1. load libraries

2. Load Seurat Object


#Load Seurat Object merged from cell lines and a control(PBMC) after filtration
SS_All_samples_Merged <- load("/home/bioinfo/Documents/1-SS-STeps/0-imp_OBJ_SS/All_Normal-PBMC_Abnormal-cellLines_T_cells_Merged_Annotated_UMAP_on_Clusters_to_USE.Robj")

All_samples_Merged
An object of class Seurat 
62625 features across 46976 samples within 6 assays 
Active assay: SCT (25901 features, 3000 variable features)
 3 layers present: counts, data, scale.data
 5 other assays present: RNA, ADT, prediction.score.celltype.l1, prediction.score.celltype.l2, prediction.score.celltype.l3
 4 dimensional reductions calculated: pca, umap, integrated_dr, ref.umap

3. QC


Idents(All_samples_Merged) <- "cell_line"
VlnPlot(All_samples_Merged, features = c("nFeature_RNA", 
                                    "nCount_RNA", 
                                    "percent.mito"), 
                                      ncol = 3)


VlnPlot(All_samples_Merged, features = c("nFeature_RNA", 
                                         "nCount_RNA", 
                                         "percent.mt",
                                         "percent.rb"), 
                            ncol = 4, pt.size = 0.1) & 
              theme(plot.title = element_text(size=10))


FeatureScatter(All_samples_Merged, 
               feature1 = "nCount_RNA", 
               feature2 = "nFeature_RNA") +
  geom_smooth(method = 'lm')

##FeatureScatter is typically used to visualize feature-feature relationships ##for anything calculated by the object, ##i.e. columns in object metadata, PC scores etc.


FeatureScatter(All_samples_Merged, 
               feature1 = "nCount_RNA", 
               feature2 = "percent.mito")+
  geom_smooth(method = 'lm')


FeatureScatter(All_samples_Merged, 
               feature1 = "nCount_RNA", 
               feature2 = "nFeature_RNA")+
  geom_smooth(method = 'lm')

4. Perform PCA

5. Perform PCA TEST




# TEST-1
# given that the output of RunPCA is "pca"
# replace "so" by the name of your seurat object

pct <- All_samples_Merged[["pca"]]@stdev / sum(All_samples_Merged[["pca"]]@stdev) * 100
cumu <- cumsum(pct) # Calculate cumulative percents for each PC
# Determine the difference between variation of PC and subsequent PC
co2 <- sort(which((pct[-length(pct)] - pct[-1]) > 0.1), decreasing = T)[1] + 1
# last point where change of % of variation is more than 0.1%. -> co2
co2
[1] 13
# TEST-2
# get significant PCs
stdv <- All_samples_Merged[["pca"]]@stdev
sum.stdv <- sum(All_samples_Merged[["pca"]]@stdev)
percent.stdv <- (stdv / sum.stdv) * 100
cumulative <- cumsum(percent.stdv)
co1 <- which(cumulative > 90 & percent.stdv < 5)[1]
co2 <- sort(which((percent.stdv[1:length(percent.stdv) - 1] - 
                       percent.stdv[2:length(percent.stdv)]) > 0.1), 
              decreasing = T)[1] + 1
min.pc <- min(co1, co2)
min.pc
[1] 13
# Create a dataframe with values
plot_df <- data.frame(pct = percent.stdv, 
           cumu = cumulative, 
           rank = 1:length(percent.stdv))

# Elbow plot to visualize 
  ggplot(plot_df, aes(cumulative, percent.stdv, label = rank, color = rank > min.pc)) + 
  geom_text() + 
  geom_vline(xintercept = 90, color = "grey") + 
  geom_hline(yintercept = min(percent.stdv[percent.stdv > 5]), color = "grey") +
  theme_bw()

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6. Harmony TEST


P1 <- DimPlot(object = All_samples_Merged, group.by = "cell_line", label = T, label.box = T) + 
  labs(title = 'Colored by cellline')
P1



P2 <- DimPlot(object = All_samples_Merged, group.by = "predicted.celltype.l2") + 
  labs(title = 'Colored by celltype')
P2



P3 <- DimPlot(object = All_samples_Merged, group.by = "cell_line", reduction = "pca") + 
  labs(title = 'Colored by cellline')
P3



P4 <- DimPlot(object = All_samples_Merged, group.by = "predicted.celltype.l2", reduction = "pca") + 
  labs(title = 'Colored by celltype')
P4


cowplot::plot_grid(P1, P2, P3, P4, nrow = 2)


cell_distribution_table <- table(All_samples_Merged$predicted.celltype.l2, All_samples_Merged$seurat_clusters)

cell_distribution_df <- as.data.frame.matrix(cell_distribution_table)


print(cell_distribution_df)


write.csv(cell_distribution_df, file = "test2.csv", row.names = TRUE)

