Unified Preprocessing Toolkit for Proteomics and Metabolomics

Provides unified workflows for quality control, normalization, and visualization of proteomic and metabolomic data. The package simplifies preprocessing through automated imputation, scaling, and principal component analysis (PCA)-based exploratory analysis, enabling researchers to prepare omics datasets efficiently for downstream statistical and machine learning analyses.

OmicsPrepR

Authors

• Isaac Osei – Maintainer ([email protected])
• Dennis Opoku Boadu – Author
• Chettupally Anil Carie – Author

Overview

OmicsPrepR is an integrated R package that provides unified workflows for quality control, normalization, and visualization of proteomic and metabolomic data.
It simplifies preprocessing through automated imputation, scaling, and PCA-based exploratory analysis, enabling efficient preparation of omics datasets for downstream statistical and machine learning analyses.

Installation

You can install the development version directly from GitHub:

```r # install.packages(“devtools”) devtools::install_github(“ikemillar/OmicsPrepR”)

library(OmicsPrepR)

Load omics dataset

omics_data <- load_omics(“data.csv”)

Impute missing values

omics_imputed <- impute_missing(omics_data)

Export cleaned dataset

export_clean(omics_imputed, “cleaned_data.csv”)

Visualize PCA structure

plot_omics(omics_imputed)