diff --git a/.gitignore b/.gitignore
index 0a19790..ff67cbc 100644
--- a/.gitignore
+++ b/.gitignore
@@ -172,3 +172,4 @@ cython_debug/
 
 # PyPI configuration file
 .pypirc
+oturum.txt
diff --git a/README.md b/README.md
index b393bbe..6bb402c 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,15 @@
 # SingleCellWebApp
 Bioinformatic technique team project  
+Here are the outputs we got from running our codes. 
+
+![PHOTO-2025-05-23-22-18-02](https://github.com/user-attachments/assets/052b7fae-295a-41b5-8f4c-9efb99e6365b)
+Above is the output from rank genes function
+
+![PHOTO-2025-05-23-22-32-16](https://github.com/user-attachments/assets/f7fe0897-e9b4-4412-be4a-c63479aa43b9)
+This one is the violin plot output.
+
+![PHOTO-2025-05-23-22-52-52](https://github.com/user-attachments/assets/62fcd331-dd72-4e4a-930a-e8698a6f8d01)
+This one is from marker genes code.
+
+![PHOTO-2025-05-23-21-32-06](https://github.com/user-attachments/assets/30361fa4-fdc4-43cf-83a1-15c57454cbc1)
+Lastly, this is the output we got from the get scores p-values function.
diff --git a/SingleCellWebApp b/SingleCellWebApp
new file mode 160000
index 0000000..a9ae786
--- /dev/null
+++ b/SingleCellWebApp
@@ -0,0 +1 @@
+Subproject commit a9ae7869927e2230f9e97f2990892d72ad2bb246
diff --git a/app_final_demo.py b/app_final_demo.py
new file mode 100644
index 0000000..8b2a762
--- /dev/null
+++ b/app_final_demo.py
@@ -0,0 +1,539 @@
+from shiny import App, ui, reactive, render
+from sqlalchemy import create_engine, Column, Integer, String
+from sqlalchemy.orm import sessionmaker, declarative_base
+import bcrypt
+import jwt
+import datetime
+
+# Database Configuration
+# IMPORTANT: Ensure your PostgreSQL server is running and the 'postgres' user
+# with password '1234' has access to the 'AZRA_SCA_DEMO' database.
+DATABASE_URL = "postgresql://postgres:1234@localhost:5432/AZRA_SCA_DEMO"
+engine = create_engine(DATABASE_URL)
+SessionLocal = sessionmaker(bind=engine)
+Base = declarative_base()
+
+# Define the User model for SQLAlchemy
+class User(Base):
+    __tablename__ = "users"
+    id = Column(Integer, primary_key=True, index=True)
+    username = Column(String, unique=True, index=True, nullable=False)
+    email = Column(String, unique=True, nullable=False)
+    password_hash = Column(String, nullable=False)
+
+# Create database tables if they don't exist
+Base.metadata.create_all(bind=engine)
+
+# JWT Secret Key
+# IMPORTANT: In a real application, use a strong, randomly generated
+# key stored securely (e.g., in environment variables), not hardcoded.
+SECRET_KEY = "supersecretkey123"
+
+# Function to create a JWT token
+def create_jwt_token(username):
+    # Payload for the JWT token
+    payload = {
+        "user": username,
+        "exp": datetime.datetime.utcnow() + datetime.timedelta(hours=1), # Token expires in 1 hour
+    }
+    # Encode the payload with the secret key and HS256 algorithm
+    return jwt.encode(payload, SECRET_KEY, algorithm="HS256")
+
+# Define the User Interface (UI) for the Shiny app
+app_ui = ui.page_fluid(
+    # Custom CSS styling for the app's appearance
+    ui.tags.style("""
+        /* General body styling for a softer background and font */
+        body {
+            background-color: #eef2f7; /* Light blue-grey background */
+            font-family: 'Inter', 'Segoe UI', Roboto, Helvetica, Arial, sans-serif;
+            color: #333;
+            line-height: 1.6;
+            margin: 0;
+            padding: 0;
+            display: flex;
+            justify-content: center;
+            align-items: center;
+            min-height: 100vh; /* Full viewport height */
+        }
+        /* Container styling for a modern card-like look */
+        .container {
+            max-width: 450px; /* Slightly wider for better spacing */
+            width: 90%; /* Responsive width */
+            padding: 30px 35px;
+            border-radius: 12px; /* More rounded corners */
+            background-color: #ffffff; /* Pure white background */
+            box-shadow: 0 8px 25px rgba(0,0,0,0.12); /* Stronger, softer shadow */
+            box-sizing: border-box; /* Include padding in element's total width and height */
+        }
+        /* Title styling */
+        h2 {
+            text-align: center;
+            color: #2c3e50; /* Darker title color */
+            margin-bottom: 30px;
+            font-size: 2.2rem; /* Larger title */
+            font-weight: 700; /* Bolder font */
+            font-family: 'Lucida Console', 'Consolas', monospace; /* Lucida Console font for bold text */
+        }
+        /* Form headings styling */
+        h3 {
+            color: #34495e;
+            margin-bottom: 25px; /* More space below heading */
+            text-align: center;
+            font-size: 1.6rem; /* Larger form headings */
+            font-weight: 600;
+            font-family: 'Lucida Console', 'Consolas', monospace; /* Lucida Console font for bold text */
+        }
+        /* Input field styling */
+        input[type="text"], input[type="password"], input[type="email"] {
+            width: 100%;
+            padding: 12px 15px; /* More padding */
+            margin-bottom: 20px; /* More space between inputs */
+            border: 1px solid #dcdcdc; /* Lighter border */
+            border-radius: 8px; /* More rounded input fields */
+            font-size: 1.05rem; /* Slightly larger text in inputs */
+            box-sizing: border-box;
+            transition: border-color 0.3s ease, box-shadow 0.3s ease;
+        }
+        /* Input focus effect */
+        input[type="text"]:focus, input[type="password"]:focus, input[type="email"]:focus {
+            border-color: #f7a6b5; /* Brighter pastel pink on focus */
+            outline: none;
+            box-shadow: 0 0 0 3px rgba(247, 166, 181, 0.5); /* Glow effect with pastel pink */
+        }
+        /* Button styling */
+        button, .btn {
+            background-color: #f7baba; /* Pastel pink color */
+            color: white;
+            padding: 12px 0; /* More vertical padding */
+            width: 100%;
+            font-size: 1.1rem; /* Larger font for buttons */
+            font-weight: 600; /* Bolder button text */
+            border: none;
+            border-radius: 8px; /* More rounded buttons */
+            cursor: pointer;
+            transition: background-color 0.3s ease, transform 0.2s ease;
+            letter-spacing: 0.5px; /* Slightly spaced letters */
+        }
+        /* Button hover effect */
+        button:hover, .btn:hover {
+            background-color: #f7a6b5; /* Darker pastel pink on hover */
+            transform: translateY(-2px); /* Slight lift effect */
+        }
+        button:active, .btn:active {
+            transform: translateY(0); /* Press down effect */
+        }
+        /* Link styling for switch between forms */
+        a.form-link {
+            display: block;
+            text-align: center;
+            margin-top: 20px;
+            color: #f7baba; /* Pastel pink for links */
+            cursor: pointer;
+            text-decoration: none;
+            font-size: 0.95rem;
+            transition: color 0.3s ease;
+        }
+        /* Link hover effect */
+        a.form-link:hover {
+            text-decoration: underline;
+            color: #f7a6b5;
+        }
+        /* Message area styling (for error/success messages) */
+        #message_text {
+            text-align: center;
+            font-weight: 600;
+            margin-top: 15px; /* Adjusted margin */
+            margin-bottom: 25px; /* Adjusted margin */
+            color: #dc3545; /* Red for error messages */
+            font-size: 0.95rem;
+        }
+        /* Styling for success messages */
+        .message-success {
+            color: #28a745 !important; /* Green for success */
+        }
+        /* Horizontal rule styling */
+        hr {
+            border: 0;
+            height: 1px;
+            background: #eee; /* Lighter divider */
+            margin: 30px 0;
+        }
+        /* Protected content styling */
+        #protected_content {
