diff --git a/src/modules/data/pbmc3k_raw.h5ad b/src/modules/data/pbmc3k_raw.h5ad
new file mode 100644
index 0000000..460ee0b
Binary files /dev/null and b/src/modules/data/pbmc3k_raw.h5ad differ
diff --git a/src/modules/preproccesing2-demo.py b/src/modules/preproccesing2-demo.py
new file mode 100644
index 0000000..deae8d8
--- /dev/null
+++ b/src/modules/preproccesing2-demo.py
@@ -0,0 +1,57 @@
+import scanpy as sc
+import numpy as np
+import pandas as pd
+from preprocessing import filter_data, calculate_qc_metrics, select_highly_variable_genes
+
+def load_file_demo(file_path):
+    adata = sc.read_h5ad(file_path)
+    adata.write("adata_demo.h5ad")
+    print("Data saved as adata_demo.h5ad")
+
+file_path = "C:/Users/pc/OneDrive/Masaüstü/bioinformatic/Hw3covid_Data_AllCells.h5ad"
+adata = load_file_demo(file_path)
+
+# Step 1: Create example data (random counts matrix)
+np.random.seed(42)  # For reproducibility
+
+n_cells = 1000  # Number of cells
+n_genes = 5000  # Number of genes
+
+# Create random gene expression data (Poisson distribution)
+counts = np.random.poisson(lam=1.0, size=(n_cells, n_genes))  # Shape: (n_cells, n_genes)
+
+# Create an AnnData object
+adata = sc.AnnData(counts)
+
+# Assign gene and cell names
+adata.var_names = [f"Gene_{i}" for i in range(n_genes)]  # Gene names: Gene_0, Gene_1, ..., Gene_4999
+adata.obs_names = [f"Cell_{i}" for i in range(n_cells)]  # Cell names: Cell_0, Cell_1, ..., Cell_999
+
+# Before adding 'MT-', note that var_names is not a list but an Index object.
+# So we need to convert var_names to a list before making changes.
+var_names_list = adata.var_names.tolist()
+
+# Add some mitochondrial genes for demonstration (every 10th gene is mitochondrial)
+for i in range(0, n_genes, 10):
+    var_names_list[i] = f"MT-{var_names_list[i]}"  # Rename every 10th gene to start with "MT-"
+
+# We assign the modified list back as an Index object
+adata.var_names = pd.Index(var_names_list)
+
+# Step 2: Apply filter_data to filter cells and genes
+adata_filtered = filter_data(adata, min_genes=200, min_cells=3)
+print(f"Filtered AnnData: {adata_filtered.shape} (cells, genes)")
+
+# Step 3: Calculate QC metrics
+adata_qc = calculate_qc_metrics(adata_filtered)
+print("\nQC Metrics (first few rows of obs):")
+print(adata_qc.obs.head())  # View the QC metrics such as 'pct_counts_mt' (percentage of mitochondrial genes)
+
+# Step 4: Select highly variable genes
+adata_hvg = select_highly_variable_genes(adata_qc, min_mean=0.0125, max_mean=3, min_disp=0.5)
+print("\nHighly Variable Genes (first few rows of 'highly_variable' column):")
+print(adata_hvg.var[['highly_variable']].head())  # Display which genes are highly variable
+
+# Step 5: Count how many genes are highly variable
+num_highly_variable_genes = adata_hvg.var['highly_variable'].sum()
+print(f"\nNumber of highly variable genes selected: {num_highly_variable_genes}")
diff --git a/src/modules/preprocessing.py b/src/modules/preprocessing.py
index e69de29..f62fc37 100644
--- a/src/modules/preprocessing.py
+++ b/src/modules/preprocessing.py
@@ -0,0 +1,77 @@
+import scanpy as sc
+import pandas as pd
+from anndata import AnnData
+
+def filter_data(
+        adata: AnnData, 
+        min_genes: int = 200, 
+        min_cells: int = 3
+) -> AnnData:
+    """
+    Filters the dataset.
+
+    Args:
+        adata (AnnData): The single-cell dataset.
+        min_genes (int): Minimum number of genes expressed per cell. Default is 200.
+        min_cells (int): Minimum number of cells expressing a gene. Default is 3.
+
+    Returns:
+        AnnData: Filtered dataset.
+    """
+    sc.pp.filter_cells(adata, min_genes=min_genes)
+    sc.pp.filter_genes(adata, min_cells=min_cells)
+    return adata
+
+def calculate_qc_metrics(adata: AnnData) -> AnnData:
+    """
+    Computes quality control (QC) metrics and adds them to the dataset.
+    Checks for mitochondrial genes with both uppercase and lowercase 'mt-' prefix.
+
+    Args:
+        adata (AnnData): The filtered dataset.
+
+    Returns:
+        AnnData: Dataset with QC metrics added.
+    """
+    if adata.var_names.str.startswith("MT-").any():
+        adata.var["mt"] = adata.var_names.str.startswith("MT-")
+    elif adata.var_names.str.startswith("mt-").any():
+        adata.var["mt"] = adata.var_names.str.startswith("mt-")
+    else:
+        # In case neither is found, check in a case-insensitive way just to be sure
+        adata.var["mt"] = adata.var_names.str.upper().str.startswith("MT-")
+
+    sc.pp.calculate_qc_metrics(
+        adata, 
+        qc_vars=["mt"], 
+        percent_top=None, 
+        log1p=False, 
+        inplace=True
+    )
+    return adata
+
+def select_highly_variable_genes(
+        adata: AnnData, 
+        min_mean: float = 0.0125, 
+        max_mean: int = 3, 
+        min_disp: float = 0.5
+    ) -> AnnData:
+    """
+    Identifies highly variable genes in the dataset.
+
+    Args:
+        adata (AnnData): The dataset with QC metrics.
+        min_mean (float): Minimum mean expression threshold. Default is 0.0125.
+        max_mean (float): Maximum mean expression threshold. Default is 3.
+        min_disp (float): Minimum dispersion threshold. Default is 0.5.
+
+    Returns:
+        AnnData: Dataset with highly variable gene information.
+    """
+    sc.pp.highly_variable_genes(
+        adata, 
+        min_mean=min_mean, 
+        max_mean=max_mean, 
+        min_disp=min_disp
+    )
+    return adata