC PBMC_10x
B 0 0 12 4 35 85 121 0 0
CD4 T 5771 3124 6351 5967 4914 4575 4634 5171 3505
CD8 T 13 25 0 1 4 5 3 0 0
DC 0 0 1 4 29 12 41 0 0
Mono 0 0 1 13 3 0 5 0 0
NK 38 2784 6 25 11 259 38 0 0
other 0 0 57 8 1025 208 487 0 0
other T 3 2 0 1 1 4 2 0 0# Azimuth l2
janitor::tabyl(filtered_seurat@meta.data, predicted.celltype.l2, cell_line) predicted.celltype.l2 L1 L2 L3 L4 L5 L6 L7 PBMC PBMC_10x
B intermediate 0 0 2 1 2 2 0 0 0
B memory 0 0 11 1 38 82 120 0 0
CD14 Mono 0 0 1 14 5 0 6 0 0
CD4 CTL 0 0 0 0 0 0 0 12 1
CD4 Naive 0 0 0 7 0 0 0 523 1512
CD4 Proliferating 2461 2852 5452 5391 4732 4002 4115 0 6
CD4 TCM 3320 270 887 562 178 557 517 4576 1963
CD4 TEM 1 0 0 0 0 0 0 60 23
CD8 Proliferating 0 0 0 0 0 1 1 0 0
CD8 TCM 1 16 0 0 0 0 0 0 0
CD8 TEM 1 8 0 0 2 3 1 0 0
cDC1 0 0 0 0 2 6 0 0 0
cDC2 0 0 0 4 11 3 35 0 0
dnT 2 3 0 1 2 5 2 0 0
HSPC 0 0 60 7 1035 213 490 0 0
ILC 0 0 0 1 0 0 0 0 0
NK 0 0 0 1 0 0 0 0 0
NK Proliferating 38 2785 6 24 11 259 38 0 0
Treg 1 1 9 9 4 15 6 0 0# Azimuth l3
janitor::tabyl(filtered_seurat@meta.data, predicted.celltype.l3, cell_line) predicted.celltype.l3 L1 L2 L3 L4 L5 L6 L7 PBMC PBMC_10x
B intermediate lambda 0 0 2 0 4 2 3 0 0
B memory kappa 0 0 7 0 7 22 55 0 0
B memory lambda 0 0 3 1 7 29 26 0 0
CD14 Mono 0 0 1 15 9 0 22 0 0
CD4 CTL 0 0 0 0 0 0 0 14 1
CD4 Naive 0 0 0 9 0 0 0 526 1524
CD4 Proliferating 2462 2852 5452 5391 4732 4003 4115 0 6
CD4 TCM_1 1932 6 6 35 0 7 3 4038 1447
CD4 TCM_2 652 250 870 516 165 536 482 265 144
CD4 TCM_3 436 12 4 6 13 7 26 265 359
CD4 TEM_1 1 0 0 0 0 0 0 4 7
CD4 TEM_2 0 0 0 0 0 0 0 15 16
CD4 TEM_3 0 0 0 0 0 0 0 44 1
CD8 Naive_2 0 0 0 0 1 1 0 0 0
CD8 Proliferating 0 0 0 0 0 1 1 0 0
CD8 TCM_1 0 8 0 0 0 0 0 0 0
CD8 TCM_2 298 6 0 0 0 0 0 0 0
CD8 TCM_3 0 1 0 0 0 0 0 0 0
CD8 TEM_2 0 0 0 0 0 1 0 0 0
CD8 TEM_6 1 10 0 0 1 1 1 0 0
cDC1 0 0 0 1 17 36 16 0 0
cDC2_1 0 0 0 1 1 0 4 0 0
cDC2_2 0 0 1 3 12 4 33 0 0
dnT_2 2 3 0 2 2 6 2 0 0
HSPC 0 0 62 7 1036 214 493 0 0
ILC 0 0 1 3 0 0 0 0 0
NK Proliferating 38 2785 6 24 11 259 38 0 0
Treg Memory 3 2 13 9 4 19 11 0 05. filter ILC and NK just one cells just keep CD4 T cells
# Set identity to cell_line
Idents(filtered_seurat) <- "cell_line"
# Remove ILC and NK cells based on predicted.celltype.l2
filtered_seurat <- subset(filtered_seurat, subset = predicted.celltype.l2 != "ILC" & predicted.celltype.l2 != "NK")
library(ggplot2)
library(RColorBrewer)
