Harmony_Intergrations_and_visualization_of_PBMC-10x.
Nasir Mahmood Abbasi
2024-11-12
1. load libraries
2. Load Seurat Object and visualize harmonyintegrated_UMAP
#Load Seurat Object merged from cell lines and a control(PBMC) after filtration
load("../../../0-IMP-OBJECTS/Harmony_integrated_All_samples_Merged_with_PBMC10x.Robj")
All_samples_MergedAn object of class Seurat
64169 features across 59355 samples within 6 assays
Active assay: SCT (27417 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
6 dimensional reductions calculated: integrated_dr, ref.umap, pca, umap, harmony, umap.harmonyDimPlot(All_samples_Merged, reduction = "umap.harmony", group.by = "cell_line",label = T, label.box = T)
3. UMAP visualization and Harmony Integration-1 Visualization
DimPlot(All_samples_Merged, reduction = "umap", group.by = "cell_line", label = T, label.box = T)
DimPlot(All_samples_Merged, reduction = "umap.harmony", group.by = "cell_line",label = T, label.box = T)
Harmony Visualization-2
library(harmony)
library(clustree)Le chargement a nécessité le package : ggraph
Attachement du package : 'ggraph'
L'objet suivant est masqué depuis 'package:sp':
geometry# Ensure the Harmony reduction is present
if (!"harmony" %in% Reductions(All_samples_Merged)) {
stop("Harmony reduction not found. Please run RunHarmony() first.")
}
# Run clustering using Harmony embeddings
All_samples_Merged <- FindNeighbors(All_samples_Merged, reduction = "harmony", dims = 1:22)Computing nearest neighbor graph
Registered S3 method overwritten by 'rmarkdown':
method from
print.paged_df
Computing SNN# Create a sequence of resolutions from 0.1 to 2 with 0.1 increments
resolutions <- seq(0.1, 2, by = 0.1)
# Loop through the resolutions
for (res in resolutions) {
# Find clusters for the current resolution using Harmony embeddings
All_samples_Merged <- FindClusters(All_samples_Merged, resolution = res)
# Create a new column for Harmony-based clustering
new_col_name <- paste0("Harmony_snn_res.", res)
All_samples_Merged[[new_col_name]] <- All_samples_Merged$seurat_clusters # Store cluster results here
# Create the plot for each resolution
plot <- DimPlot(All_samples_Merged,
group.by = new_col_name,
reduction = "umap.harmony",
label.size = 3,
repel = TRUE,
label = TRUE,
label.box = TRUE) +
ggtitle(paste("Harmony-based Clustering, Resolution:", res))
# Display the plot directly in the notebook
print(plot)
}Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1882456
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9776
Number of communities: 10
Elapsed time: 21 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1882456
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9606
Number of communities: 13
Elapsed time: 25 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1882456
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9478
Number of communities: 15
Elapsed time: 24 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1882456
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9354
Number of communities: 15
Elapsed time: 25 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1882456
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9226
Number of communities: 18
Elapsed time: 26 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1882456
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9143
Number of communities: 19
Elapsed time: 22 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1882456
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9055
Number of communities: 24
Elapsed time: 22 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1882456
