Less is more: improving cell-type identification with augmentation-free single-cell RNA-Seq contrastive learning.

Motivation: Cell-type identification is one of the most important tasks in single-cell RNA Sequencing (scRNA-Seq) analysis. Recent research has revealed contrastive learning's great potential in handling multiple cell-type identification tasks.

Results: In this work, we proposed a novel augmentation-free scRNA-Seq contrastive learning (AF-RCL) algorithm, which simplifies the conventional data augmentation operation and adopts a new contrastive learning loss function. A large-scale empirical evaluation suggests that AF-RCL not only outperformed other contrastive learning-based cell-type identification methods but also obtained state-of-the-art predictive performance compared with other well-known cell-type identification methods. Further analysis also shows AF-RCL's advantages in learning high-quality discriminative feature representations based on scRNA-Seq expression profiles.

Availability and implementation: The source code is available at https://doi.org/10.6084/m9.figshare.28830311.v1 and at https://github.com/ibrahimsaggaf/AFRCL. The pre-trained AF-RCL encoders can be downloaded from https://doi.org/10.5281/zenodo.15109736, and the scRNA-Seq datasets used in this work can be downloaded from https://doi.org/10.5281/zenodo.8087611.