+            background-color: #f8f9fa;
+            border: 1px solid #e9ecef;
+            border-radius: 8px;
+            padding: 20px;
+            margin-top: 25px;
+            text-align: center;
+            color: #495057;
+        }
+        #protected_content h4 {
+            color: #f7baba; /* Pastel pink for protected content heading */
+            margin-bottom: 15px;
+            font-size: 1.3rem;
+            font-family: 'Lucida Console', 'Consolas', monospace; /* Lucida Console font for bold text */
+        }
+        /* JWT Token display styling */
+        .token-display {
+            white-space: pre-wrap;
+            word-wrap: break-word;
+            max-height: 150px;
+            overflow-y: auto;
+            background-color: #e9ecef; /* Slightly darker background for token */
+            padding: 15px;
+            border: 1px solid #ced4da;
+            border-radius: 8px;
+            font-family: 'Fira Code', 'Cascadia Code', monospace; /* Modern monospace font */
+            font-size: 0.85rem;
+            margin-top: 15px;
+            color: #495057;
+        }
+    """),
+    # Main application div container
+    ui.div(
+        ui.h2("SCA-Web"), # Application title
+        ui.output_ui("message_text"), # Displays messages to the user
+        ui.output_ui("main_ui"), # UI for login/register/welcome screen
+        ui.hr(), # Horizontal rule
+        ui.output_ui("protected_content"), # Content visible only when logged in
+        class_="container" # Apply the CSS container class
+    ),
+    # Add JavaScript for "Enter to continue" functionality on form submissions
+    ui.tags.script("""
+        $(document).on('keypress', function(e) {
+            if(e.which == 13) { // Enter key
+                if ($('#main_ui').find('#btn_login').is(':visible')) {
+                    $('#btn_login').click();
+                } else if ($('#main_ui').find('#btn_register').is(':visible')) {
+                    $('#btn_register').click();
+                } else if ($('#main_ui').find('#btn_reset_password_initiate').is(':visible')) {
+                    $('#btn_reset_password_initiate').click();
+                } else if ($('#main_ui').find('#btn_reset_password_final').is(':visible')) {
+                    $('#btn_reset_password_final').click();
+                }
+            }
+        });
+    """)
+)
+
+# Define the Server logic for the Shiny app
+def server(input, output, session):
+    # Reactive values to manage application state
+    logged_in = reactive.Value(False)
+    current_user = reactive.Value(None)
+    jwt_token = reactive.Value(None)
+    message = reactive.Value("")
+    message_type = reactive.Value("error")
+    # Default page state is "register"
+    page_state = reactive.Value("register") # Can be "register", "login", "forgot_password_initiate", "forgot_password_reset"
+    show_token = reactive.Value(False)
+    
+    # Store temporary user info for password reset flow
+    reset_username = reactive.Value(None)
+    reset_email = reactive.Value(None)
+
+    # Render reactive message text to the UI with dynamic styling
+    @output
+    @render.ui
+    def message_text():
+        if message_type() == "success":
+            return ui.tags.div(message(), class_="message-success")
+        return ui.tags.div(message())
+
+    # Dynamically render the main UI based on login state and page state
+    @output
+    @render.ui
+    def main_ui():
+        if logged_in():
+            return ui.div(
+                ui.h3(f"Welcome, {current_user()}!"),
+                ui.p("You are successfully logged in and can now access protected content."),
+                ui.input_action_button("btn_toggle_token", "Toggle JWT Token Display", class_="btn"),
+                ui.output_ui("token_text"),
+                ui.input_action_button("btn_logout", "Log out", class_="btn"),
+            )
+        elif page_state() == "register":
+            return ui.div(
+                ui.h3("Register New Account"),
+                ui.input_text("reg_username", "Username", placeholder="Enter your desired username"),
+                ui.input_text("reg_email", "Email", placeholder="Enter your email address"),
+                ui.input_password("reg_password", "Password", placeholder="Create a strong password"),
+                ui.input_action_button("btn_register", "Register", class_="btn"),
+                ui.a(
+                    "Already have an account? Log in here.",
+                    href="#",
+                    onclick="Shiny.setInputValue('go_to_login', Math.random())",
+                    class_="form-link"
+                ),
+            )
+        elif page_state() == "login":
+            return ui.div(
+                ui.h3("Log In to Your Account"),
+                ui.input_text("login_username", "Username", placeholder="Enter your username"),
+                ui.input_password("login_password", "Password", placeholder="Enter your password"),
+                ui.input_action_button("btn_login", "Log In", class_="btn"),
+                ui.a(
+                    "Don't have an account? Register here.",
+                    href="#",
+                    onclick="Shiny.setInputValue('go_to_register', Math.random())",
+                    class_="form-link"
+                ),
+                ui.a(
+                    "Forgot Password?",
+                    href="#",
+                    onclick="Shiny.setInputValue('go_to_forgot_password_initiate', Math.random())",
+                    class_="form-link"
+                )
+            )
+        elif page_state() == "forgot_password_initiate":
+            return ui.div(
+                ui.h3("Reset Your Password"),
+                ui.p("Please enter your username and email to proceed."),
+                ui.input_text("reset_username_input", "Username", placeholder="Enter your username"),
+                ui.input_text("reset_email_input", "Email", placeholder="Enter your email address"),
+                ui.input_action_button("btn_reset_password_initiate", "Continue", class_="btn"),
+                ui.a(
+                    "Back to Login",
+                    href="#",
+                    onclick="Shiny.setInputValue('go_to_login', Math.random())",
+                    class_="form-link"
+                )
+            )
+        elif page_state() == "forgot_password_reset":
+            return ui.div(
+                ui.h3("Set New Password"),
+                ui.p(f"Setting new password for {reset_username()}."),
+                ui.input_password("new_password", "New Password", placeholder="Enter your new strong password"),
+                ui.input_password("confirm_new_password", "Confirm New Password", placeholder="Confirm your new password"),
+                ui.input_action_button("btn_reset_password_final", "Reset Password", class_="btn"),
+                ui.a(
+                    "Back to Login",
+                    href="#",
+                    onclick="Shiny.setInputValue('go_to_login', Math.random())",
+                    class_="form-link"
+                )
+            )
+
+    # Render protected content based on JWT token presence
+    @output
+    @render.ui
+    def protected_content():
+        if jwt_token():
+            return ui.div(
+                ui.h4("🔒 Protected Application Dashboard"),
+                ui.p("Welcome to your secure dashboard! This area is only accessible after successful authentication."),
+                ui.p("You can integrate your secure analytics, personalized reports, or confidential data visualizations here."),
+                ui.tags.ul(
+                    ui.tags.li("View real-time data analytics."),
+                    ui.tags.li("Manage user settings."),
+                    ui.tags.li("Access exclusive features."),
+                ),
+            )
+        else:
+            return ui.div(
+                ui.h4("🚫 Access Restricted"),
+                ui.p("Please log in to view the protected content and features."),
+                ui.p("Register if you don't have an account yet.")