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8989
Number of communities: 24
Elapsed time: 21 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1882456
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8922
Number of communities: 25
Elapsed time: 23 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1882456
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8868
Number of communities: 28
Elapsed time: 26 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1882456
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8809
Number of communities: 27
Elapsed time: 22 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1882456
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8754
Number of communities: 30
Elapsed time: 24 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1882456
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8709
Number of communities: 29
Elapsed time: 25 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1882456
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8656
Number of communities: 29
Elapsed time: 23 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1882456
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8608
Number of communities: 31
Elapsed time: 22 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1882456
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8558
Number of communities: 33
Elapsed time: 22 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1882456
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8515
Number of communities: 34
Elapsed time: 22 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1882456
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8471
Number of communities: 35
Elapsed time: 23 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1882456
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8437
Number of communities: 37
Elapsed time: 20 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1882456
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8399
Number of communities: 37
Elapsed time: 24 seconds
# Optionally, create a plot for a specific resolution (e.g., 0.5)
seurat_plot <- DimPlot(All_samples_Merged,
group.by = "Harmony_snn_res.0.9",
reduction = "umap.harmony",
label.size = 3,
repel = TRUE,
label = TRUE,
label.box = TRUE) +
ggtitle("Harmony-based Seurat Clusters (Resolution: 0.9)")
# Display the specific resolution plot
print(seurat_plot)
Harmony Visualization-3
# Combine clusters and cell lines for visualization
All_samples_Merged$Cluster_CellLine <- paste(All_samples_Merged$Harmony_snn_res.0.7, All_samples_Merged$cell_line, sep = "_")
# Plot with combined information
DimPlot(All_samples_Merged, group.by = "Cluster_CellLine", reduction = "umap.harmony", label = TRUE, label.size = 3) +
ggtitle("Clusters and Cell Lines on UMAP") + theme(legend.position = "none")
# Plot with combined information
DimPlot(All_samples_Merged, group.by = "Cluster_CellLine", reduction = "umap.harmony", label = TRUE, label.size = 3, label.box = T) +
ggtitle("Clusters and Cell Lines on UMAP") + theme(legend.position = "none")
p1 <- DimPlot(All_samples_Merged,
group.by = "Harmony_snn_res.0.7",
reduction = "umap.harmony",
label = TRUE,
repel = TRUE,
label.box = TRUE,
pt.size = 0.5) +
ggtitle("Clusters on UMAP") +
NoLegend()
p2 <- DimPlot(All_samples_Merged,
group.by = "cell_line",
reduction = "umap.harmony",
label = TRUE,
repel = TRUE,
label.box = TRUE,
pt.size = 0.5) +
ggtitle("Cell Lines on UMAP") +
NoLegend()
# Display plots side by side
p1 | p2
DimPlot(All_samples_Merged,
group.by = "predicted.celltype.l1",
