Semi-supervised disentangled representation learning for single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) data are inherently high-dimensional, and most analysis tools reduce this complexity by projecting the data into a low-dimensional latent space before performing downstream analyses. However, the resulting representations are often entangled, with biological or technical factors such as batch effects and disease stages mixed together, which complicates interpretation. Recent methods have introduced disentanglement mechanisms to improve interpretability, but they typically require large amounts of well-annotated data to perform well or are limited to factors with only a few categories. To address these challenges, we propose SCDRL (Semi-Supervised Disentangled Representation Learning for Single-Cell RNA Sequencing Data), a method that uses gene expression profiles together with a small proportion of labeled samples to learn disentangled representations that separate batch effects, cell types, and other biological signals, thereby enhancing interpretability. Unlike existing approaches, SCDRL generalizes from factors with only a few categories to complex settings involving more than 10 cell types. Experiments on both simulated and real-world datasets demonstrate that SCDRL consistently outperforms existing disentangled representation learning methods for scRNA-seq data, even when only 5% of labeled samples are available.