# Assuming you have 10 different cell lines, generating a color palette with 10 colors
cell_line_colors <- brewer.pal(10, "Set3")
# Assuming filtered_seurat$cell_line is a factor or character vector containing cell line names
data <- as.data.frame(table(filtered_seurat$cell_line))
colnames(data) <- c("cell_line", "nUMI") # Change column name to nUMI
ncells <- ggplot(data, aes(x = cell_line, y = nUMI, fill = cell_line)) +
geom_col() +
theme_classic() +
geom_text(aes(label = nUMI),
position = position_dodge(width = 0.9),
vjust = -0.25) +
scale_fill_manual(values = cell_line_colors) +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)) + # Adjust the title position
ggtitle("Filtered cells per sample") +
xlab("Cell lines") + # Adjust x-axis label
ylab("Frequency") # Adjust y-axis label
print(ncells)
NA
NA
NACell type Distribution to check clusters
# We can apply it later on R obj to get these tables to compare it to decide about resolution.
# Azimuth l1
janitor::tabyl(filtered_seurat@meta.data, predicted.celltype.l1, cell_line) predicted.celltype.l1 L1 L2 L3 L4 L5 L6 L7 PBMC PBMC_10x
B 0 0 12 4 35 85 121 0 0
CD4 T 5771 3124 6351 5967 4914 4575 4634 5171 3505
CD8 T 13 25 0 1 4 5 3 0 0
DC 0 0 1 4 29 12 41 0 0
Mono 0 0 1 13 3 0 5 0 0
NK 38 2784 6 24 11 259 38 0 0
other 0 0 57 7 1025 208 487 0 0
other T 3 2 0 1 1 4 2 0 0janitor::tabyl(filtered_seurat@meta.data, predicted.celltype.l2, cell_line) predicted.celltype.l2 L1 L2 L3 L4 L5 L6 L7 PBMC PBMC_10x
B intermediate 0 0 2 1 2 2 0 0 0
B memory 0 0 11 1 38 82 120 0 0
CD14 Mono 0 0 1 14 5 0 6 0 0
CD4 CTL 0 0 0 0 0 0 0 12 1
CD4 Naive 0 0 0 7 0 0 0 523 1512
CD4 Proliferating 2461 2852 5452 5391 4732 4002 4115 0 6
CD4 TCM 3320 270 887 562 178 557 517 4576 1963
CD4 TEM 1 0 0 0 0 0 0 60 23
CD8 Proliferating 0 0 0 0 0 1 1 0 0
CD8 TCM 1 16 0 0 0 0 0 0 0
CD8 TEM 1 8 0 0 2 3 1 0 0
cDC1 0 0 0 0 2 6 0 0 0
cDC2 0 0 0 4 11 3 35 0 0
dnT 2 3 0 1 2 5 2 0 0
HSPC 0 0 60 7 1035 213 490 0 0
NK Proliferating 38 2785 6 24 11 259 38 0 0
Treg 1 1 9 9 4 15 6 0 0janitor::tabyl(filtered_seurat@meta.data, predicted.celltype.l3, cell_line) predicted.celltype.l3 L1 L2 L3 L4 L5 L6 L7 PBMC PBMC_10x
B intermediate lambda 0 0 2 0 4 2 3 0 0
B memory kappa 0 0 7 0 7 22 55 0 0
B memory lambda 0 0 3 1 7 29 26 0 0
CD14 Mono 0 0 1 14 9 0 22 0 0
CD4 CTL 0 0 0 0 0 0 0 14 1
CD4 Naive 0 0 0 9 0 0 0 526 1524
CD4 Proliferating 2462 2852 5452 5391 4732 4003 4115 0 6