6. Apply Harmony



All_samples_Merged_integrated <- RunHarmony(All_samples_Merged, "cell_line")
Transposing data matrix
Initializing state using k-means centroids initialization
Harmony 1/10
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Harmony 2/10
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Harmony converged after 2 iterations
# Do UMAP and clustering using ** Harmony embeddings instead of PCA **
All_samples_Merged_integrated <- All_samples_Merged_integrated %>%
   RunUMAP(reduction = 'harmony', dims = 1:13) %>%
   FindNeighbors(reduction = "harmony", dims = 1:13) %>%
   FindClusters(resolution = 0.5)
17:01:56 UMAP embedding parameters a = 0.9922 b = 1.112
17:01:56 Read 46976 rows and found 13 numeric columns
17:01:56 Using Annoy for neighbor search, n_neighbors = 30
17:01:56 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:01:59 Writing NN index file to temp file /tmp/RtmplSHSp6/file180302c135d88
17:01:59 Searching Annoy index using 1 thread, search_k = 3000
17:02:11 Annoy recall = 100%
17:02:12 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
17:02:14 Initializing from normalized Laplacian + noise (using RSpectra)
17:02:15 Commencing optimization for 200 epochs, with 1955114 positive edges
Using method 'umap'
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:02:30 Optimization finished
Computing nearest neighbor graph
Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 46976
Number of edges: 1404500

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8881
Number of communities: 12
Elapsed time: 11 seconds

DimPlot(object = All_samples_Merged_integrated, group.by = "predicted.celltype.l2", label = T, label.box = T, repel = T, reduction = "umap")


DimPlot(object = All_samples_Merged_integrated, group.by = "predicted.celltype.l2", label = T, label.box = T, repel = T, reduction = "harmony")


DimPlot(object = All_samples_Merged_integrated, group.by = "predicted.celltype.l2", label = T, label.box = T, repel = T, reduction = "integrated_dr")


DimPlot(object = All_samples_Merged_integrated, group.by = "predicted.celltype.l2", label = T, label.box = T, repel = T, reduction = "pca")


DimPlot(object = All_samples_Merged_integrated, group.by = "predicted.celltype.l2", label = T, label.box = T, repel = T, reduction = "ref.umap")



DimPlot(object = All_samples_Merged_integrated, group.by = "cell_line", label = T, label.box = T, repel = T, reduction = "umap")


DimPlot(object = All_samples_Merged_integrated, group.by = "seurat_clusters", label = T, label.box = T, repel = T, reduction = "umap")

7. Cell Distribution



cell_distribution_table <- table(All_samples_Merged_integrated$cell_line, All_samples_Merged_integrated$seurat_clusters)

cell_distribution_df <- as.data.frame.matrix(cell_distribution_table)


print(cell_distribution_df)


write.csv(cell_distribution_df, file = "15-2-integration_table_HARMONY-TEST1.csv", row.names = TRUE)


cell_distribution_table <- table(All_samples_Merged_integrated$predicted.celltype.l2, All_samples_Merged_integrated$seurat_clusters)

cell_distribution_df <- as.data.frame.matrix(cell_distribution_table)


print(cell_distribution_df)


write.csv(cell_distribution_df, file = "15-2-integration_table_HARMONY-TEST1_annotationbased.csv", row.names = TRUE)


cell_distribution_table <- table(All_samples_Merged_integrated$predicted.celltype.l1, All_samples_Merged_integrated$seurat_clusters)

cell_distribution_df <- as.data.frame.matrix(cell_distribution_table)


print(cell_distribution_df)


write.csv(cell_distribution_df, file = "15-2-integration_table_HARMONY-TEST1_annotationbased_l1.csv", row.names = TRUE)

cell_distribution_table <- table(All_samples_Merged_integrated$predicted.celltype.l2, All_samples_Merged_integrated$cell_line)

cell_distribution_df <- as.data.frame.matrix(cell_distribution_table)


print(cell_distribution_df)


write.csv(cell_distribution_df, file = "15-2-integration_table_HARMONY-TEST1_annotationbased_cellline.csv", row.names = TRUE)

