Differential Expression Analysis using Harmony Integrated Clusters
Nasir Mahmood Abbasi
2025-01-18
1. Load Libraries
2. Load Seurat Object
#Load Seurat Object merged from cell lines and a control(PBMC) after filtration
load("0-imp_Robj/Harmony_integrated_CD4Tcells_harmony_integrated.Robj")
All_samples_Merged An object of class Seurat
62926 features across 49360 samples within 6 assays
Active assay: SCT (26174 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.harmony3. Set Up Identifiers for Clustering
# Assign cluster identities to the Seurat object
Idents(All_samples_Merged) <- "seurat_clusters"
DimPlot(All_samples_Merged, reduction = "umap.harmony", group.by = "seurat_clusters",label = T, label.box = T, repel = T) +
ggtitle("Harmony Integration - By Clusters")
4. Differential Expression Analysis
4.1 Using MAST with Latent Variables (Batch Correction)
markers_mast_batch <- FindMarkers(
All_samples_Merged,
ident.1 = c("0", "1", "2", "3", "4", "5", "7", "9"), # Cell lines
ident.2 = c("6", "8", "10", "11"), # CD4 T cells
assay = "SCT",
test.use = "MAST",
latent.vars = c("cell_line"), # Adjust for batch effects
min.pct = 0,
logfc.threshold = 0
)Avis : sparse->dense coercion: allocating vector of size 9.6 GiBAvis : Dropping illegal slot(s) norm.method for class BayesGLMlike.
This likely indicates a bug in an upstream package.
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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Processed 219380 groups out of 471132. 47% done. Time elapsed: 3s. ETA: 3s.
Processed 289789 groups out of 471132. 62% done. Time elapsed: 4s. ETA: 2s.
Processed 361491 groups out of 471132. 77% done. Time elapsed: 5s. ETA: 1s.
Processed 436911 groups out of 471132. 93% done. Time elapsed: 6s. ETA: 0s.
Processed 471132 groups out of 471132. 100% done. Time elapsed: 6s. ETA: 0s.Calculating likelihood ratio tests
Refitting on reduced model...
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Done!gc() used (Mb) gc trigger (Mb) max used (Mb)
Ncells 8388915 448.1 15661334 836.5 15661334 836.5
Vcells 1232996630 9407.1 6392433025 48770.4 7990541281 60963.0gc() used (Mb) gc trigger (Mb) max used (Mb)
Ncells 8388837 448.1 15661334 836.5 15661334 836.5
Vcells 1232961244 9406.8 5113946420 39016.4 7990541281 60963.0write.csv(markers1, file = "0-imp_Robj/1-MAST_with_batch_as_Covariate.csv", row.names = TRUE)Erreur dans is.data.frame(x) : objet 'markers1' introuvable4.2 Using Wilcoxon Test (Without Batch Correction)
4.3 Using MAST Without Latent Variables (No Batch Correction)
markers_mast_no_batch <- FindMarkers(
All_samples_Merged,
ident.1 = c("0", "1", "2", "3", "4", "5", "7", "9"), # Cell lines
ident.2 = c("6", "8", "10", "11"), # CD4 T cells
assay = "SCT",
test.use = "MAST",
min.pct = 0,
logfc.threshold = 0
)Avis : sparse->dense coercion: allocating vector of size 9.6 GiBAvis : Dropping illegal slot(s) norm.method for class BayesGLMlike.
This likely indicates a bug in an upstream package.
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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