CD4 TCM_1 1932 6 6 35 0 7 3 4038 1447
CD4 TCM_2 652 250 870 516 165 536 482 265 144
CD4 TCM_3 436 12 4 6 13 7 26 265 359
CD4 TEM_1 1 0 0 0 0 0 0 4 7
CD4 TEM_2 0 0 0 0 0 0 0 15 16
CD4 TEM_3 0 0 0 0 0 0 0 44 1
CD8 Naive_2 0 0 0 0 1 1 0 0 0
CD8 Proliferating 0 0 0 0 0 1 1 0 0
CD8 TCM_1 0 8 0 0 0 0 0 0 0
CD8 TCM_2 298 6 0 0 0 0 0 0 0
CD8 TCM_3 0 1 0 0 0 0 0 0 0
CD8 TEM_2 0 0 0 0 0 1 0 0 0
CD8 TEM_6 1 10 0 0 1 1 1 0 0
cDC1 0 0 0 1 17 36 16 0 0
cDC2_1 0 0 0 1 1 0 4 0 0
cDC2_2 0 0 1 3 12 4 33 0 0
dnT_2 2 3 0 2 2 6 2 0 0
HSPC 0 0 62 7 1036 214 493 0 0
ILC 0 0 1 2 0 0 0 0 0
NK Proliferating 38 2785 6 24 11 259 38 0 0
Treg Memory 3 2 13 9 4 19 11 0 06. filter cells just keep CD4 T cells
# Load necessary libraries
library(Seurat)
library(ggplot2)
library(RColorBrewer)
# Set identity to cell_line
Idents(filtered_seurat) <- "cell_line"
# Identify CD14 Monocytes in L4 to exclude
cd14_mono_l4 <- WhichCells(filtered_seurat, expression =
grepl("CD14 Mono", filtered_seurat$predicted.celltype.l2) &
grepl("CD14 Mono", filtered_seurat$predicted.celltype.l3) &
cell_line == "L4"
)
# Identify cells to keep (excluding CD14 Monocytes in L4)
cells_to_keep <- setdiff(Cells(filtered_seurat), cd14_mono_l4)
# Subset the Seurat object with selected cells
filtered_seurat <- subset(filtered_seurat, cells = cells_to_keep)
library(ggplot2)
library(RColorBrewer)
# Assuming you have 10 different cell lines, generating a color palette with 10 colors
cell_line_colors <- brewer.pal(10, "Set3")
# Assuming filtered_seurat$cell_line is a factor or character vector containing cell line names
data <- as.data.frame(table(filtered_seurat$cell_line))
colnames(data) <- c("cell_line", "nUMI") # Change column name to nUMI
ncells <- ggplot(data, aes(x = cell_line, y = nUMI, fill = cell_line)) +
geom_col() +
theme_classic() +
geom_text(aes(label = nUMI),
position = position_dodge(width = 0.9),
vjust = -0.25) +
scale_fill_manual(values = cell_line_colors) +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)) + # Adjust the title position
ggtitle("Filtered cells per sample") +
xlab("Cell lines") + # Adjust x-axis label
ylab("Frequency") # Adjust y-axis label
print(ncells)
NA
NA
NACell type Distribution to check clusters
# We can apply it later on R obj to get these tables to compare it to decide about resolution.
# Azimuth l1
janitor::tabyl(filtered_seurat@meta.data, predicted.celltype.l1, cell_line) predicted.celltype.l1 L1 L2 L3 L4 L5 L6 L7 PBMC PBMC_10x