reduction = "umap.harmony",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged,
group.by = "predicted.celltype.l2",
reduction = "umap.harmony",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged,
group.by = "predicted.celltype.l3",
reduction = "umap.harmony",
label.size = 3,
repel = T,
label = T, label.box = T)
NA
NA
NA
NA
NAHarmony Visualization-4
# Set identity classes to an existing column in meta data
Idents(object = All_samples_Merged) <- "Harmony_snn_res.0.7"
cluster_table <- table(Idents(All_samples_Merged))
table(All_samples_Merged$predicted.celltype.l2, All_samples_Merged$Harmony_snn_res.0.5)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
ASDC 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0
B intermediate 0 1 0 0 0 0 3 0 664 1 17 4 0 2 2 2 0 0
B memory 7 3 0 7 14 0 3 205 261 0 2 4 0 13 1 2 1 0
B naive 0 1 0 0 0 0 4 0 1170 1 2 13 0 0 1 0 0 0
CD14 Mono 3 1 8 0 0 20 2595 39 0 0 814 327 6 0 0 7 3 0
CD16 Mono 1 0 0 0 0 0 4 0 0 0 120 1 0 0 0 0 0 0
CD4 CTL 0 0 0 0 0 0 0 0 0 16 0 1 0 0 0 0 0 0
CD4 Naive 0 1989 0 0 0 0 0 0 7 0 0 6 0 0 40 0 0 1
CD4 Proliferating 10573 8 4839 6254 5489 412 1 738 2 1 0 23 48 490 132 1 0 0
CD4 TCM 909 5922 335 115 212 2649 18 1293 65 38 32 398 579 43 256 3 0 11
CD4 TEM 0 80 0 0 0 1 0 0 0 10 0 3 0 0 0 0 0 0
CD8 Naive 0 1330 0 0 0 0 0 0 2 1 1 20 1 0 17 0 1 0
CD8 Proliferating 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0
CD8 TCM 1 370 9 0 0 0 0 0 2 80 0 6 7 0 1 0 0 1
CD8 TEM 0 64 6 0 1 0 1 6 1 305 0 0 0 0 7 0 0 0
cDC1 0 0 0 0 0 0 13 5 0 0 0 0 0 2 1 21 0 0
cDC2 0 0 1 1 2 0 123 45 0 0 1 0 0 3 0 53 0 0
dnT 0 31 0 0 0 1 0 7 2 0 3 17 0 0 20 0 0 1
gdT 0 15 0 0 0 0 0 0 0 78 0 0 0 0 0 0 0 0
HSPC 55 0 0 1429 258 3 6 11 1 1 0 3 6 38 7 4 7 5
ILC 0 0 0 0 0 0 0 0 1 4 0 1 0 0 1 0 0 0
MAIT 0 9 0 0 0 0 0 0 0 227 0 3 0 0 3 0 0 0
NK 0 0 0 0 0 0 0 0 1 523 1 7 0 0 2 0 0 0
NK Proliferating 24 0 2818 15 254 0 0 6 0 2 0 4 0 11 32 0 1 0
NK_CD56bright 0 0 0 0 0 0 0 0 0 14 0 0 0 0 2 0 0 0
pDC 0 0 0 0 0 0 56 0 0 0 0 0 0 0 0 0 0 0
Plasmablast 0 0 0 0 0 0 5 0 13 0 0 0 0 0 1 0 0 0
Platelet 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 30 0
Treg 0 258 1 0 0 0 0 9 5 0 0 40 0 0 39 1 0 0table(All_samples_Merged$predicted.celltype.l2, All_samples_Merged$Harmony_snn_res.0.6)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
ASDC 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0
B intermediate 0 0 0 0 0 0 1 0 3 664 1 17 4 0 2 2 2 0 0
B memory 7 1 0 1 219 6 2 0 3 261 0 2 4 0 13 1 2 1 0
B naive 0 0 0 0 0 0 1 0 4 1170 1 2 13 0 0 1 0 0 0
CD14 Mono 3 0 8 0 39 0 1 20 2521 0 0 818 397 6 0 0 7 3 0
CD16 Mono 1 0 0 0 0 0 0 0 4 0 0 120 1 0 0 0 0 0 0
CD4 CTL 0 0 0 0 0 0 0 0 0 0 16 0 1 0 0 0 0 0 0
CD4 Naive 0 0 0 1957 0 0 33 0 0 7 0 0 5 0 0 40 0 0 1
CD4 Proliferating 10573 6721 4472 1 2719 3039 8 775 1 2 1 0 26 48 492 132 1 0 0
CD4 TCM 913 91 305 2662 1469 55 3257 2674 18 65 47 32 394 583 44 256 2 0 11
CD4 TEM 0 0 0 6 0 0 68 1 0 0 16 0 3 0 0 0 0 0 0
CD8 Naive 0 0 0 1316 0 0 13 0 0 2 2 1 20 1 0 17 0 1 0
CD8 Proliferating 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
CD8 TCM 1 0 9 128 0 0 221 0 0 2 101 0 6 7 0 1 0 0 1
CD8 TEM 0 0 6 5 7 0 44 0 1 1 321 0 0 0 0 6 0 0 0
cDC1 0 0 0 0 5 0 0 0 13 0 0 0 0 0 2 1 21 0 0
cDC2 0 1 1 0 47 0 0 0 121 0 0 1 2 0 3 0 53 0 0
dnT 0 0 0 7 7 0 24 1 0 2 0 3 17 0 0 20 0 0 1
gdT 0 0 0 9 0 0 6 0 0 0 78 0 0 0 0 0 0 0 0
HSPC 55 905 0 0 34 759 0 3 6 1 1 0 3 6 38 7 4 7 5
ILC 0 0 0 0 0 0 0 0 0 1 4 0 1 0 0 1 0 0 0
MAIT 0 0 0 0 0 0 8 0 0 0 228 0 3 0 0 3 0 0 0
NK 0 0 0 0 0 0 0 0 0 1 523 1 7 0 0 2 0 0 0
NK Proliferating 24 220 2813 0 49 6 0 4 0 0 2 0 5 0 11 32 0 1 0
NK_CD56bright 0 0 0 0 0 0 0 0 0 0 14 0 0 0 0 2 0 0 0
