PBMC_10x

1. load libraries

2. Reading Data from 10x & Creating Seurat Object

# Reading 10x data and creating Seurat object
sc.data <- Read10X(data.dir = "filtered_feature_bc_matrix/")

# Renaming barcodes to prevent conflicts if merging later
colnames(sc.data) <- paste('PBMC_10x', colnames(sc.data), sep = '_')

# Creating the Seurat object from the RNA counts
SS_PBMC_10x <- CreateSeuratObject(counts = sc.data)

# Preview the object
SS_PBMC_10x

An object of class Seurat 
36601 features across 10636 samples within 1 assay 
Active assay: RNA (36601 features, 0 variable features)
 1 layer present: counts

3. Quality Control (QC)

# Calculating the percentage of mitochondrial gene expression
SS_PBMC_10x[["percent.mt"]] <- PercentageFeatureSet(SS_PBMC_10x, pattern = "^MT-")

# Ensure 'percent.mt' is numeric and handle NaNs
SS_PBMC_10x$percent.mt <- replace(as.numeric(SS_PBMC_10x$percent.mt), is.na(SS_PBMC_10x$percent.mt), 0)

# Violin plot to visualize QC metrics
VlnPlot(SS_PBMC_10x, features = c("nFeature_RNA","nCount_RNA", "percent.mt"), ncol = 3)

Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.

# Scatter plots to visualize relationships between key metrics
plot1 <-FeatureScatter(SS_PBMC_10x, feature1 = "nCount_RNA", feature2 = "nFeature_RNA") 
plot2 <-FeatureScatter(SS_PBMC_10x, feature1 = "nCount_RNA", feature2 = "percent.mt") 
plot3 <-FeatureScatter(SS_PBMC_10x, feature1 = "nFeature_RNA", feature2 = "percent.mt") 

plot1

plot2

plot3

# Display the scatter plots
plot1 + plot2 + plot3

4. Filtering

SS_PBMC_10x <- subset(SS_PBMC_10x, subset = nFeature_RNA > 500 & nFeature_RNA < 7000 & nCount_RNA < 40000 & percent.mt < 7.5 
                  & percent.mt > 0.1)

# Visualize QC metrics as a violin plot
VlnPlot(SS_PBMC_10x, features = c("nFeature_RNA","nCount_RNA", "percent.mt"),  ncol = 3)

Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.

plot4 <-FeatureScatter(SS_PBMC_10x, feature1 = "nCount_RNA", feature2 = "nFeature_RNA") 
plot5 <-FeatureScatter(SS_PBMC_10x, feature1 = "nCount_RNA", feature2 = "percent.mt") 
plot6 <-FeatureScatter(SS_PBMC_10x, feature1 = "nFeature_RNA", feature2 = "percent.mt") 

plot4

plot5

plot6

SS_PBMC_10x

An object of class Seurat 
36601 features across 10162 samples within 1 assay 
Active assay: RNA (36601 features, 0 variable features)
 1 layer present: counts

5. Adding Metadata

6. Save the Seurat object as an Robj file

```r

save(SS_PBMC_10x, file = \PBMC_10x.Robj\)

```