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Done!View(markers_mast_batch)
View(markers_wilcox)
View(markers_wilcox)
View(markers_wilcox)
write.csv(markers_mast_no_batch, file = "0-imp_Robj/3-MAST_without_batch_as_Covariate.csv", row.names = TRUE)5. Compute Mean Expression for Groups
# Define cell groups
group1_cells <- WhichCells(All_samples_Merged, idents = c("0", "1", "2", "3", "4", "5", "7", "9"))
group2_cells <- WhichCells(All_samples_Merged, idents = c("6", "8", "10", "11"))
# Extract normalized expression values
expression_data <- GetAssayData(All_samples_Merged, slot = "data") # Log-normalized valuesAvis : The `slot` argument of `GetAssayData()` is deprecated as of SeuratObject 5.0.0.
Please use the `layer` argument instead.# Calculate mean expression for each group
calculate_mean_expression <- function(markers, group1_cells, group2_cells, expression_data) {
group1_mean <- rowMeans(expression_data[, group1_cells])
group2_mean <- rowMeans(expression_data[, group2_cells])
markers <- markers %>%
rownames_to_column("gene") %>%
mutate(mean_expr_group1 = group1_mean[gene],
mean_expr_group2 = group2_mean[gene])
return(markers)
}
markers_mast_batch <- calculate_mean_expression(markers_mast_batch, group1_cells, group2_cells, expression_data)
markers_wilcox <- calculate_mean_expression(markers_wilcox, group1_cells, group2_cells, expression_data)
markers_mast_no_batch <- calculate_mean_expression(markers_mast_no_batch, group1_cells, group2_cells, expression_data)
# Save updated results with mean expression
write.csv(markers_mast_batch, file = "0-imp_Robj/1-MAST_with_batch_as_Covariate_with_meanExpression.csv", row.names = TRUE)
write.csv(markers_wilcox, file = "0-imp_Robj/2-Wilcox_min.pct_logfcT-0_with_meanExpression.csv", row.names = TRUE)
write.csv(markers_mast_no_batch, file = "0-imp_Robj/3-MAST_without_batch_as_Covariate_with_meanExpression.csv", row.names = TRUE)6. Summarize Results
6.1 Summary Function
summarize_markers <- function(markers) {
num_pval0 <- sum(markers$p_val_adj == 0)
num_pval1 <- sum(markers$p_val_adj == 1)
num_significant <- sum(markers$p_val_adj < 0.05)
num_upregulated <- sum(markers$avg_log2FC > 1)
num_downregulated <- sum(markers$avg_log2FC < -1)
cat("Number of genes with p_val_adj = 0:", num_pval0, "\n")
cat("Number of genes with p_val_adj = 1:", num_pval1, "\n")
cat("Number of significant genes (p_val_adj < 0.05):", num_significant, "\n")
cat("Number of upregulated genes (avg_log2FC > 1):", num_upregulated, "\n")
cat("Number of downregulated genes (avg_log2FC < -1):", num_downregulated, "\n")
}
6.2 Summarize Markers1 (MAST with Batch Correction)
cat("Markers1 Summary (MAST with Batch Correction):\n")Markers1 Summary (MAST with Batch Correction):summarize_markers(markers_mast_batch)Number of genes with p_val_adj = 0: 1
Number of genes with p_val_adj = 1: 16799
Number of significant genes (p_val_adj < 0.05): 8601
Number of upregulated genes (avg_log2FC > 1): 14796
Number of downregulated genes (avg_log2FC < -1): 2494 6.3 Summary for Markers2 (Wilcoxon Test)