pDC 0 0 0 0 0 0 0 0 56 0 0 0 0 0 0 0 0 0 0
Plasmablast 0 0 0 0 0 0 0 0 5 13 0 0 0 0 0 1 0 0 0
Platelet 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 30 0
Treg 0 0 0 94 9 0 164 0 0 5 0 0 41 0 0 39 1 0 0table(All_samples_Merged$predicted.celltype.l2, All_samples_Merged$Harmony_snn_res.0.7)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
ASDC 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0
B intermediate 0 0 0 5 0 0 0 0 0 0 3 664 0 1 17 0 2 2 0 0 0 2 0
B memory 6 1 0 6 0 219 0 6 1 0 1 261 1 0 2 0 13 1 0 2 0 2 1
B naive 0 0 0 7 0 0 0 0 0 0 4 1170 0 1 0 0 0 1 7 0 2 0 0
CD14 Mono 3 0 0 17 0 39 9 0 0 20 2593 0 0 0 809 6 0 0 310 2 5 7 3
CD16 Mono 1 0 0 0 0 0 0 0 0 0 4 0 0 0 18 0 0 0 1 0 102 0 0
CD4 CTL 0 0 0 0 0 0 0 0 0 0 0 0 0 16 0 0 0 0 1 0 0 0 0
CD4 Naive 0 1957 0 37 0 0 0 0 0 0 0 7 0 0 0 0 0 40 1 0 0 0 0
CD4 Proliferating 8798 1 4328 32 1810 2035 3106 2941 3172 331 1 2 1781 1 0 49 493 129 0 0 0 1 0
CD4 TCM 827 2658 89 3632 7 1474 339 51 1 2629 18 65 87 47 32 588 42 257 21 0 0 3 0
CD4 TEM 0 6 0 72 0 0 0 0 0 1 0 0 0 15 0 0 0 0 0 0 0 0 0
CD8 Naive 0 1316 0 32 0 0 0 0 0 0 0 2 0 1 1 1 0 17 2 0 0 0 1
CD8 Proliferating 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
CD8 TCM 1 124 0 248 0 0 9 0 0 0 0 2 0 84 0 7 0 1 0 0 0 0 0
CD8 TEM 0 5 0 45 1 7 5 0 0 0 1 1 0 320 0 0 0 6 0 0 0 0 0
cDC1 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 2 1 0 13 0 21 0
cDC2 0 0 1 0 0 47 1 0 0 0 56 0 0 0 0 0 3 0 0 67 1 53 0
dnT 0 7 0 41 0 7 0 0 0 1 0 2 0 0 3 0 0 20 0 0 0 0 0
gdT 0 9 0 6 0 0 0 0 0 0 0 0 0 78 0 0 0 0 0 0 0 0 0
HSPC 55 0 808 3 0 40 0 752 97 3 4 1 0 1 0 6 39 7 0 2 0 4 7
ILC 0 0 0 1 0 0 0 0 0 0 0 1 0 4 0 0 0 1 0 0 0 0 0
MAIT 0 0 0 9 0 0 0 0 0 0 0 0 0 228 0 0 0 3 2 0 0 0 0
NK 0 0 0 0 0 0 0 0 0 0 0 1 0 523 1 0 0 2 7 0 0 0 0
NK Proliferating 9 0 13 5 2401 10 416 6 246 0 0 0 15 2 0 0 11 32 0 0 0 0 1
NK_CD56bright 0 0 0 0 0 0 0 0 0 0 0 0 0 14 0 0 0 2 0 0 0 0 0
pDC 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 56 0 0 0
Plasmablast 0 0 0 0 0 0 0 0 0 0 0 13 0 0 0 0 0 1 0 5 0 0 0
Platelet 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 30
Treg 0 94 0 203 0 9 1 0 0 0 0 5 0 0 0 0 0 39 1 0 0 1 0
23
ASDC 0
B intermediate 0
B memory 0
B naive 0
CD14 Mono 0
CD16 Mono 0
CD4 CTL 0
CD4 Naive 1
CD4 Proliferating 0
CD4 TCM 11
CD4 TEM 0
CD8 Naive 0
CD8 Proliferating 0
CD8 TCM 1
CD8 TEM 0
cDC1 0
cDC2 0
dnT 1
gdT 0
HSPC 5
ILC 0
MAIT 0
NK 0
NK Proliferating 0
NK_CD56bright 0
pDC 0
Plasmablast 0
Platelet 0
Treg 0table(All_samples_Merged$predicted.celltype.l2, All_samples_Merged$Harmony_snn_res.0.8)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
ASDC 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0
B intermediate 0 0 0 0 0 0 1 0 0 3 0 664 0 1 4 17 0 2 2 0 0 2 0
B memory 6 1 0 1 7 218 2 0 0 1 0 261 1 0 4 2 0 13 1 2 0 2 1
B naive 0 0 0 0 0 0 1 0 0 4 0 1170 0 1 13 0 0 0 1 0 2 0 0
CD14 Mono 3 0 0 0 0 39 1 0 20 2590 9 0 0 0 326 812 6 0 0 2 5 7 3
CD16 Mono 1 0 0 0 0 0 0 0 0 4 0 0 0 0 1 18 0 0 0 0 102 0 0
CD4 CTL 0 0 0 0 0 0 0 0 0 0 0 0 0 16 1 0 0 0 0 0 0 0 0
CD4 Naive 0 1965 0 0 0 0 23 0 0 0 0 7 0 0 7 0 0 0 40 0 0 0 0
CD4 Proliferating 8955 1 2561 4213 3123 2096 4 3049 395 1 2290 2 1613 1 29 0 50 495 132 0 0 1 0
CD4 TCM 838 2727 37 74 59 1428 3159 53 2638 18 303 65 75 45 430 32 584 43 256 0 0 3 0
CD4 TEM 0 6 0 0 0 0 71 0 1 0 0 0 0 13 3 0 0 0 0 0 0 0 0
CD8 Naive 0 1315 0 0 0 0 13 0 0 0 0 2 0 1 22 1 1 0 17 0 0 0 1
CD8 Proliferating 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
CD8 TCM 1 124 0 0 0 0 244 0 0 0 9 2 0 82 6 0 7 0 1 0 0 0 0
CD8 TEM 0 5 1 0 0 7 55 0 0 1 5 1 0 309 0 0 0 0 7 0 0 0 0
cDC1 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 2 1 13 0 21 0
