Reproducible and Multi-Study Transcriptomic Integration with <i>disint</i> , Disease Integration and Clustering Toolkit, and Application to Drug Repositioning

Integrating Big Data, such as large-scale transcriptomic datasets across diseases, continues to be a major challenge. This is in part due to inconsistent preprocessing and the lack of a standardized, reproducible analytical framework. Existing pipelines often rely on manual parameter tuning and fragmented scripts, which limits cross-dataset comparability and downstream interpretability. We developed <i>disint</i> (disease integration and clustering toolkit), an open-source Python framework for standardized cross-dataset expression integration, embedding, and clustering. The pipeline implements housekeeping gene-based normalization, disease-specific log₂ fold-change computation, automated Uniform Manifold Approximation and Projection hyperparameter optimization, and adaptive K-means clustering. Building on its outputs, we further implemented a prototype downstream module, <i>disease reposition</i>, which extracts disease-specific gene signatures, evaluates their shared components, and explores potential drug repositioning candidates. The framework was validated on 28 transcriptomic datasets encompassing 34 disease categories and 386 samples, including 255 patient and 131 healthy control samples, covering 194,182 genes in total. These results highlight the reproducibility, scalability, and translational versatility of our proposed framework.