B 0 0 12 3 35 85 121 0 0
CD4 T 5771 3124 6351 5967 4914 4575 4634 5171 3505
CD8 T 13 25 0 1 4 5 3 0 0
DC 0 0 1 4 29 12 41 0 0
Mono 0 0 1 0 3 0 5 0 0
NK 38 2784 6 24 11 259 38 0 0
other 0 0 57 7 1025 208 487 0 0
other T 3 2 0 1 1 4 2 0 0janitor::tabyl(filtered_seurat@meta.data, predicted.celltype.l2, cell_line) predicted.celltype.l2 L1 L2 L3 L4 L5 L6 L7 PBMC PBMC_10x
B intermediate 0 0 2 1 2 2 0 0 0
B memory 0 0 11 1 38 82 120 0 0
CD14 Mono 0 0 1 0 5 0 6 0 0
CD4 CTL 0 0 0 0 0 0 0 12 1
CD4 Naive 0 0 0 7 0 0 0 523 1512
CD4 Proliferating 2461 2852 5452 5391 4732 4002 4115 0 6
CD4 TCM 3320 270 887 562 178 557 517 4576 1963
CD4 TEM 1 0 0 0 0 0 0 60 23
CD8 Proliferating 0 0 0 0 0 1 1 0 0
CD8 TCM 1 16 0 0 0 0 0 0 0
CD8 TEM 1 8 0 0 2 3 1 0 0
cDC1 0 0 0 0 2 6 0 0 0
cDC2 0 0 0 4 11 3 35 0 0
dnT 2 3 0 1 2 5 2 0 0
HSPC 0 0 60 7 1035 213 490 0 0
NK Proliferating 38 2785 6 24 11 259 38 0 0
Treg 1 1 9 9 4 15 6 0 0janitor::tabyl(filtered_seurat@meta.data, predicted.celltype.l3, cell_line) predicted.celltype.l3 L1 L2 L3 L4 L5 L6 L7 PBMC PBMC_10x
B intermediate lambda 0 0 2 0 4 2 3 0 0
B memory kappa 0 0 7 0 7 22 55 0 0
B memory lambda 0 0 3 1 7 29 26 0 0
CD14 Mono 0 0 1 0 9 0 22 0 0
CD4 CTL 0 0 0 0 0 0 0 14 1
CD4 Naive 0 0 0 9 0 0 0 526 1524
CD4 Proliferating 2462 2852 5452 5391 4732 4003 4115 0 6
CD4 TCM_1 1932 6 6 35 0 7 3 4038 1447
CD4 TCM_2 652 250 870 516 165 536 482 265 144
CD4 TCM_3 436 12 4 6 13 7 26 265 359
CD4 TEM_1 1 0 0 0 0 0 0 4 7
CD4 TEM_2 0 0 0 0 0 0 0 15 16
CD4 TEM_3 0 0 0 0 0 0 0 44 1
CD8 Naive_2 0 0 0 0 1 1 0 0 0
CD8 Proliferating 0 0 0 0 0 1 1 0 0
CD8 TCM_1 0 8 0 0 0 0 0 0 0
CD8 TCM_2 298 6 0 0 0 0 0 0 0
CD8 TCM_3 0 1 0 0 0 0 0 0 0
CD8 TEM_2 0 0 0 0 0 1 0 0 0
CD8 TEM_6 1 10 0 0 1 1 1 0 0
cDC1 0 0 0 1 17 36 16 0 0
cDC2_1 0 0 0 1 1 0 4 0 0
cDC2_2 0 0 1 3 12 4 33 0 0
dnT_2 2 3 0 2 2 6 2 0 0
HSPC 0 0 62 7 1036 214 493 0 0
ILC 0 0 1 2 0 0 0 0 0
NK Proliferating 38 2785 6 24 11 259 38 0 0
Treg Memory 3 2 13 9 4 19 11 0 07. Save the Seurat object as an Robj file
---
title: "use Annotated Robj including PBMC10x to remove NonCD4Tcells from Control and keep just CD4Tcells"
author: Nasir Mahmood Abbasi
date: "`r Sys.Date()`"
output:
  # pdf_document: default
  # word_document: default
  # html_document: default
  #rmdformats::readthedown
  html_notebook:
    toc: true
    toc_float: true
    toc_collapsed: true
---