cDC2 0 0 0 0 0 47 0 1 0 56 1 0 0 0 0 0 0 3 0 67 1 53 0
dnT 0 7 0 0 0 7 24 0 1 0 0 2 0 0 17 3 0 0 20 0 0 0 0
gdT 0 9 0 0 0 0 6 0 0 0 0 0 0 77 0 0 0 0 1 0 0 0 0
HSPC 55 0 0 256 782 21 0 639 3 4 0 1 0 1 3 0 6 38 7 2 0 4 7
ILC 0 0 0 0 0 0 0 0 0 0 0 1 0 4 1 0 0 0 1 0 0 0 0
MAIT 0 1 0 0 0 0 5 0 0 0 0 0 0 230 3 0 0 0 3 0 0 0 0
NK 0 0 0 0 0 0 0 0 0 0 0 1 0 518 7 1 0 0 7 0 0 0 0
NK Proliferating 11 0 2755 218 6 47 0 4 0 0 62 0 13 2 5 0 0 11 32 0 0 0 1
NK_CD56bright 0 0 0 0 0 0 0 0 0 0 0 0 0 14 0 0 0 0 2 0 0 0 0
pDC 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 56 0 0 0
Plasmablast 0 0 0 0 0 0 0 0 0 0 0 13 0 0 0 0 0 0 1 5 0 0 0
Platelet 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 30
Treg 0 93 0 0 0 9 161 0 0 0 1 5 0 0 44 0 0 0 39 0 0 1 0
23
ASDC 0
B intermediate 0
B memory 0
B naive 0
CD14 Mono 0
CD16 Mono 0
CD4 CTL 0
CD4 Naive 1
CD4 Proliferating 0
CD4 TCM 11
CD4 TEM 0
CD8 Naive 0
CD8 Proliferating 0
CD8 TCM 1
CD8 TEM 0
cDC1 0
cDC2 0
dnT 1
gdT 0
HSPC 5
ILC 0
MAIT 0
NK 0
NK Proliferating 0
NK_CD56bright 0
pDC 0
Plasmablast 0
Platelet 0
Treg 0table(All_samples_Merged$predicted.celltype.l2, All_samples_Merged$Harmony_snn_res.0.9)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
ASDC 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0
B intermediate 0 0 0 0 0 1 0 0 0 0 0 3 664 0 1 17 0 4 0 2 2 0 2
B memory 1 5 1 0 0 2 218 7 0 0 0 3 260 1 0 2 1 5 0 13 1 0 2
B naive 0 0 0 0 0 1 0 0 0 0 0 7 1170 0 1 0 0 6 0 0 1 6 0
CD14 Mono 0 1 2 0 0 1 38 0 9 0 20 2522 0 1 0 812 0 218 6 0 0 183 7
CD16 Mono 0 1 0 0 0 0 0 0 0 0 0 105 0 0 0 19 0 0 0 0 0 1 0
CD4 CTL 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16 0 0 0 0 0 0 1 0
CD4 Naive 1953 0 0 0 0 35 0 0 0 0 0 0 7 0 0 0 0 6 0 0 40 1 0
CD4 Proliferating 1 4409 4338 1901 3476 6 1977 2989 2946 3183 402 1 2 1846 1 0 825 28 48 498 133 0 1
CD4 TCM 2606 805 19 8 69 3290 1458 52 331 15 2643 18 65 107 53 32 1 392 580 44 256 20 3
CD4 TEM 6 0 0 0 0 69 0 0 0 0 1 0 0 0 15 0 0 3 0 0 0 0 0
CD8 Naive 1315 0 0 0 0 15 0 0 0 0 0 0 2 0 1 1 0 18 1 0 17 2 0
CD8 Proliferating 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
CD8 TCM 121 1 0 0 0 237 0 0 9 0 0 0 2 0 92 0 0 6 7 0 1 0 0
CD8 TEM 5 0 0 1 0 40 7 0 5 0 0 1 1 0 324 0 0 0 0 0 7 0 0
cDC1 0 0 0 0 0 0 5 0 0 0 0 13 0 0 0 0 0 0 0 2 1 0 21
cDC2 0 0 0 0 1 0 47 0 1 0 0 122 0 0 0 0 0 0 0 3 0 2 53
dnT 7 0 0 0 0 24 7 0 0 0 1 0 2 0 0 3 0 17 0 0 20 0 0
gdT 9 0 0 0 0 6 0 0 0 0 0 0 0 0 77 0 0 0 0 0 1 0 0
HSPC 0 1 54 0 705 0 37 757 0 197 3 6 1 0 1 0 1 3 6 39 7 0 4
ILC 0 0 0 0 0 0 0 0 0 0 0 0 1 0 4 0 0 1 0 0 1 0 0
MAIT 0 0 0 0 0 8 0 0 0 0 0 0 0 0 228 0 0 1 0 0 3 2 0
NK 0 0 0 0 0 0 0 0 0 0 0 0 1 0 518 1 0 1 0 0 7 6 0
NK Proliferating 0 0 9 2374 5 0 8 6 443 218 0 0 0 15 2 0 38 5 0 11 32 0 0
NK_CD56bright 0 0 0 0 0 0 0 0 0 0 0 0 0 0 14 0 0 0 0 0 2 0 0
pDC 0 0 0 0 0 0 0 0 0 0 0 56 0 0 0 0 0 0 0 0 0 0 0
Plasmablast 0 0 0 0 0 0 0 0 0 0 0 5 11 0 0 0 0 2 0 0 1 0 0
Platelet 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0
Treg 90 0 0 0 0 166 9 0 1 0 0 0 5 0 0 0 0 41 0 0 39 1 1
23 24
ASDC 0 0
B intermediate 0 0
B memory 1 0
B naive 0 0
CD14 Mono 3 0
CD16 Mono 0 0
CD4 CTL 0 0
CD4 Naive 0 1
CD4 Proliferating 0 0
CD4 TCM 0 11
CD4 TEM 0 0
CD8 Naive 1 0
CD8 Proliferating 0 0
CD8 TCM 0 1
CD8 TEM 0 0
cDC1 0 0
cDC2 0 0
dnT 0 1
gdT 0 0
HSPC 7 5
ILC 0 0
MAIT 0 0
NK 0 0
NK Proliferating 1 0
NK_CD56bright 0 0
pDC 0 0
Plasmablast 0 0
Platelet 30 0
Treg 0 0table(All_samples_Merged$cell_line, All_samples_Merged$Harmony_snn_res.0.8)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
L1 0 2 224 0 1 0 0 0 3026 16 1919 0 0 0 25 1 599 0 12 0 0 0 0 0
