I'm showing for one gene only in this code, you can do similarly for other genes as well.

rm(list=ls())
library(Seurat)
library(SeuratData)
library(ggplot2)

seurat_obj=UpdateSeuratObject(pbmc3k.final)

# Add a new column 'condition' and assign values 'WT' and 'MT'
metadata <- data.frame(
  cell_id = colnames(seurat_obj),
  other_metadata_column = sample(c("Group1", "Group2"), ncol(seurat_obj), replace = TRUE)
)

# Assign the metadata to Seurat object
seurat_obj <- AddMetaData(object = seurat_obj, metadata = metadata)
seurat_obj@meta.data$condition <- ifelse(metadata$other_metadata_column == "Group1", "WT", "MT")

# Print the updated metadata to verify
head(seurat_obj@meta.data)

normalized_counts <- GetAssayData(seurat_obj, layer =  "data", assay = "RNA")

# Extract meta data
metadata <- seurat_obj@meta.data

# Specify the gene of interest
gene_of_interest <- "MS4A1"

# Get the normalized counts for the specific gene
gene_data <- normalized_counts[gene_of_interest, ]

# Combine with metadata
plot_data <- data.frame(
  Seurat_Ident = Idents(seurat_obj),
  Condition = metadata$condition,  # Replace 'condition' with your actual column name in metadata
  Normalized_Count = gene_data
)

# Check the data
head(plot_data)

# Plot the histogram
ggplot(plot_data, aes(x = Seurat_Ident, y = Normalized_Count, fill = Condition)) +
  geom_histogram(stat = "identity", position = "dodge") +
  labs(title = paste("Normalized counts for", gene_of_interest),
       x = "Seurat Idents",
       y = "Normalized Counts") +
  theme_minimal() +
  scale_fill_manual(values = c("WT" = "blue", "MT" = "red"))