pguIMP

pguIMP provides reproducible preprocessing of bioanalytical laboratory data for pharmacological, lipidomic, and biomedical research.

Key Features:

• Reproducible preprocessing: Implements a fixed sequence of preprocessing steps to ensure consistent results across analyses.
• Advanced data cleaning and imputation: Incorporates machine-learning-based imputation techniques, including k-nearest-neighbors-based imputation, to replace missing values and outliers.
• Data transformation and structure preservation: Performs data transformations aimed at preserving essential data structures such as clusters.
• Clustering algorithms: Supports k-means clustering and density-based spatial clustering of applications with noise.
• Visualization and error correction: Provides methods for visualization and error correction to identify and rectify issues in datasets.
• R integration: Implemented as an R package and integrates into the R data science environment.
• Evaluation on domain datasets: Evaluated on lipidomics and drug research bioanalytical datasets.

Scientific Applications:

• Bioanalytical data preprocessing: Preprocessing of laboratory-derived bioanalytical data in biomedical research where data quality is critical.
• Pharmacological and drug research: Data preparation and cleaning for pharmacological studies and drug-related analyses.
• Lipidomics: Preprocessing of lipidomics datasets to support downstream clustering and pattern discovery.

Methodology:

Uses a fixed sequence of preprocessing steps including machine-learning-based imputation (including k-nearest-neighbors), replacement of missing values and outliers, data transformations, k-means clustering, density-based spatial clustering of applications with noise, visualization, and error correction; implemented as an R package.