L2 2 0 5130 0 1 2 0 0 9 0 750 0 0 0 8 0 22 0 11 0 0 0 0 0
L3 6174 2 0 0 0 37 1 0 1 0 0 4 95 0 6 1 1 68 36 0 0 0 2 0
L4 3671 0 0 0 4 588 3 0 2 6 0 158 1607 1 18 12 2 57 18 1 0 2 0 0
L5 1 1 0 388 3049 448 4 1781 0 0 0 0 0 0 13 0 3 316 17 0 0 1 0 0
L6 14 1 0 3166 118 1225 2 326 0 0 0 0 0 1 23 0 8 106 158 0 0 0 0 0
L7 3 1 0 1208 804 1565 4 1639 0 0 0 0 0 0 19 0 2 57 29 0 0 0 0 0
PBMC 1 2796 0 0 0 24 2577 0 6 43 4 1097 0 401 449 743 2 3 81 8 3 91 6 19
PBMC_10x 4 3451 0 0 0 37 1179 0 14 2618 7 938 0 912 365 130 15 0 209 141 107 0 35 0Harmony Visualization-5
clustree(All_samples_Merged, prefix = "Harmony_snn_res.")
Harmony Visualization-6
# Function to create and save tables for a given variable
create_and_save_tables <- function(variable_name) {
# Loop through resolutions from 0.5 to 1.2 with a step of 0.1
for (res in seq(0.5, 1.2, by = 0.1)) {
# Create the column name for the current resolution
col_name <- paste0("Harmony_snn_res.", res)
# Create the cross-tabulation
table_res <- table(All_samples_Merged[[variable_name]], All_samples_Merged[[col_name]])
# Create a filename for the current resolution
filename <- paste0(variable_name, "_vs_resolution_", res, ".csv")
# Save the table to CSV
write.csv(table_res, filename)
# Print a message to confirm the file was saved
print(paste("Saved:", filename))
}
}
# Create and save tables for predicted cell types
create_and_save_tables("predicted.celltype.l2")Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)[1] "Saved: predicted.celltype.l2_vs_resolution_0.5.csv"Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)[1] "Saved: predicted.celltype.l2_vs_resolution_0.6.csv"Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)[1] "Saved: predicted.celltype.l2_vs_resolution_0.7.csv"Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)[1] "Saved: predicted.celltype.l2_vs_resolution_0.8.csv"Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)[1] "Saved: predicted.celltype.l2_vs_resolution_0.9.csv"Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)[1] "Saved: predicted.celltype.l2_vs_resolution_1.csv"Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)[1] "Saved: predicted.celltype.l2_vs_resolution_1.1.csv"Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)[1] "Saved: predicted.celltype.l2_vs_resolution_1.2.csv"# Create and save tables for cell lines
create_and_save_tables("cell_line")Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)[1] "Saved: cell_line_vs_resolution_0.5.csv"Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)[1] "Saved: cell_line_vs_resolution_0.6.csv"Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)[1] "Saved: cell_line_vs_resolution_0.7.csv"Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)[1] "Saved: cell_line_vs_resolution_0.8.csv"Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)[1] "Saved: cell_line_vs_resolution_0.9.csv"Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)[1] "Saved: cell_line_vs_resolution_1.csv"Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)[1] "Saved: cell_line_vs_resolution_1.1.csv"Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)Avis : impossible d’utiliser xtfrm sur un tableau de données (data frame)[1] "Saved: cell_line_vs_resolution_1.2.csv"4. Save the Seurat object as an Robj file
#save(All_samples_Merged, file = "../../../0-IMP-OBJECTS/Harmony_integrated_All_samples_Merged_with_PBMC10x.Robj")
---
title: "Harmony_Intergrations_and_visualization_of_PBMC-10x."
author: Nasir Mahmood Abbasi
date: "`r Sys.Date()`"
output:
  #rmdformats::readthedown
  html_notebook:
    toc: true
    toc_float: true
    toc_collapsed: true
---


