Last pca process

src/demo_pcanlysis.py

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+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)

src/modules/demo_pcanlysis.py

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+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)

src/modules/qc.py

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+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

src/pca_analysis.py

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+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