+            )
+
+    # Render the JWT token text with styling for readability
+    @output
+    @render.ui
+    def token_text():
+        if show_token() and jwt_token():
+           return ui.div(
+               jwt_token(),
+               class_="token-display"
+           )
+        return None
+
+    # Reactive effect to toggle the visibility of the JWT token
+    @reactive.Effect
+    @reactive.event(input.btn_toggle_token)
+    def toggle_token():
+        show_token.set(not show_token())
+
+    # Reactive effect to switch to the login page
+    @reactive.Effect
+    @reactive.event(input.go_to_login)
+    def go_to_login_event():
+        message.set("")
+        message_type.set("error")
+        page_state.set("login")
+
+    # Reactive effect to switch to the register page
+    @reactive.Effect
+    @reactive.event(input.go_to_register)
+    def go_to_register_event():
+        message.set("")
+        message_type.set("error")
+        page_state.set("register")
+
+    # Reactive effect to switch to the forgot password initiation page
+    @reactive.Effect
+    @reactive.event(input.go_to_forgot_password_initiate)
+    def go_to_forgot_password_initiate_event():
+        message.set("")
+        message_type.set("error")
+        page_state.set("forgot_password_initiate")
+
+    # Reactive effect to handle user registration
+    @reactive.Effect
+    @reactive.event(input.btn_register)
+    def register():
+        username = input.reg_username()
+        email = input.reg_email()
+        password = input.reg_password()
+
+        if not username or not email or not password:
+            message_type.set("error")
+            message.set("Please fill in all registration fields.")
+            return
+
+        db = SessionLocal()
+        try:
+            if db.query(User).filter((User.username == username) | (User.email == email)).first():
+                message_type.set("error")
+                message.set("Username or Email already registered. Please log in or use different credentials.")
+                page_state.set("login")
+                return
+
+            hashed = bcrypt.hashpw(password.encode(), bcrypt.gensalt()).decode()
+            new_user = User(username=username, email=email, password_hash=hashed)
+            db.add(new_user)
+            db.commit()
+            message_type.set("success")
+            message.set("Registration successful! You can now log in.")
+            page_state.set("login")
+        except Exception as e:
+            db.rollback()
+            message_type.set("error")
+            message.set(f"An error occurred during registration: {e}")
+        finally:
+            db.close()
+
+    # Reactive effect to handle user login
+    @reactive.Effect
+    @reactive.event(input.btn_login)
+    def login():
+        username = input.login_username()
+        password = input.login_password()
+
+        if not username or not password:
+            message_type.set("error")
+            message.set("Please enter both username and password.")
+            return
+
+        db = SessionLocal()
+        user = db.query(User).filter(User.username == username).first()
+        db.close()
+
+        if not user:
+            message_type.set("error")
+            message.set("User not found or incorrect username.")
+            return
+
+        if bcrypt.checkpw(password.encode(), user.password_hash.encode()):
+            logged_in.set(True)
+            current_user.set(username)
+            token = create_jwt_token(username)
+            jwt_token.set(token)
+            message_type.set("success")
+            message.set("Login successful! Welcome.")
+        else:
+            message_type.set("error")
+            message.set("Incorrect password.")
+
+    # Reactive effect to handle initiation of password reset
+    @reactive.Effect
+    @reactive.event(input.btn_reset_password_initiate)
+    def reset_password_initiate():
+        username = input.reset_username_input()
+        email = input.reset_email_input()
+
+        if not username or not email:
+            message_type.set("error")
+            message.set("Please provide both username and email.")
+            return
+
+        db = SessionLocal()
+        user = db.query(User).filter(User.username == username, User.email == email).first()
+        db.close()
+
+        if user:
+            reset_username.set(username)
+            reset_email.set(email)
+            message_type.set("success")
+            message.set("Username and email verified. Please set your new password.")
+            page_state.set("forgot_password_reset")
+        else:
+            message_type.set("error")
+            message.set("Username or email is incorrect. Please try again.")
+
+    # Reactive effect to handle final password reset
+    @reactive.Effect
+    @reactive.event(input.btn_reset_password_final)
+    def reset_password_final():
+        new_password = input.new_password()
+        confirm_new_password = input.confirm_new_password()
+
+        if not new_password or not confirm_new_password:
+            message_type.set("error")
+            message.set("Please enter and confirm your new password.")
+            return
+        
+        if new_password != confirm_new_password:
+            message_type.set("error")
+            message.set("Passwords do not match. Please try again.")
+            return
+
+        if not reset_username() or not reset_email():
+            message_type.set("error")
+            message.set("Error: User information not found for password reset. Please start again.")
+            page_state.set("forgot_password_initiate")
+            return
+
+        db = SessionLocal()
+        try:
+            user = db.query(User).filter(User.username == reset_username(), User.email == reset_email()).first()
+            if user:
+                hashed = bcrypt.hashpw(new_password.encode(), bcrypt.gensalt()).decode()
+                user.password_hash = hashed
+                db.commit()
+                message_type.set("success")
+                message.set("Your password has been successfully reset. You can now log in with your new password.")
+                page_state.set("login")
+                # Clear reset state
+                reset_username.set(None) 
+                reset_email.set(None)
+            else:
+                message_type.set("error")
+                message.set("User not found during password reset. Please try again.")
+                page_state.set("forgot_password_initiate")
+        except Exception as e:
+            db.rollback()
+            message_type.set("error")
+            message.set(f"An error occurred during password reset: {e}")
+        finally:
+            db.close()
+
+    # Reactive effect to handle user logout
+    @reactive.Effect
+    @reactive.event(input.btn_logout)
+    def logout():
+        logged_in.set(False)
+        current_user.set(None)
+        jwt_token.set(None)
+        show_token.set(False)
+        page_state.set("login")
+        message_type.set("success")
+        message.set("You have been successfully logged out.")
+
+# Create the Shiny App instance
+app = App(app_ui, server)
+
+# This line starts the Shiny web server. 
+if __name__ == "__main__":
+    app.run()
diff --git a/data/pbmc3k_raw.h5ad b/data/pbmc3k_raw.h5ad
new file mode 100644
index 0000000..460ee0b
Binary files /dev/null and b/data/pbmc3k_raw.h5ad differ
diff --git a/pcaprocess/demo_pcanlysis.py b/pcaprocess/demo_pcanlysis.py
new file mode 100644
index 0000000..10a0607
--- /dev/null
+++ b/pcaprocess/demo_pcanlysis.py
@@ -0,0 +1,93 @@
+import scanpy as sc
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Define the PCA functions in the same file or import them correctly
+# from pca_functions import run_pca, plot_pca, plot_variance, save_results, get_adata
+
+# Step 1: Create random data
+n_cells, n_genes = 100, 2000
+np.random.seed(42)
+
+# Generate random gene expression data
+data = np.random.rand(n_cells, n_genes)
+
+# Create an AnnData object
+adata = sc.AnnData(X=data)
+
+# Adding cell types (obs)
+adata.obs['cell_type'] = ['type1' if i < 50 else 'type2' for i in range(n_cells)]
+adata.var['gene_id'] = [f"gene{i}" for i in range(n_genes)]
+
+# Step 2: Apply PCA
+def run_pca(adata, n_comps=50, svd_solver='arpack'):
+    """Runs PCA and stores the computed components."""
