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

ABSTRACT:

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

SUBMITTER: Alsaggaf I 

PROVIDER: S-EPMC12417077 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature