HNSC filtered datasets from GSE139324 and GSE164690

The filtered HNSC datasets include GSE139324 and GSE164690, comprising scRNA-seq samples from healthy donors, as well as HPV-negative and HPV-positive cases.This project uses deep learning models to improve clustering accuracy in large-scale single-cell atlases. By integrating contrastive learning and transformer based variational autoencoders (VAEs), these models learn biologically meaningful low-dimensional representations, enabling precise identification of cell types, including rare and transitional populations.Key innovations include:Robust handling of batch effects and technical noiseScalable architecture for millions of cellsImproved clustering metrics (ARI, NMI) on benchmark datasetsEnhanced interpretability by linking latent features to gene programsThese models offer a powerful tool for building accurate, scalable cell atlases and advancing our understanding of cellular diversity.