##. In this script I will remove Non T cells from PBMC 

# 1. load libraries
```{r setup, include=FALSE}

library(Seurat)
library(SeuratObject)
library(SeuratData)
library(patchwork)

library(dplyr)
library(tidyverse)
library(ggplot2)
library(RColorBrewer)
library(magrittr)
library(dbplyr)
library(rmarkdown)
library(knitr)
library(tinytex)
#Azimuth Annotation libraries
library(Azimuth)
#ProjecTils Annotation libraries
library(STACAS)
library(ProjecTILs)
#singleR Annotation libraries
library(SingleR)
library(celldex)
library(SingleCellExperiment)

library(clustree)

```


# 2. Load Seurat Object 
```{r load_seurat}

#Load Seurat Object merged from cell lines and a control(PBMC) after filtration
load("../0-R_Objects/All_Samples_Merged_with_10x_Azitmuth_Annotated.robj")


All_samples_Merged


```

##  Cell type Distribution to check clusters
```{r Distribution1, fig.height=10, fig.width=10}
# We can apply it later on R obj to get these tables to compare it to decide about resolution.

# Azimuth l1
janitor::tabyl(All_samples_Merged@meta.data, predicted.celltype.l1, cell_line)


# Azimuth l2
janitor::tabyl(All_samples_Merged@meta.data, predicted.celltype.l2, cell_line)

# Azimuth l3
janitor::tabyl(All_samples_Merged@meta.data, predicted.celltype.l3, cell_line)

```

##  Cell type Distribution barplot
```{r Distribution2, fig.height=6, fig.width=10}

library(ggplot2)
library(RColorBrewer)  

# Assuming you have 10 different cell lines, generating a color palette with 10 colors
cell_line_colors <- brewer.pal(10, "Set3")

# Assuming All_samples_Merged$cell_line is a factor or character vector containing cell line names
data <- as.data.frame(table(All_samples_Merged$cell_line))
colnames(data) <- c("cell_line", "nUMI")  # Change column name to nUMI

ncells <- ggplot(data, aes(x = cell_line, y = nUMI, fill = cell_line)) + 
  geom_col() +
  theme_classic() +
  geom_text(aes(label = nUMI), 
            position = position_dodge(width = 0.9), 
            vjust = -0.25) +
  scale_fill_manual(values = cell_line_colors) + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        plot.title = element_text(hjust = 0.5)) +  # Adjust the title position
  ggtitle("Filtered cells per sample") +
  xlab("Cell lines") +  # Adjust x-axis label
  ylab("Frequency")    # Adjust y-axis label

print(ncells)


```

# 3. filter cells just keep CD4 T cells
```{r Distribution3, fig.height=6, fig.width=10}

# Set identity to cell_line 
Idents(All_samples_Merged) <- "cell_line"

# Identify CD4 T cells in PBMC and PBMC_10x
cd4_cells_pbmc <- WhichCells(All_samples_Merged, expression = 
  grepl("^CD4", All_samples_Merged$predicted.celltype.l1) &
  grepl("^CD4", All_samples_Merged$predicted.celltype.l2) &
  grepl("^CD4", All_samples_Merged$predicted.celltype.l3) &
  (cell_line %in% c("PBMC", "PBMC_10x"))
)

# Select all cells from other cell lines
other_cells_seurat <- subset(All_samples_Merged, subset = cell_line %in% c("L1", "L2", "L3", "L4", "L5", "L6", "L7"))

# Get the cell names from the subset
other_cells <- colnames(other_cells_seurat)

# Combine the cell lists
cells_to_keep <- c(cd4_cells_pbmc, other_cells)

# Create the final filtered Seurat object
filtered_seurat <- subset(All_samples_Merged, cells = cells_to_keep)



library(ggplot2)
library(RColorBrewer)  

# Assuming you have 10 different cell lines, generating a color palette with 10 colors
cell_line_colors <- brewer.pal(10, "Set3")

# Assuming All_samples_Merged$cell_line is a factor or character vector containing cell line names
data <- as.data.frame(table(filtered_seurat$cell_line))
colnames(data) <- c("cell_line", "nUMI")  # Change column name to nUMI

ncells <- ggplot(data, aes(x = cell_line, y = nUMI, fill = cell_line)) + 
  geom_col() +
  theme_classic() +
  geom_text(aes(label = nUMI), 
            position = position_dodge(width = 0.9), 
            vjust = -0.25) +
  scale_fill_manual(values = cell_line_colors) + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        plot.title = element_text(hjust = 0.5)) +  # Adjust the title position
  ggtitle("Filtered cells per sample") +
  xlab("Cell lines") +  # Adjust x-axis label
  ylab("Frequency")    # Adjust y-axis label

print(ncells)