cat("Markers2 Summary (Wilcoxon Test):\n")Markers2 Summary (Wilcoxon Test):summarize_markers(markers_wilcox)Number of genes with p_val_adj = 0: 4502
Number of genes with p_val_adj = 1: 10049
Number of significant genes (p_val_adj < 0.05): 15357
Number of upregulated genes (avg_log2FC > 1): 14796
Number of downregulated genes (avg_log2FC < -1): 2494 6.4 Summary for Markers3 (MAST without Batch Correction)
cat("Markers3 Summary (MAST without Batch Correction):\n")Markers3 Summary (MAST without Batch Correction):summarize_markers(markers_mast_no_batch)Number of genes with p_val_adj = 0: 4957
Number of genes with p_val_adj = 1: 9570
Number of significant genes (p_val_adj < 0.05): 15882
Number of upregulated genes (avg_log2FC > 1): 14796
Number of downregulated genes (avg_log2FC < -1): 2494 7. Visualization
7.1 Volcano Plot for MAST with Batch Correction
EnhancedVolcano(markers_mast_batch,
lab = markers_mast_batch$gene,
x = "avg_log2FC",
y = "p_val_adj",
title = "MAST with Batch Correction",
pCutoff = 0.05,
FCcutoff = 1.0)Avis : One or more p-values is 0. Converting to 10^-1 * current lowest non-zero p-value...
7.2 Volcano Plot for Wilcoxon Test
EnhancedVolcano(markers_wilcox,
lab = markers_wilcox$gene,
x = "avg_log2FC",
y = "p_val_adj",
title = "MAST with Batch Correction",
pCutoff = 0.05,
FCcutoff = 1.0)Avis : One or more p-values is 0. Converting to 10^-1 * current lowest non-zero p-value...
7.3 Volcano Plot for MAST without Batch Correction
EnhancedVolcano(markers_mast_no_batch,
lab = markers_mast_no_batch$gene,
x = "avg_log2FC",
y = "p_val_adj",
title = "MAST with Batch Correction",
pCutoff = 0.05,
FCcutoff = 1.0)Avis : One or more p-values is 0. Converting to 10^-1 * current lowest non-zero p-value...
7.4 Heatmap of Top Differentially Expressed Genes
# Load the pheatmap package
library(pheatmap)
# Define cell groups
group1_cells <- WhichCells(All_samples_Merged, idents = c("0", "1", "2", "3", "4", "5", "7", "9"))
group2_cells <- WhichCells(All_samples_Merged, idents = c("6", "8", "10", "11"))
# Define top genes based on adjusted p-value
top_genes <- head(markers_mast_batch$gene[order(markers_mast_batch$p_val_adj)], 20)
# Extract the corresponding expression data
expression_data <- GetAssayData(All_samples_Merged, slot = "data") # Log-normalized values
heatmap_data <- expression_data[top_genes, c(group1_cells, group2_cells)]
# Create annotation data
annotation_data <- data.frame(
CellLine = factor(All_samples_Merged$cell_line)[c(group1_cells, group2_cells)],
Cluster = factor(All_samples_Merged$seurat_clusters[c(group1_cells, group2_cells)])
)
# Set row names for annotation data to match column names of heatmap_data
rownames(annotation_data) <- colnames(heatmap_data)
# Generate the heatmap
pheatmap(heatmap_data,
cluster_rows = TRUE,
cluster_cols = TRUE,
show_rownames = TRUE,
show_colnames = FALSE, # Hide cell labels below the heatmap
annotation_col = annotation_data)
---
title: "Differential Expression Analysis using Harmony Integrated Clusters"
author: Nasir Mahmood Abbasi
date: "`r Sys.Date()`"
output:
  html_notebook:
    toc: true
    toc_float: true
    toc_collapsed: true
---