# 1. load libraries
```{r setup, include=FALSE}
library(Seurat)
library(SeuratWrappers)
library(SeuratObject)
library(SeuratData)
library(patchwork)
library(harmony)
library(ggplot2)
library(reticulate)
library(Azimuth)
library(dplyr)
library(Rtsne)
library(harmony)

options(future.globals.maxSize = 1e9)

```




# 2. Load Seurat Object and visualize harmonyintegrated_UMAP
```{r load_seurat}

#Load Seurat Object merged from cell lines and a control(PBMC) after filtration
load("../../../0-IMP-OBJECTS/Harmony_integrated_All_samples_Merged_with_PBMC10x.Robj")

All_samples_Merged

DimPlot(All_samples_Merged, reduction = "umap.harmony", group.by = "cell_line",label = T, label.box = T)

```



# 3. UMAP visualization and Harmony Integration-1 Visualization
```{r data1, fig.height=8, fig.width=12}


DimPlot(All_samples_Merged, reduction = "umap", group.by = "cell_line", label = T, label.box = T)

DimPlot(All_samples_Merged, reduction = "umap.harmony", group.by = "cell_line",label = T, label.box = T)

```

##  Harmony Visualization-2
```{r harmony-visualization2, fig.height=8, fig.width=12}
library(harmony)
library(clustree)

# Ensure the Harmony reduction is present
if (!"harmony" %in% Reductions(All_samples_Merged)) {
  stop("Harmony reduction not found. Please run RunHarmony() first.")
}

# Run clustering using Harmony embeddings
All_samples_Merged <- FindNeighbors(All_samples_Merged, reduction = "harmony", dims = 1:22)

# Create a sequence of resolutions from 0.1 to 2 with 0.1 increments
resolutions <- seq(0.1, 2, by = 0.1)

# Loop through the resolutions
for (res in resolutions) {
  # Find clusters for the current resolution using Harmony embeddings
  All_samples_Merged <- FindClusters(All_samples_Merged, resolution = res)
  
  # Create a new column for Harmony-based clustering
  new_col_name <- paste0("Harmony_snn_res.", res)
  All_samples_Merged[[new_col_name]] <- All_samples_Merged$seurat_clusters  # Store cluster results here
  
  # Create the plot for each resolution
  plot <- DimPlot(All_samples_Merged,
                  group.by = new_col_name,
                  reduction = "umap.harmony",
                  label.size = 3,
                  repel = TRUE,
                  label = TRUE,
                  label.box = TRUE) +
          ggtitle(paste("Harmony-based Clustering, Resolution:", res))
  
  # Display the plot directly in the notebook
  print(plot)
}

# Optionally, create a plot for a specific resolution (e.g., 0.5)
seurat_plot <- DimPlot(All_samples_Merged,
                       group.by = "Harmony_snn_res.0.9",
                       reduction = "umap.harmony",
                       label.size = 3,
                       repel = TRUE,
                       label = TRUE,
                       label.box = TRUE) +
                ggtitle("Harmony-based Seurat Clusters (Resolution: 0.9)")

# Display the specific resolution plot
print(seurat_plot)
```