+    try:
+        if 'X_pca' in adata.obsm:
+            print("PCA already computed. Overwriting previous results...")
+        
+        print(f"Running PCA with {n_comps} components using {svd_solver} solver...")
+        sc.pp.normalize_total(adata, target_sum=1e4)  # Normalization
+        sc.pp.log1p(adata)  # Log transformation
+        sc.pp.scale(adata)  # Scaling
+        sc.tl.pca(adata, n_comps=n_comps, svd_solver=svd_solver)
+        
+        print("PCA completed.")
+    except Exception as e:
+        print(f"Error during PCA: {e}")
+        raise  
+
+run_pca(adata, n_comps=10, svd_solver='arpack')
+
+# Step 3: Plot PCA graph
+def plot_pca(adata, color=None):
+    """Plots PCA results, colored by a specified attribute (if provided)."""
+    try:
+        print(f"Plotting PCA, color by: {color or 'default'}")
+        sc.pl.pca(adata, color=color, show=False)
+        plt.title(f"PCA - Colored by {color if color else 'default'}")
+        plt.show()
+    except KeyError:
+        print(f"Warning: '{color}' not found. Using default coloring.")
+        sc.pl.pca(adata, show=False)
+        plt.title("PCA - Default Coloring")
+        plt.show()
+    except Exception as e:
+        print(f"Error in PCA plot: {e}")
+        raise  
+
+plot_pca(adata, color='cell_type')
+
+# Step 4: Plot explained variance
+def plot_variance(adata, log=True):
+    """Plots the variance explained by PCA components."""
+    try:
+        print("Plotting explained variance...")
+        sc.pl.pca_variance_ratio(adata, log=log, show=False)
+        plt.title("PCA: Explained Variance")
+        plt.show()
+    except Exception as e:
+        print(f"Error in variance plot: {e}")
+        raise  
+
+plot_variance(adata)
+
+# Step 5: Save PCA results
+def save_results(adata, results_file="pca_results.h5ad"):
+    """Saves the PCA results to an H5AD file."""
+    try:
+        print(f"Saving results to {results_file}...")
+        adata.write(results_file)
+        print("Save successful.")
+    except Exception as e:
+        print(f"Error saving results: {e}")
+        raise  
+
+save_results(adata, "pca_results.h5ad")
+
+# Step 6: Retrieve processed AnnData object
+def get_adata(adata):
+    """Returns the processed AnnData object."""
+    return adata
+
+processed_adata = get_adata(adata)
diff --git a/rank_genes_violin_demo.py b/rank_genes_violin_demo.py
new file mode 100644
index 0000000..879a1f0
--- /dev/null
+++ b/rank_genes_violin_demo.py
@@ -0,0 +1,17 @@
+from rank_genes_violin import get_rank_genes_groups_violin
+import scanpy as sc
+
+#sample dataset
+adata = sc.datasets.pbmc3k()
+
+# calculating neighborhoods
+sc.pp.neighbors(adata)
+
+sc.tl.leiden(adata, resolution=1.0)
+
+# Differential expression analysis (with t-test)
+sc.tl.rank_genes_groups(adata, groupby='leiden', method='t-test')
+
+
+# calling the function and visualizing
+get_rank_genes_groups_violin(adata, groups='0', n_genes=8)
diff --git a/src/demo_pcanlysis.py b/src/demo_pcanlysis.py
new file mode 100644
index 0000000..10a0607
--- /dev/null
+++ b/src/demo_pcanlysis.py
@@ -0,0 +1,93 @@
+import scanpy as sc
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Define the PCA functions in the same file or import them correctly
+# from pca_functions import run_pca, plot_pca, plot_variance, save_results, get_adata
+
+# Step 1: Create random data
+n_cells, n_genes = 100, 2000
+np.random.seed(42)
+
+# Generate random gene expression data
+data = np.random.rand(n_cells, n_genes)
+
+# Create an AnnData object
+adata = sc.AnnData(X=data)
+
+# Adding cell types (obs)
+adata.obs['cell_type'] = ['type1' if i < 50 else 'type2' for i in range(n_cells)]
+adata.var['gene_id'] = [f"gene{i}" for i in range(n_genes)]
+
+# Step 2: Apply PCA
+def run_pca(adata, n_comps=50, svd_solver='arpack'):
+    """Runs PCA and stores the computed components."""
+    try:
+        if 'X_pca' in adata.obsm:
+            print("PCA already computed. Overwriting previous results...")
+        
+        print(f"Running PCA with {n_comps} components using {svd_solver} solver...")
+        sc.pp.normalize_total(adata, target_sum=1e4)  # Normalization
+        sc.pp.log1p(adata)  # Log transformation
+        sc.pp.scale(adata)  # Scaling
+        sc.tl.pca(adata, n_comps=n_comps, svd_solver=svd_solver)
+        
+        print("PCA completed.")
+    except Exception as e:
+        print(f"Error during PCA: {e}")
+        raise  
+
+run_pca(adata, n_comps=10, svd_solver='arpack')
+
+# Step 3: Plot PCA graph
+def plot_pca(adata, color=None):
+    """Plots PCA results, colored by a specified attribute (if provided)."""
+    try:
+        print(f"Plotting PCA, color by: {color or 'default'}")
+        sc.pl.pca(adata, color=color, show=False)
+        plt.title(f"PCA - Colored by {color if color else 'default'}")
+        plt.show()
+    except KeyError:
+        print(f"Warning: '{color}' not found. Using default coloring.")
+        sc.pl.pca(adata, show=False)
+        plt.title("PCA - Default Coloring")
+        plt.show()
+    except Exception as e:
+        print(f"Error in PCA plot: {e}")
+        raise  
+
+plot_pca(adata, color='cell_type')
+
+# Step 4: Plot explained variance
+def plot_variance(adata, log=True):
+    """Plots the variance explained by PCA components."""
+    try:
+        print("Plotting explained variance...")
+        sc.pl.pca_variance_ratio(adata, log=log, show=False)
+        plt.title("PCA: Explained Variance")
+        plt.show()
+    except Exception as e:
+        print(f"Error in variance plot: {e}")
+        raise  
+
+plot_variance(adata)
+
+# Step 5: Save PCA results
+def save_results(adata, results_file="pca_results.h5ad"):
+    """Saves the PCA results to an H5AD file."""
+    try:
+        print(f"Saving results to {results_file}...")
+        adata.write(results_file)
+        print("Save successful.")
+    except Exception as e:
+        print(f"Error saving results: {e}")
+        raise  
+
+save_results(adata, "pca_results.h5ad")
+
+# Step 6: Retrieve processed AnnData object
+def get_adata(adata):
+    """Returns the processed AnnData object."""
+    return adata
+
+processed_adata = get_adata(adata)
diff --git a/src/modules/Neighborhood.py b/src/modules/Neighborhood.py
new file mode 100644
index 0000000..7e25636
--- /dev/null
+++ b/src/modules/Neighborhood.py
@@ -0,0 +1,64 @@
+import shiny  #Only needed if you are planning to use it later
+import scanpy as sc
+import umap
+
+adata = sc.read("C:/Users/pc/.ipython/HW3/Hw3covid_Data_AllCells.h5ad")
+
+#computing
+def compute(adata, n_neighbors=10, n_pcs=40):
+    """_summary_
+
+    Args:
+        Hw3covid_Data_AllCells (_type_): _description_
+        n_neighbors (int, optional): _description_. Defaults to 10.
+        n_pcs (int, optional): _description_. Defaults to 40.
+    """
+    try:
+        sc.pp.neighbors(adata, n_neighbors=n_neighbors, n_pcs=n_pcs)
+        print("Neighbors computed successfully.")
+    except Exception as e:
+        print(f"Error in computing neighbors: {e}")
+
+#embedding
+def embed(adata, color=['CST3', 'NKG7', 'PPBP']):
+    """_summary_
+
+    Args:
+        adata (_type_): _description_
+        color (list, optional): _description_. Defaults to ['CST3', 'NKG7', 'PPBP'].
+    """
+    try:
+        sc.pl.umap(adata, color=color)
+        sc.pl.umap(adata, color=color)  # Note: This plots twice; check if you really want this
+        print("Embedding and plotting successful.")
+    except Exception as e:
+        print(f"Error in embedding: {e}")
+
+#clustering
+def cluster(adata, color=['leiden', 'CST3', 'NKG7']):
+    """_summary_
+
+    Args:
+        adata (_type_): _description_
+        color (list, optional): _description_. Defaults to ['leiden', 'CST3', 'NKG7'].
+    """
+    try:
+        sc.tl.leiden(adata)
+        sc.pl.umap(adata, color=color)
+        print("Clustering and plotting successful.")
+    except Exception as e:
+        print(f"Error in clustering: {e}")
+
+#saving
+def save(results_file, adata):
+    """_summary_
+
+    Args:
+        results_file (_type_): _description_
+        adata (_type_): _description_
+    """
+    try:
+        adata.write(results_file)
+        print(f"Data saved successfully to {results_file}.")
+    except Exception as e:
+        print(f"Error in saving data: {e}")
diff --git a/src/modules/Neighborhood_demo.py b/src/modules/Neighborhood_demo.py
new file mode 100644
index 0000000..28ff284
--- /dev/null
+++ b/src/modules/Neighborhood_demo.py
@@ -0,0 +1,30 @@
+import scanpy as sc
+import Neighborhood as nb  # Your functions are here!
+import umap
+
+#Load the dataset
+print("Loading dataset...")
+adata = sc.datasets.pbmc3k()
+print(f"Dataset loaded! Number of cells: {adata.n_obs}, Number of genes: {adata.n_vars}\n")
+
+#Compute the neighbor graph
+print("Computing the neighborhood graph...")
+nb.compute(adata, n_neighbors=10, n_pcs=40)
+
+#Compute UMAP
+print("Computing UMAP embedding...")
+sc.tl.umap(adata)
+
+#Visualize the embedding
+print("Plotting UMAP...")
+genes_of_interest = ['CD3D', 'MS4A1', 'GNLY']
+nb.embed(adata, color=genes_of_interest)
+
+#Perform clustering
+print("Performing Leiden clustering...")
+nb.cluster(adata, color=['leiden'] + genes_of_interest)
+
+#Save the results
+results_file = "pbmc3k_final_results.h5ad"
+print(f"Saving results to {results_file}...")
+nb.save(results_file, adata)
diff --git a/src/modules/SingleCellWebApp b/src/modules/SingleCellWebApp
new file mode 160000
index 0000000..655ed2d
--- /dev/null
+++ b/src/modules/SingleCellWebApp
@@ -0,0 +1 @@
+Subproject commit 655ed2d37d10c4c3830633da388db467fe845571
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/demo_pcanlysis.py b/src/modules/demo_pcanlysis.py
new file mode 100644
index 0000000..2e16cf3
--- /dev/null
+++ b/src/modules/demo_pcanlysis.py
@@ -0,0 +1,78 @@
+import scanpy as sc
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Step 1: Create random data
+n_cells, n_genes = 100, 2000
+np.random.seed(42)
+
+data = np.random.rand(n_cells, n_genes)
+adata = sc.AnnData(X=data)
+
+adata.obs['cell_type'] = ['type1' if i < 50 else 'type2' for i in range(n_cells)]
+adata.var['gene_id'] = [f"gene{i}" for i in range(n_genes)]
+
+# Step 2: Apply PCA
+def run_pca(adata, n_comps=50, svd_solver='arpack'):
+    """Runs PCA and stores the computed components."""
+    if 'X_pca' in adata.obsm:
+        print("PCA already computed. Overwriting previous results...")
+
+    try:
+        print(f"Running PCA with {n_comps} components using {svd_solver} solver...")
+        sc.pp.normalize_total(adata, target_sum=1e4)
+        sc.pp.log1p(adata)
+        sc.pp.scale(adata)
+        sc.tl.pca(adata, n_comps=n_comps, svd_solver=svd_solver)
+        print("PCA completed.")
+    except Exception as e:
+        raise Exception(f"PCA sırasında hata oluştu: {e}")
+
+run_pca(adata, n_comps=10, svd_solver='arpack')
+
+# Step 3: Plot PCA
+def plot_pca(adata, color=None):
+    """Plots PCA results, colored by a specified attribute (if provided)."""
+    try:
+        print(f"Plotting PCA, color by: {color or 'default'}")
+        sc.pl.pca(adata, color=color, show=False)
+        plt.title(f"PCA - Colored by {color if color else 'default'}")
+        plt.show()
+    except KeyError:
+        raise Exception(f"'{color}' özelliği bulunamadı, renklemek için geçersiz.")
+    except Exception as e:
+        raise Exception(f"PCA görselleştirmesinde hata oluştu: {e}")
+
+plot_pca(adata, color='cell_type')
+
+# Step 4: Plot explained variance
+def plot_variance(adata, log=True):
+    """Plots the variance explained by PCA components."""
+    try:
+        print("Plotting explained variance...")
+        sc.pl.pca_variance_ratio(adata, log=log, show=False)
+        plt.title("PCA: Explained Variance")
+        plt.show()
+    except Exception as e:
+        raise Exception(f"Varyans grafiğinde hata oluştu: {e}")
+
+plot_variance(adata)
+
+# Step 5: Save PCA results
+def save_results(adata, results_file="pca_results.h5ad"):
+    """Saves the PCA results to an H5AD file."""
+    try:
+        print(f"Saving results to {results_file}...")
+        adata.write(results_file)
+        print("Save successful.")
+    except Exception as e:
+        raise Exception(f"Sonuçları kaydederken hata oluştu: {e}")
+
+save_results(adata, "pca_results.h5ad")
+
+# Step 6: Retrieve processed AnnData object
+def get_adata(adata):
+    """Returns the processed AnnData object."""
+    return adata
+
+processed_adata = get_adata(adata)
diff --git a/src/modules/functions/10xformat_to_anndata.py b/src/modules/functions/10xformat_to_anndata.py
new file mode 100644
index 0000000..0b4e68a
--- /dev/null
+++ b/src/modules/functions/10xformat_to_anndata.py
@@ -0,0 +1,43 @@
+import scanpy as sc
+import anndata
+import os
+
+def read_10x_matrix_folder(matrix_dir: str, sample_name: str = None) -> anndata.AnnData:
+    """
+    Loads a 10x Genomics dataset (already extracted folder) using Scanpy.