## 1. Load Libraries
```{r setup, include=FALSE}
library(Seurat)
library(harmony)
library(dplyr)
library(EnhancedVolcano)
library(ggplot2)
library(pheatmap)
library(tibble)
```


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

#Load Seurat Object merged from cell lines and a control(PBMC) after filtration
load("0-imp_Robj/Harmony_integrated_CD4Tcells_harmony_integrated.Robj")

All_samples_Merged 



```

# 3. Set Up Identifiers for Clustering
```{r}
# Assign cluster identities to the Seurat object
Idents(All_samples_Merged) <- "seurat_clusters"


DimPlot(All_samples_Merged, reduction = "umap.harmony", group.by = "seurat_clusters",label = T, label.box = T, repel = T) + 
  ggtitle("Harmony Integration - By Clusters")
```

# 4.  Differential Expression Analysis

## 4.1 Using MAST with Latent Variables (Batch Correction)
```{r}
markers_mast_batch <- FindMarkers(
  All_samples_Merged,
  ident.1 = c("0", "1", "2", "3", "4", "5", "7", "9"), # Cell lines
  ident.2 = c("6", "8", "10", "11"), # CD4 T cells
  assay = "SCT",
  test.use = "MAST",
  latent.vars = c("cell_line"), # Adjust for batch effects
  min.pct = 0,
  logfc.threshold = 0
)
write.csv(markers_mast_batch, file = "0-imp_Robj/1-MAST_with_batch_as_Covariate.csv", row.names = TRUE)
```

## 4.2 Using Wilcoxon Test (Without Batch Correction)
```{r}
markers_wilcox <- FindMarkers(
  All_samples_Merged,
  ident.1 = c("0", "1", "2", "3", "4", "5", "7", "9"), # Cell lines
  ident.2 = c("6", "8", "10", "11"), # CD4 T cells
  assay = "SCT",
  test.use = "wilcox",
  min.pct = 0,
  logfc.threshold = 0
)
write.csv(markers_wilcox, file = "0-imp_Robj/2-Wilcox_min.pct_logfcT-0.csv", row.names = TRUE)
```

## 4.3 Using MAST Without Latent Variables (No Batch Correction)
```{r}
markers_mast_no_batch <- FindMarkers(
  All_samples_Merged,
  ident.1 = c("0", "1", "2", "3", "4", "5", "7", "9"), # Cell lines
  ident.2 = c("6", "8", "10", "11"), # CD4 T cells
  assay = "SCT",
  test.use = "MAST",
  min.pct = 0,
  logfc.threshold = 0
)
write.csv(markers_mast_no_batch, file = "0-imp_Robj/3-MAST_without_batch_as_Covariate.csv", row.names = TRUE)
```

# 5. Compute Mean Expression for Groups
```{r}
# Define cell groups
group1_cells <- WhichCells(All_samples_Merged, idents = c("0", "1", "2", "3", "4", "5", "7", "9"))
group2_cells <- WhichCells(All_samples_Merged, idents = c("6", "8", "10", "11"))

# Extract normalized expression values
expression_data <- GetAssayData(All_samples_Merged, slot = "data")  # Log-normalized values

# Calculate mean expression for each group
calculate_mean_expression <- function(markers, group1_cells, group2_cells, expression_data) {
  group1_mean <- rowMeans(expression_data[, group1_cells])
  group2_mean <- rowMeans(expression_data[, group2_cells])
  
  markers <- markers %>%
    rownames_to_column("gene") %>%
    mutate(mean_expr_group1 = group1_mean[gene],
           mean_expr_group2 = group2_mean[gene])
  
  return(markers)
}

markers_mast_batch <- calculate_mean_expression(markers_mast_batch, group1_cells, group2_cells, expression_data)
markers_wilcox <- calculate_mean_expression(markers_wilcox, group1_cells, group2_cells, expression_data)
markers_mast_no_batch <- calculate_mean_expression(markers_mast_no_batch, group1_cells, group2_cells, expression_data)

# Save updated results with mean expression
write.csv(markers_mast_batch, file = "0-imp_Robj/1-MAST_with_batch_as_Covariate_with_meanExpression.csv", row.names = TRUE)
write.csv(markers_wilcox, file = "0-imp_Robj/2-Wilcox_min.pct_logfcT-0_with_meanExpression.csv", row.names = TRUE)
write.csv(markers_mast_no_batch, file = "0-imp_Robj/3-MAST_without_batch_as_Covariate_with_meanExpression.csv", row.names = TRUE)
```

# 6. Summarize Results

## 6.1 Summary Function
```{r}
summarize_markers <- function(markers) {
  num_pval0 <- sum(markers$p_val_adj == 0)
  num_pval1 <- sum(markers$p_val_adj == 1)
  num_significant <- sum(markers$p_val_adj < 0.05)
  num_upregulated <- sum(markers$avg_log2FC > 1)
  num_downregulated <- sum(markers$avg_log2FC < -1)
  
  cat("Number of genes with p_val_adj = 0:", num_pval0, "\n")
  cat("Number of genes with p_val_adj = 1:", num_pval1, "\n")
  cat("Number of significant genes (p_val_adj < 0.05):", num_significant, "\n")
  cat("Number of upregulated genes (avg_log2FC > 1):", num_upregulated, "\n")
  cat("Number of downregulated genes (avg_log2FC < -1):", num_downregulated, "\n")
}

```

## 6.2 Summarize Markers1 (MAST with Batch Correction)
```{r}
cat("Markers1 Summary (MAST with Batch Correction):\n")
summarize_markers(markers_mast_batch)

```

## 6.3 Summary for Markers2 (Wilcoxon Test)
```{r}
cat("Markers2 Summary (Wilcoxon Test):\n")
summarize_markers(markers_wilcox)
```

## 6.4 Summary for Markers3 (MAST without Batch Correction)
```{r}
cat("Markers3 Summary (MAST without Batch Correction):\n")
summarize_markers(markers_mast_no_batch)
```


# 7. Visualization

## 7.1 Volcano Plot for MAST with Batch Correction
```{r, fig.height=12, fig.width=12}
EnhancedVolcano(markers_mast_batch,
                lab = markers_mast_batch$gene,
                x = "avg_log2FC",
                y = "p_val_adj",
                title = "MAST with Batch Correction",
                pCutoff = 0.05,
                FCcutoff = 1.0)

```

## 7.2 Volcano Plot for Wilcoxon Test
```{r, fig.height=12, fig.width=12}
EnhancedVolcano(markers_wilcox,
                lab = markers_wilcox$gene,
                x = "avg_log2FC",
                y = "p_val_adj",
                title = "MAST with Batch Correction",
                pCutoff = 0.05,
                FCcutoff = 1.0)

```

## 7.3 Volcano Plot for MAST without Batch Correction
```{r, fig.height=12, fig.width=12}
EnhancedVolcano(markers_mast_no_batch,
                lab = markers_mast_no_batch$gene,
                x = "avg_log2FC",
                y = "p_val_adj",
                title = "MAST with Batch Correction",
                pCutoff = 0.05,
                FCcutoff = 1.0)

```

## 7.4 Heatmap of Top Differentially Expressed Genes
```{r, fig.height=6, fig.width=10}
# Load the pheatmap package
library(pheatmap)

# Define cell groups
group1_cells <- WhichCells(All_samples_Merged, idents = c("0", "1", "2", "3", "4", "5", "7", "9"))
group2_cells <- WhichCells(All_samples_Merged, idents = c("6", "8", "10", "11"))

# Define top genes based on adjusted p-value
top_genes <- head(markers_mast_batch$gene[order(markers_mast_batch$p_val_adj)], 20)

# Extract the corresponding expression data
expression_data <- GetAssayData(All_samples_Merged, slot = "data")  # Log-normalized values
heatmap_data <- expression_data[top_genes, c(group1_cells, group2_cells)]

# Create annotation data
annotation_data <- data.frame(
  CellLine = factor(All_samples_Merged$cell_line)[c(group1_cells, group2_cells)],
  Cluster = factor(All_samples_Merged$seurat_clusters[c(group1_cells, group2_cells)])
)

# Set row names for annotation data to match column names of heatmap_data
rownames(annotation_data) <- colnames(heatmap_data)

# Generate the heatmap
pheatmap(heatmap_data, 
         cluster_rows = TRUE, 
         cluster_cols = TRUE, 
         show_rownames = TRUE,
         show_colnames = FALSE, # Hide cell labels below the heatmap
         annotation_col = annotation_data)
```




