geomx_rna_analysis

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import QuantileTransformer
import statsmodels.api as sm
from statsmodels.stats.multitest import multipletests
import scanpy as sc

# Step 1: Load the Data
data_dir = 'path_to_your_data_directory/'

# Assuming your data is in CSV format; modify this as needed
demo_data = pd.read_csv(f'{data_dir}/your_data.csv')

# Step 2: Data Normalization
transformer = QuantileTransformer(output_distribution='normal')
normalized_data = pd.DataFrame(transformer.fit_transform(demo_data.iloc[:, 1:]), columns=demo_data.columns[1:])
normalized_data.insert(0, 'Sample', demo_data['Sample'])  # Preserving the sample column

# Step 3: Quality Control and Filtering
filtered_data = normalized_data[normalized_data['ROICount'] > 100]  # Modify based on your QC metric

# Step 4: Exploratory Data Analysis - Boxplot
plt.figure(figsize=(10, 6))
sns.boxplot(data=filtered_data.melt(id_vars=['Sample']), x='variable', y='value')
plt.xticks(rotation=90)
plt.show()

# Step 5: Differential Expression Analysis
# Assuming 'Group' is a categorical column in your data
design_matrix = pd.get_dummies(filtered_data['Group'], drop_first=True)
# Fit a linear model (example: expression vs group)
model = sm.OLS(filtered_data.drop(columns=['Sample', 'Group']).values, design_matrix.values).fit()
results = model.summary()

# Extract p-values for differential expression
pvals = model.pvalues
adjusted_pvals = multipletests(pvals, alpha=0.05, method='fdr_bh')[1]
top_genes = filtered_data.columns[adjusted_pvals < 0.05]

# Step 6: Heatmap Visualization
sns.clustermap(filtered_data.drop(columns=['Sample', 'Group']).T, cmap="viridis", figsize=(10,10))
plt.show()

# Step 7: Save Results
filtered_data.to_csv('filtered_data.csv', index=False)

# Optional Step: Advanced Analysis - Clustering with Scanpy (for single-cell RNA-seq data)
adata = sc.AnnData(filtered_data.drop(columns=['Sample', 'Group']).values)
sc.pp.neighbors(adata, n_neighbors=10)
sc.tl.umap(adata)
sc.pl.umap(adata, color='Sample')