+
+    Parameters:
+        matrix_dir (str): Path to the directory containing matrix.mtx, barcodes.tsv, and features.tsv or genes.tsv
+        sample_name (str, optional): If given, adds this as a 'source' column in adata.obs
+
+    Returns:
+        anndata.AnnData: The loaded AnnData object
+    """
+    if not os.path.isdir(matrix_dir):
+        raise FileNotFoundError(f"Directory not found: {matrix_dir}")
+
+    adata = sc.read_10x_mtx(matrix_dir, var_names="gene_symbols", cache=True)
+
+    if sample_name:
+        adata.obs["source"] = sample_name
+
+    return adata
+
+if __name__ == "__main__":
+    test_matrix_dir = "/Users/eceyigit/Desktop/filtered_gene_bc_matrices 2/hg19"  # Replace with actual path
+    test_sample_name = "test_sample"
+
+    print(f"Attempting to load data from: {test_matrix_dir}")
+
+    try:
+        adata = read_10x_matrix_folder(test_matrix_dir, test_sample_name)
+        print("\nSuccessfully loaded data!")
+        print(f"AnnData object shape: {adata.shape}")
+        print(f"Sample name: {test_sample_name}")
+        print("\nFirst few observations:")
+        print(adata.obs.head())
+    except Exception as e:
+        print(f"\nError occurred: {str(e)}")
+        print("Please check:")
+        print(f"- Does the directory exist? {os.path.exists(test_matrix_dir)}")
+        print(f"- Does it contain matrix.mtx, barcodes.tsv, and features.tsv files?")
\ No newline at end of file
diff --git a/src/modules/functions/fetching_data_from_census.py b/src/modules/functions/fetching_data_from_census.py
new file mode 100644
index 0000000..16a0476
--- /dev/null
+++ b/src/modules/functions/fetching_data_from_census.py
@@ -0,0 +1,59 @@
+!pip install cellxgene_census
+import cellxgene_census 
+from typing import List, Optional
+import pandas as pd
+import anndata
+
+def census_function(
+    organism: str = "Homo sapiens",
+    cell_filter: Optional[str] = None,
+    gene_filter: Optional[str] = None,
+    metadata_columns: Optional[List[str]] = None,
+    return_anndata: bool = False,
+    census_version: str = "stable"
+) -> pd.DataFrame | anndata.AnnData:
+
+    if metadata_columns is None:        # if there are no parameters set from the user just get the parameters below.
+        metadata_columns = [
+            "dataset_id","assay","suspension_type",
+            "sex","tissue_general","tissue","cell_type"
+        ]
+
+    with cellxgene_census.open_soma(census_version=census_version) as census: #connecting to the census dataset and census version is to decide which census we are fetching the data
+        if return_anndata:   # if true the get_anndata function is called
+            adata = cellxgene_census.get_anndata(
+                census=census,
+                organism=organism,
+                var_value_filter=gene_filter,
+                obs_value_filter=cell_filter,
+                column_names={"obs": metadata_columns},
+            )
+            return adata
+        else:
+            obs = census["census_data"][organism.lower().replace(" ", "_")].obs # we are getting the cell informations about the given organism like "homo sapiens"
+            table = obs.read(
+                value_filter=cell_filter,
+                column_names=metadata_columns
+            )
+            data = table.concat().to_pandas()
+            return data # the data is like an excel table
+
+#testing the function
+if __name__ == "__main__":
+    # example usage
+    print("Test is running...")
+    
+    # test with assumed parameters
+    default_result = census_function()
+    print("\nResult with default parameters (DataFrame):")
+    print(default_result.head())
+    
+    adata = census_function(
+        organism="Homo sapiens",
+        cell_filter="tissue == 'brain' and sex == 'male'",
+        gene_filter="feature_id in ['ENSG00000161798','ENSG00000188229']",
+        metadata_columns=["cell_type","tissue"],
+        return_anndata=True
+    )
+    print("\nAnndata object:")
+    print(adata)
diff --git a/src/modules/functions/reading_tarfile_and_extract10x_to_anndata.py b/src/modules/functions/reading_tarfile_and_extract10x_to_anndata.py
new file mode 100644
index 0000000..c4080c8
--- /dev/null
+++ b/src/modules/functions/reading_tarfile_and_extract10x_to_anndata.py
@@ -0,0 +1,75 @@
+import tarfile
+import os
+import pandas as pd
+import scipy.io
+import scanpy as sc
+import anndata
+from scipy import sparse
+def extract_10x_tar_to_anndata(tar_path: str, extract_dir: str = "./temp") -> anndata.AnnData:
+    os.makedirs(extract_dir, exist_ok=True)
+
+    with tarfile.open(tar_path, "r") as tar:
+        tar.extractall(path=extract_dir)
+
+    matrix_file = None
+    barcodes_file = None
+    genes_file = None
+
+    for root, _, files in os.walk(extract_dir):
+        for file in files:
+            path = os.path.join(root, file)
+            if file.endswith("matrix.mtx"):
+                matrix_file = path
+            elif file.endswith("barcodes.tsv"): # More specific check for barcodes file
+                barcodes_file = path
+            elif file.endswith("genes.tsv") or file.endswith("features.tsv"): # Check for both genes.tsv and features.tsv
+                genes_file = path
+
+    if not all([matrix_file, barcodes_file, genes_file]): # checking for all the necessary files if not foun an error will be raised.
+
+        # Construct the missing list with descriptive strings instead of None
+        missing = []
+        if matrix_file is None:
+            missing.append(".mtx matrix file")
+        if barcodes_file is None:
+            missing.append(".tsv file")
+        if genes_file is None:
+            missing.append(".tsv file")
+
+        raise FileNotFoundError(f"Required file(s) not found in tar file: {', '.join(missing)}")
+
+    matrix = scipy.io.mmread(matrix_file)
+    barcodes = pd.read_csv(barcodes_file, header=None)[0].tolist()
+
+    genes_df = pd.read_csv(genes_file, header=None)
+
+    if genes_df.shape[1] >= 2: # if there are 2 or more columns in genes file, this code takes the first column as gene id and the second as gene names
+        genes = genes_df[1].tolist()
+        gene_ids = genes_df[0].tolist()
+    else:
+        genes = genes_df[0].tolist()
+        gene_ids = genes_df[0].tolist()
+
+    matrix_transposed = matrix.transpose().tocsc()  # transposing the matrice because anndata format is obsxvar
+
+    adata = anndata.AnnData(X=matrix_transposed)
+    adata.obs_names = barcodes
+    adata.var_names = genes
+    if gene_ids and gene_ids != genes:
+
+         adata.var['gene_id'] = gene_ids
+# this code checks if there's a list called gene_ids and is it's different from the existing genes list if both are true it adds these gene_ids as a new column named 'gene_id' to the AnnData object's gene-specific information (adata.var).
+
+    return adata
+#testing
+if __name__ == "__main__":
+    test_file = "/pbmc3k_filtered_gene_bc_matrices.tar.gz"
+    try:
+        adata = extract_10x_tar_to_anndata(test_file)
+        print("test is succesfull, AnnData :")
+        print(adata)
+    except FileNotFoundError:
+        print(f" Error: {test_file} not found! Please try another file path.")
