OmicsCheck Enables Improved Selection of High-Quality Gene Expression Datasets: A Pre-download Screening Tool with Biological Impact Assessment

The growing volume of gene expression datasets available through the Gene Expression Omnibus (GEO) presents both opportunities and challenges to researchers. While the abundance of data offers rich potential for biological insights, it also increases the risk of downloading and analyzing low-quality or poorly structured datasets, leading to wasted time and computational resources. To address this issue, we introduce OmicsCheck, a novel statistical model designed for the pre-download evaluation of gene expression matrices from GEO. OmicsCheck performs rapid screening of expression datasets by analyzing their internal structure, assessing matrix orientation, identifying outliers, suggesting log transformations based on skewness and value distribution, and computing a weighted Quality Assessment Score (QA Score) derived from key statistical features. Additionally, OmicsCheck highlights the most variable genes using variance filtering and visualizes dataset integrity using PCA and a correlation-based Gene Expression Similarity Network, providing researchers with a comprehensive snapshot before data acquisition. Our tool was tested on nine GEO studies spanning human, animal, and plant datasets, and successfully identified datasets with questionable quality, atypical variance distribution, or transposition errors. OmicsCheck is fully automated, lightweight, and outputs a structured PDF report and exportable summary files (CSV/JSON), making it a practical solution for exploratory and large-scale bioinformatics projects. By offering a reliable pre-download diagnostic layer, OmicsCheck empowers researchers to make informed decisions on data suitability before committing to full-scale analysis, ultimately enhancing the efficiency, reproducibility, and rigor of transcriptomic studies.