```




## Cell type Distribution to check clusters
```{r Distribution4, fig.height=6, fig.width=10}

# We can apply it later on R obj to get these tables to compare it to decide about resolution.


janitor::tabyl(filtered_seurat@meta.data, predicted.celltype.l1, cell_line)


janitor::tabyl(filtered_seurat@meta.data, predicted.celltype.l2, cell_line)


janitor::tabyl(filtered_seurat@meta.data, predicted.celltype.l3, cell_line)

```


# 4. filter B cells from L4
```{r Distribution5, fig.height=6, fig.width=10}

# Set identity to cell_line
Idents(filtered_seurat) <- "cell_line"


# Load necessary libraries
library(Seurat)
library(ggplot2)
library(RColorBrewer)

# Identify B cells in L4 to exclude
b_cells_l4 <- WhichCells(filtered_seurat, expression = 
  grepl("^B", filtered_seurat$predicted.celltype.l1) &
  grepl("^B", filtered_seurat$predicted.celltype.l2) &
  grepl("^B", filtered_seurat$predicted.celltype.l3) &
  cell_line == "L4"
)

# Identify cells to keep (excluding B cells in L4)
cells_to_keep <- setdiff(Cells(filtered_seurat), b_cells_l4)

# Subset the Seurat object with selected cells
filtered_seurat <- subset(filtered_seurat, cells = cells_to_keep)

# Plot the filtered data

# Define cell line colors
cell_line_colors <- brewer.pal(length(unique(filtered_seurat$cell_line)), "Set3")

# Generate data frame for plotting
data <- as.data.frame(table(filtered_seurat$cell_line))
colnames(data) <- c("cell_line", "nUMI")

# Create the bar plot
ncells <- ggplot(data, aes(x = cell_line, y = nUMI, fill = cell_line)) +
  geom_col() +
  theme_classic() +
  geom_text(aes(label = nUMI), position = position_dodge(width = 0.9), vjust = -0.25) +
  scale_fill_manual(values = cell_line_colors) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        plot.title = element_text(hjust = 0.5)) +
  ggtitle("Filtered Cells per Sample") +
  xlab("Cell Lines") +
  ylab("Frequency")

print(ncells)

```

## Cell type Distribution to check clusters
```{r Distribution6, fig.height=10, fig.width=10}
# We can apply it later on R obj to get these tables to compare it to decide about resolution.

# Azimuth l1
janitor::tabyl(filtered_seurat@meta.data, predicted.celltype.l1, cell_line)

# Azimuth l2
janitor::tabyl(filtered_seurat@meta.data, predicted.celltype.l2, cell_line)

# Azimuth l3
janitor::tabyl(filtered_seurat@meta.data, predicted.celltype.l3, cell_line)