+
+
+
diff --git a/src/modules/get_marker_genes_demo.py b/src/modules/get_marker_genes_demo.py
new file mode 100644
index 0000000..2ff2dbb
--- /dev/null
+++ b/src/modules/get_marker_genes_demo.py
@@ -0,0 +1,18 @@
+import scanpy as sc
+import pandas as pd
+from markedgenes import get_marker_genes  # defined function in main code
+
+# Demo dataset
+adata = sc.datasets.pbmc3k()
+
+#preprocessing and clustering (same with the main code)
+sc.pp.pca(adata, svd_solver='arpack')
+sc.pp.neighbors(adata)
+sc.tl.leiden(adata)  # Leiden clustering yap
+
+# ranking genes between groups
+sc.tl.rank_genes_groups(adata, 'leiden', method='t-test')
+
+# recalling the function
+scores_and_pvals = get_scores_and_pvals(adata)
+print(scores_and_pvals)
diff --git a/src/modules/get_rank_genes_demo.py b/src/modules/get_rank_genes_demo.py
new file mode 100644
index 0000000..45380a1
--- /dev/null
+++ b/src/modules/get_rank_genes_demo.py
@@ -0,0 +1,23 @@
+from rank_genes_group import rank_genes_groups_custom
+import scanpy as sc
+
+sc.datasets.pbmc3k()
+sc.pp.normalize_total(adata, target_sum=1e4)
+sc.pp.log1p(adata)
+sc.pp.highly_variable_genes(adata, min_mean=0.0125, max_mean=3, min_disp=0.5)
+adata = adata[:, adata.var.highly_variable]
+sc.pp.scale(adata, max_value=10)
+
+# PCA and neighborhoods
+sc.pp.pca(adata, svd_solver='arpack')  # Bu satır uyarıyı çözer
+sc.pp.neighbors(adata, n_pcs=40)
+
+# Clustering (Leiden)
+sc.tl.leiden(adata, resolution=1.0)
+
+sc.settings.verbosity = 2
+
+#to save results
+results_file = "pbmc3k_rank_genes.h5ad"
+rank_genes_groups_custom(adata, groupby='leiden' , method='t-test' , n_genes=25 , sharey=False)
+adata.write(results_file)
\ No newline at end of file
diff --git a/src/modules/get_scors_and_pvals.py b/src/modules/get_scors_and_pvals.py
new file mode 100644
index 0000000..f079087
--- /dev/null
+++ b/src/modules/get_scors_and_pvals.py
@@ -0,0 +1,36 @@
+import scanpy as sc
+import pandas as pd
+from rank_genes_group import rank_genes_groups
+
+# Load the dataset
+adata = sc.datasets.pbmc3k()
+
+# Apply PCA to reduce dimensionality (to avoid high-dimension warning)
+sc.pp.pca(adata, svd_solver='arpack')
+
+# Calculate neighbors on PCA-reduced data
+sc.pp.neighbors(adata)
+
+# Run Leiden algorithm (with future-proofing for 'igraph' backend)
+sc.tl.leiden(adata, resolution=1.0, flavor='igraph', directed=False, n_iterations=2)
+
+# Run differential expression analysis to rank genes
+sc.tl.rank_genes_groups(adata, groupby='leiden', method='t-test')
+
+
+# Reduce verbosity for cleaner output
+sc.settings.verbosity = 2
+
+# Define a function to get gene names and p-values from rank_genes_groups
+def get_scores_and_pvals(adata):
+    result = adata.uns['rank_genes_groups']
+    groups = result['names'].dtype.names
+    df = pd.DataFrame(
+        {group + '_' + key[:1]: result[key][group]
+         for group in groups for key in ['names', 'pvals']}).head(5)
+    return df
+
+# Run the function to get the results
+scores_and_pvals = get_scores_and_pvals(adata)
+print(scores_and_pvals)
+
diff --git a/src/modules/get_scors_and_pvals_demo.py b/src/modules/get_scors_and_pvals_demo.py
new file mode 100644
index 0000000..87429ce
--- /dev/null
+++ b/src/modules/get_scors_and_pvals_demo.py
@@ -0,0 +1,17 @@
+from get_scors_and_pvals import get_scores_and_pvals
+import scanpy as sc
+import pandas as pd
+
+# Demo dataset
+adata = sc.datasets.pbmc3k() 
+
+# ranking genes between groups
+sc.pp.pca(adata, svd_solver='arpack')
+sc.pp.neighbors(adata)
+sc.tl.leiden(adata)
+sc.tl.rank_genes_groups(adata, 'leiden', method='t-test')
+
+# recalling the function
+scores_and_pvals = get_scores_and_pvals(adata)
+print(scores_and_pvals)
+ 
\ No newline at end of file
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
diff --git a/src/modules/pvals_demo.py b/src/modules/pvals_demo.py
new file mode 100644
index 0000000..3445c66
--- /dev/null
+++ b/src/modules/pvals_demo.py
@@ -0,0 +1,16 @@
+from get_scors_and_pvals import get_scores_and_pvals
+import scanpy as sc
+import pandas as pd
+
+# Demo dataset
+adata = sc.datasets.pbmc3k() 
+
+# ranking genes between groups
+sc.pp.pca(adata, svd_solver='arpack')
+sc.pp.neighbors(adata)
+sc.tl.leiden(adata)
+sc.tl.rank_genes_groups(adata, 'leiden', method='t-test')
+
+# recalling the function
+scores_and_pvals = get_scores_and_pvals(adata)
+print(scores_and_pvals)
\ No newline at end of file
diff --git a/src/modules/qc.py b/src/modules/qc.py
index e69de29..bca2052 100644
--- a/src/modules/qc.py
+++ b/src/modules/qc.py
@@ -0,0 +1,52 @@
+import scanpy as sc
+import matplotlib.pyplot as plt
+
+def run_pca(adata, n_comps=50, svd_solver='arpack'):
+    """Runs PCA and stores the computed components."""
+    if 'X_pca' in adata.obsm:
+        print("PCA already computed. Overwriting previous results...")
+    
+    try:
+        print(f"Running PCA with {n_comps} components using {svd_solver} solver...")
+        sc.pp.normalize_total(adata, target_sum=1e4)
+        sc.pp.log1p(adata)
+        sc.pp.scale(adata)
+        sc.tl.pca(adata, n_comps=n_comps, svd_solver=svd_solver)
+        print("PCA completed.")
+    except Exception as e:
+        raise Exception(f"PCA işleminde hata oluştu: {e}")
+
+def plot_pca(adata, color=None):
+    """Plots PCA results, colored by a specified attribute (if provided)."""
+    try:
+        print(f"Plotting PCA, color by: {color or 'default'}")
+        sc.pl.pca(adata, color=color, show=False)
+        plt.title(f"PCA - Colored by {color if color else 'default'}")
+        plt.show()
+    except KeyError:
+        raise Exception(f"Belirtilen renk özelliği '{color}' bulunamadı.")
+    except Exception as e:
+        raise Exception(f"PCA grafiğinde hata oluştu: {e}")
+
+def plot_variance(adata, log=True):
+    """Plots the variance explained by PCA components."""
+    try:
+        print("Plotting explained variance...")
+        sc.pl.pca_variance_ratio(adata, log=log, show=False)
+        plt.title("PCA: Explained Variance")
+        plt.show()
+    except Exception as e:
+        raise Exception(f"Varyans grafiğinde hata oluştu: {e}")
+
+def save_results(adata, results_file="pca_results.h5ad"):
+    """Saves the PCA results to an H5AD file."""
+    try:
+        print(f"Saving results to {results_file}...")
+        adata.write(results_file)
+        print("Save successful.")
+    except Exception as e:
+        raise Exception(f"Sonuçlar kaydedilirken hata oluştu: {e}")
+
+def get_adata(adata):
+    """Returns the processed AnnData object."""