Save the Seurat object as an Robj file———————————————————————-



# save
#save(All_samples_Merged_integrated,file = "15-2_All_samples_Merged_integrated.Robj")

```

---
title: "PC test and harmony integration on SCT"
author: Nasir Mahmood Abbasi
date: "2024-06-05"
output:
  html_notebook: 
    toc: true
    toc_float: true
    toc_collapsed: true
    theme: darkly
---

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

library(Seurat)
library(SeuratObject)
library(SeuratData)
library(patchwork)
library(Azimuth)
library(dplyr)
library(ggplot2)
library(tidyverse)
library(rmarkdown)
library(tinytex)


library(dplyr)
library(dittoSeq)
library(ggrepel)
#library(ggtree)
library(parallel)
library(plotly)  # 3D plot
library(Seurat)  # Idents()
library(SeuratDisk)  # SaveH5Seurat()
library(tibble)  # rownnames_to_column
library(harmony) # RunHarmony()
#options(mc.cores = detectCores() - 1)



```


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

#Load Seurat Object merged from cell lines and a control(PBMC) after filtration
SS_All_samples_Merged <- load("/home/bioinfo/Documents/1-SS-STeps/0-imp_OBJ_SS/All_Normal-PBMC_Abnormal-cellLines_T_cells_Merged_Annotated_UMAP_on_Clusters_to_USE.Robj")

All_samples_Merged
```
# 3. QC
```{r QC, fig.height=6, fig.width=10}

Idents(All_samples_Merged) <- "cell_line"
VlnPlot(All_samples_Merged, features = c("nFeature_RNA", 
                                    "nCount_RNA", 
                                    "percent.mito"), 
                                      ncol = 3)

VlnPlot(All_samples_Merged, features = c("nFeature_RNA", 
                                         "nCount_RNA", 
                                         "percent.mt",
                                         "percent.rb"), 
                            ncol = 4, pt.size = 0.1) & 
              theme(plot.title = element_text(size=10))

FeatureScatter(All_samples_Merged, 
               feature1 = "nCount_RNA", 
               feature2 = "nFeature_RNA") +
  geom_smooth(method = 'lm')

```

##FeatureScatter is typically used to visualize feature-feature relationships
##for anything calculated by the object, 
##i.e. columns in object metadata, PC scores etc.

```{r FC, fig.height=6, fig.width=10}

FeatureScatter(All_samples_Merged, 
               feature1 = "nCount_RNA", 
               feature2 = "percent.mito")+
  geom_smooth(method = 'lm')

FeatureScatter(All_samples_Merged, 
               feature1 = "nCount_RNA", 
               feature2 = "nFeature_RNA")+
  geom_smooth(method = 'lm')

```
# 4. Perform PCA
```{r PCA, fig.height=6, fig.width=10}


ElbowPlot(All_samples_Merged, ndims = 50)


```

# 5. Perform PCA TEST
```{r PCA-TEST2, fig.height=6, fig.width=10}



# TEST-1
# given that the output of RunPCA is "pca"
# replace "so" by the name of your seurat object

pct <- All_samples_Merged[["pca"]]@stdev / sum(All_samples_Merged[["pca"]]@stdev) * 100
cumu <- cumsum(pct) # Calculate cumulative percents for each PC
# Determine the difference between variation of PC and subsequent PC
co2 <- sort(which((pct[-length(pct)] - pct[-1]) > 0.1), decreasing = T)[1] + 1
# last point where change of % of variation is more than 0.1%. -> co2
co2

# TEST-2
# get significant PCs
stdv <- All_samples_Merged[["pca"]]@stdev
sum.stdv <- sum(All_samples_Merged[["pca"]]@stdev)
percent.stdv <- (stdv / sum.stdv) * 100
cumulative <- cumsum(percent.stdv)
co1 <- which(cumulative > 90 & percent.stdv < 5)[1]
co2 <- sort(which((percent.stdv[1:length(percent.stdv) - 1] - 
                       percent.stdv[2:length(percent.stdv)]) > 0.1), 
              decreasing = T)[1] + 1
min.pc <- min(co1, co2)
min.pc

# Create a dataframe with values
plot_df <- data.frame(pct = percent.stdv, 
           cumu = cumulative, 
           rank = 1:length(percent.stdv))

# Elbow plot to visualize 
  ggplot(plot_df, aes(cumulative, percent.stdv, label = rank, color = rank > min.pc)) + 
  geom_text() + 
  geom_vline(xintercept = 90, color = "grey") + 
  geom_hline(yintercept = min(percent.stdv[percent.stdv > 5]), color = "grey") +
  theme_bw()