##  Harmony Visualization-3
```{r harmony-visualization3, fig.height=8, fig.width=12}

# Combine clusters and cell lines for visualization
All_samples_Merged$Cluster_CellLine <- paste(All_samples_Merged$Harmony_snn_res.0.7, All_samples_Merged$cell_line, sep = "_")

# Plot with combined information
DimPlot(All_samples_Merged, group.by = "Cluster_CellLine", reduction = "umap.harmony", label = TRUE, label.size = 3) +
  ggtitle("Clusters and Cell Lines on UMAP") + theme(legend.position = "none")


# Plot with combined information
DimPlot(All_samples_Merged, group.by = "Cluster_CellLine", reduction = "umap.harmony", label = TRUE, label.size = 3, label.box = T) +
  ggtitle("Clusters and Cell Lines on UMAP") + theme(legend.position = "none")


p1 <- DimPlot(All_samples_Merged, 
              group.by = "Harmony_snn_res.0.7", 
              reduction = "umap.harmony", 
              label = TRUE, 
              repel = TRUE,
              label.box = TRUE,
              pt.size = 0.5) +
  ggtitle("Clusters on UMAP") +
  NoLegend()

p2 <- DimPlot(All_samples_Merged, 
              group.by = "cell_line", 
              reduction = "umap.harmony", 
              label = TRUE, 
              repel = TRUE,
              label.box = TRUE,
              pt.size = 0.5) +
  ggtitle("Cell Lines on UMAP") +
  NoLegend()

# Display plots side by side
p1 | p2

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

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

DimPlot(All_samples_Merged,
        group.by = "predicted.celltype.l3",
        reduction = "umap.harmony",
        label.size = 3,
        repel = T, 
        label = T, label.box = T)





```

##  Harmony Visualization-4
```{r harmony-visualization4, fig.height=8, fig.width=12}

# Set identity classes to an existing column in meta data
Idents(object = All_samples_Merged) <- "Harmony_snn_res.0.7"

cluster_table <- table(Idents(All_samples_Merged))


table(All_samples_Merged$predicted.celltype.l2, All_samples_Merged$Harmony_snn_res.0.5)
table(All_samples_Merged$predicted.celltype.l2, All_samples_Merged$Harmony_snn_res.0.6)
table(All_samples_Merged$predicted.celltype.l2, All_samples_Merged$Harmony_snn_res.0.7)
table(All_samples_Merged$predicted.celltype.l2, All_samples_Merged$Harmony_snn_res.0.8)
table(All_samples_Merged$predicted.celltype.l2, All_samples_Merged$Harmony_snn_res.0.9)

table(All_samples_Merged$cell_line, All_samples_Merged$Harmony_snn_res.0.8)

```


##  Harmony Visualization-5
```{r harmony-visualization5, fig.height=8, fig.width=12}


clustree(All_samples_Merged, prefix = "Harmony_snn_res.")

```
##  Harmony Visualization-6
```{r harmony-visualization6, fig.height=8, fig.width=12}

# Function to create and save tables for a given variable
create_and_save_tables <- function(variable_name) {
  # Loop through resolutions from 0.5 to 1.2 with a step of 0.1
  for (res in seq(0.5, 1.2, by = 0.1)) {
    # Create the column name for the current resolution
    col_name <- paste0("Harmony_snn_res.", res)
    
    # Create the cross-tabulation
    table_res <- table(All_samples_Merged[[variable_name]], All_samples_Merged[[col_name]])
    
    # Create a filename for the current resolution
    filename <- paste0(variable_name, "_vs_resolution_", res, ".csv")
    
    # Save the table to CSV
    write.csv(table_res, filename)
    
    # Print a message to confirm the file was saved
    print(paste("Saved:", filename))
  }
}

# Create and save tables for predicted cell types
create_and_save_tables("predicted.celltype.l2")

# Create and save tables for cell lines
create_and_save_tables("cell_line")




```


# 4. Save the Seurat object as an Robj file
```{r saveROBJ}

#save(All_samples_Merged, file = "../../../0-IMP-OBJECTS/Harmony_integrated_All_samples_Merged_with_PBMC10x.Robj")

```