+    return adata
\ No newline at end of file
diff --git a/src/modules/rank_genes_group_demo.py b/src/modules/rank_genes_group_demo.py
new file mode 100644
index 0000000..1b409d3
--- /dev/null
+++ b/src/modules/rank_genes_group_demo.py
@@ -0,0 +1,24 @@
+from rankgenesgroup import rank_genes_groups
+import scanpy as sc
+
+adata = sc.datasets.pbmc3k()
+
+sc.pp.normalize_total(adata, target_sum=1e4)
+sc.pp.log1p(adata)
+sc.pp.highly_variable_genes(adata, min_mean=0.0125, max_mean=3, min_disp=0.5)
+adata = adata[:, adata.var.highly_variable]
+sc.pp.scale(adata, max_value=10)
+
+# PCA and neighborhoods
+sc.pp.pca(adata, svd_solver='arpack')  # Bu satır uyarıyı çözer
+sc.pp.neighbors(adata, n_pcs=40)
+
+# Clustering (Leiden)
+sc.tl.leiden(adata, resolution=1.0)
+
+sc.settings.verbosity = 2
+
+#to save results
+results_file = "pbmc3k_rank_genes.h5ad"
+sc.tl.rank_genes_groups(adata, groupby='leiden' , method='t-test' , n_genes=25 , sharey=False)
+adata.write(results_file)
diff --git a/src/modules/rank_genes_violin.py b/src/modules/rank_genes_violin.py
new file mode 100644
index 0000000..022747d
--- /dev/null
+++ b/src/modules/rank_genes_violin.py
@@ -0,0 +1,18 @@
+import scanpy as sc
+adata = sc.datasets.pbmc3k()
+sc.pp.neighbors(adata)
+sc.tl.leiden(adata, resolution=1.0)
+sc.settings.verbosity = 2  # reduce the verbosity
+
+def get_rank_genes_groups_violin(adata, groups='0', n_genes=8):
+   """
+   -Plots a violin plot for the top ranked genes in specified groups (clusters).
+   
+    Parameters:
+    - adata: AnnData object with results from rank_genes_groups
+    - groups: List or string specifying the groups (clusters) to plot (default is all groups)
+    - n_genes: Number of top genes to display in the plot (default is 8)
+
+   """
+   sc.pl.rank_genes_groups_violin(adata, groups=groups, n_genes=n_genes)
+   return adata
diff --git a/src/modules/rank_genes_violin_demo.py b/src/modules/rank_genes_violin_demo.py
new file mode 100644
index 0000000..879a1f0
--- /dev/null
+++ b/src/modules/rank_genes_violin_demo.py
@@ -0,0 +1,17 @@
+from rank_genes_violin import get_rank_genes_groups_violin
+import scanpy as sc
+
+#sample dataset
+adata = sc.datasets.pbmc3k()
+
+# calculating neighborhoods
+sc.pp.neighbors(adata)
+
+sc.tl.leiden(adata, resolution=1.0)
+
+# Differential expression analysis (with t-test)
+sc.tl.rank_genes_groups(adata, groupby='leiden', method='t-test')
+
+
+# calling the function and visualizing
+get_rank_genes_groups_violin(adata, groups='0', n_genes=8)
diff --git a/src/modules/rankgenesgroup.py b/src/modules/rankgenesgroup.py
new file mode 100644
index 0000000..795fd94
--- /dev/null
+++ b/src/modules/rankgenesgroup.py
@@ -0,0 +1,36 @@
+import scanpy as sc
+
+# Örnek veri setini yükle
+adata = sc.datasets.pbmc3k()
+
+# Temel ön işleme
+sc.pp.normalize_total(adata, target_sum=1e4)
+sc.pp.log1p(adata)
+sc.pp.highly_variable_genes(adata, min_mean=0.0125, max_mean=3, min_disp=0.5)
+adata = adata[:, adata.var.highly_variable]
+sc.pp.scale(adata, max_value=10)
+
+# PCA ve komşuluk hesaplama
+sc.pp.pca(adata, svd_solver='arpack')  # Bu satır uyarıyı çözer
+sc.pp.neighbors(adata, n_pcs=40)
+
+# Clustering (Leiden)
+sc.tl.leiden(adata, resolution=1.0)
+
+# Verbosity ayarı
+sc.settings.verbosity = 2
+
+# Sonuçları kaydetmek için bir dosya ismi belirle
+results_file = "pbmc3k_rank_genes.h5ad"
+
+# Gen sıralama ve görselleştirme fonksiyonu
+def rank_genes_groups(adata, method='t-test', n_genes=25, sharey=False):
+    """
+    - Perform differential expression analysis and plot top marker genes.
+    """
+    sc.tl.rank_genes_groups(adata, groupby='leiden', method=method)
+    sc.pl.rank_genes_groups(adata, n_genes=n_genes, sharey=sharey)
+    adata.write(results_file)
+
+# Fonksiyonu çağır
+rank_genes_groups(adata)
diff --git a/src/pca_analysis.py b/src/pca_analysis.py
new file mode 100644
index 0000000..4760724
--- /dev/null
+++ b/src/pca_analysis.py
@@ -0,0 +1,60 @@
+import scanpy as sc
+import matplotlib.pyplot as plt
+
+def run_pca(adata, n_comps=50, svd_solver='arpack'):
+    """Runs PCA and stores the computed components."""
+    try:
+        if 'X_pca' in adata.obsm:
+            print("PCA already computed. Overwriting previous results...")
+        
+        print(f"Running PCA with {n_comps} components using {svd_solver} solver...")
+        sc.pp.normalize_total(adata, target_sum=1e4)  # Normalization
+        sc.pp.log1p(adata)  # Log transformation
+        sc.pp.scale(adata)  # Scaling
+        sc.tl.pca(adata, n_comps=n_comps, svd_solver=svd_solver)
+        
+        print("PCA completed.")
+    except Exception as e:
+        print(f"Error during PCA: {e}")
+        raise  
+
+def plot_pca(adata, color=None):
+    """Plots PCA results, colored by a specified attribute (if provided)."""
+    try:
+        print(f"Plotting PCA, color by: {color or 'default'}")
+        sc.pl.pca(adata, color=color, show=False)
+        plt.title(f"PCA - Colored by {color if color else 'default'}")
+        plt.show()
+    except KeyError:
+        print(f"Warning: '{color}' not found. Using default coloring.")
+        sc.pl.pca(adata, show=False)
+        plt.title("PCA - Default Coloring")
+        plt.show()
+    except Exception as e:
+        print(f"Error in PCA plot: {e}")
+        raise  
+
+def plot_variance(adata, log=True):
+    """Plots the variance explained by PCA components."""
+    try:
+        print("Plotting explained variance...")
+        sc.pl.pca_variance_ratio(adata, log=log, show=False)
+        plt.title("PCA: Explained Variance")
+        plt.show()
+    except Exception as e:
+        print(f"Error in variance plot: {e}")
+        raise  
+
+def save_results(adata, results_file="pca_results.h5ad"):
+    """Saves the PCA results to an H5AD file."""
+    try:
+        print(f"Saving results to {results_file}...")
+        adata.write(results_file)
+        print("Save successful.")
+    except Exception as e:
+        print(f"Error saving results: {e}")
+        raise  
+
+def get_adata(adata):
+    """Returns the processed AnnData object."""
+    return adata