  











```



# 6. Harmony TEST
```{r PCA-TEST, fig.height=6, fig.width=10}

P1 <- DimPlot(object = All_samples_Merged, group.by = "cell_line", label = T, label.box = T) + 
  labs(title = 'Colored by cellline')
P1


P2 <- DimPlot(object = All_samples_Merged, group.by = "predicted.celltype.l2") + 
  labs(title = 'Colored by celltype')
P2


P3 <- DimPlot(object = All_samples_Merged, group.by = "cell_line", reduction = "pca") + 
  labs(title = 'Colored by cellline')
P3


P4 <- DimPlot(object = All_samples_Merged, group.by = "predicted.celltype.l2", reduction = "pca") + 
  labs(title = 'Colored by celltype')
P4

cowplot::plot_grid(P1, P2, P3, P4, nrow = 2)

cell_distribution_table <- table(All_samples_Merged$predicted.celltype.l2, All_samples_Merged$seurat_clusters)

cell_distribution_df <- as.data.frame.matrix(cell_distribution_table)


print(cell_distribution_df)


write.csv(cell_distribution_df, file = "test2.csv", row.names = TRUE)


```
# 6. Apply Harmony
```{r C1, fig.height=6, fig.width=10}


All_samples_Merged_integrated <- RunHarmony(All_samples_Merged, "cell_line")
# Do UMAP and clustering using ** Harmony embeddings instead of PCA **
All_samples_Merged_integrated <- All_samples_Merged_integrated %>%
   RunUMAP(reduction = 'harmony', dims = 1:13) %>%
   FindNeighbors(reduction = "harmony", dims = 1:13) %>%
   FindClusters(resolution = 0.5)
```


```{r C2, fig.height=6, fig.width=10}

DimPlot(object = All_samples_Merged_integrated, group.by = "predicted.celltype.l2", label = T, label.box = T, repel = T, reduction = "umap")

DimPlot(object = All_samples_Merged_integrated, group.by = "predicted.celltype.l2", label = T, label.box = T, repel = T, reduction = "harmony")

DimPlot(object = All_samples_Merged_integrated, group.by = "predicted.celltype.l2", label = T, label.box = T, repel = T, reduction = "integrated_dr")

DimPlot(object = All_samples_Merged_integrated, group.by = "predicted.celltype.l2", label = T, label.box = T, repel = T, reduction = "pca")

DimPlot(object = All_samples_Merged_integrated, group.by = "predicted.celltype.l2", label = T, label.box = T, repel = T, reduction = "ref.umap")


DimPlot(object = All_samples_Merged_integrated, group.by = "cell_line", label = T, label.box = T, repel = T, reduction = "umap")

DimPlot(object = All_samples_Merged_integrated, group.by = "seurat_clusters", label = T, label.box = T, repel = T, reduction = "umap")

```

# 7. Cell Distribution
```{r cellD}


cell_distribution_table <- table(All_samples_Merged_integrated$cell_line, All_samples_Merged_integrated$seurat_clusters)

cell_distribution_df <- as.data.frame.matrix(cell_distribution_table)


print(cell_distribution_df)


write.csv(cell_distribution_df, file = "15-2-integration_table_HARMONY-TEST1.csv", row.names = TRUE)


cell_distribution_table <- table(All_samples_Merged_integrated$predicted.celltype.l2, All_samples_Merged_integrated$seurat_clusters)

cell_distribution_df <- as.data.frame.matrix(cell_distribution_table)


print(cell_distribution_df)


write.csv(cell_distribution_df, file = "15-2-integration_table_HARMONY-TEST1_annotationbased.csv", row.names = TRUE)


cell_distribution_table <- table(All_samples_Merged_integrated$predicted.celltype.l1, All_samples_Merged_integrated$seurat_clusters)

cell_distribution_df <- as.data.frame.matrix(cell_distribution_table)


print(cell_distribution_df)


write.csv(cell_distribution_df, file = "15-2-integration_table_HARMONY-TEST1_annotationbased_l1.csv", row.names = TRUE)

cell_distribution_table <- table(All_samples_Merged_integrated$predicted.celltype.l2, All_samples_Merged_integrated$cell_line)

cell_distribution_df <- as.data.frame.matrix(cell_distribution_table)


print(cell_distribution_df)


write.csv(cell_distribution_df, file = "15-2-integration_table_HARMONY-TEST1_annotationbased_cellline.csv", row.names = TRUE)


```

# Save the Seurat object as an Robj file----------------------------------------------------------------------
```{r saveRDS}


# save
#save(All_samples_Merged_integrated,file = "15-2_All_samples_Merged_integrated.Robj")

```

```



