Experimental Batch Effect Correction Machine Learning Model

Develop a machine learning framework for detecting and correcting systematic batch effects in large-scale scientific experimental datasets. Create a model that can automatically identify technical variations across different experiment runs, instrument calibrations, or research sites. Implement dimensionality reduction techniques like PCA and UMAP to visualize batch effects, design a correction algorithm using either ComBat or surrogate variable analysis approaches, and generate statistical reports quantifying the effectiveness of batch normalization. The solution should be generalizable across genomics, proteomics, and multi-omics research contexts.