```


# 5. filter ILC and NK just one cells just keep CD4 T cells
```{r Distribution7, fig.height=6, fig.width=10}


# Set identity to cell_line 
Idents(filtered_seurat) <- "cell_line"



# Remove ILC and NK cells based on predicted.celltype.l2
filtered_seurat <- subset(filtered_seurat, subset = predicted.celltype.l2 != "ILC" & predicted.celltype.l2 != "NK")




library(ggplot2)
library(RColorBrewer)  

# Assuming you have 10 different cell lines, generating a color palette with 10 colors
cell_line_colors <- brewer.pal(10, "Set3")

# Assuming filtered_seurat$cell_line is a factor or character vector containing cell line names
data <- as.data.frame(table(filtered_seurat$cell_line))
colnames(data) <- c("cell_line", "nUMI")  # Change column name to nUMI

ncells <- ggplot(data, aes(x = cell_line, y = nUMI, fill = cell_line)) + 
  geom_col() +
  theme_classic() +
  geom_text(aes(label = nUMI), 
            position = position_dodge(width = 0.9), 
            vjust = -0.25) +
  scale_fill_manual(values = cell_line_colors) + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        plot.title = element_text(hjust = 0.5)) +  # Adjust the title position
  ggtitle("Filtered cells per sample") +
  xlab("Cell lines") +  # Adjust x-axis label
  ylab("Frequency")    # Adjust y-axis label

print(ncells)



```




## Cell type Distribution to check clusters
```{r Distribution8, fig.height=6, fig.width=10}

# We can apply it later on R obj to get these tables to compare it to decide about resolution.

# Azimuth l1
janitor::tabyl(filtered_seurat@meta.data, predicted.celltype.l1, cell_line)


janitor::tabyl(filtered_seurat@meta.data, predicted.celltype.l2, cell_line)


janitor::tabyl(filtered_seurat@meta.data, predicted.celltype.l3, cell_line)

```

# 6. filter cells just keep CD4 T cells
```{r Distribution9, fig.height=6, fig.width=10}


# Load necessary libraries
library(Seurat)
library(ggplot2)
library(RColorBrewer)

# Set identity to cell_line
Idents(filtered_seurat) <- "cell_line"

# Identify CD14 Monocytes in L4 to exclude
cd14_mono_l4 <- WhichCells(filtered_seurat, expression = 
  grepl("CD14 Mono", filtered_seurat$predicted.celltype.l2) &
  grepl("CD14 Mono", filtered_seurat$predicted.celltype.l3) &
  cell_line == "L4"
)

# Identify cells to keep (excluding CD14 Monocytes in L4)
cells_to_keep <- setdiff(Cells(filtered_seurat), cd14_mono_l4)

# Subset the Seurat object with selected cells
filtered_seurat <- subset(filtered_seurat, cells = cells_to_keep)
library(ggplot2)
library(RColorBrewer)  

# Assuming you have 10 different cell lines, generating a color palette with 10 colors
cell_line_colors <- brewer.pal(10, "Set3")

# Assuming filtered_seurat$cell_line is a factor or character vector containing cell line names
data <- as.data.frame(table(filtered_seurat$cell_line))
colnames(data) <- c("cell_line", "nUMI")  # Change column name to nUMI

ncells <- ggplot(data, aes(x = cell_line, y = nUMI, fill = cell_line)) + 
  geom_col() +
  theme_classic() +
  geom_text(aes(label = nUMI), 
            position = position_dodge(width = 0.9), 
            vjust = -0.25) +
  scale_fill_manual(values = cell_line_colors) + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        plot.title = element_text(hjust = 0.5)) +  # Adjust the title position
  ggtitle("Filtered cells per sample") +
  xlab("Cell lines") +  # Adjust x-axis label
  ylab("Frequency")    # Adjust y-axis label

print(ncells)



```


## Cell type Distribution to check clusters
```{r Distribution10, fig.height=6, fig.width=10}

# We can apply it later on R obj to get these tables to compare it to decide about resolution.

# Azimuth l1
janitor::tabyl(filtered_seurat@meta.data, predicted.celltype.l1, cell_line)


janitor::tabyl(filtered_seurat@meta.data, predicted.celltype.l2, cell_line)


janitor::tabyl(filtered_seurat@meta.data, predicted.celltype.l3, cell_line)

```




# 7. Save the Seurat object as an Robj file
```{r saveROBJ, echo=FALSE}

save(filtered_seurat, file = "0-imp_Robj/SS_CD4_Tcells_Azimuth_Annotated_PBMC10x_excluding_nonCD4_cells_from_Control.robj")


```







