DropDAE: Denosing Autoencoder with Contrastive Learning for Addressing Dropout Events in scRNA-seq Data. Bioengineering (Basel) 2025 Jul 31;12(8)
Date
08/30/2025Pubmed ID
40868342Pubmed Central ID
PMC12383624DOI
10.3390/bioengineering12080829Scopus ID
2-s2.0-105015446085 (requires institutional sign-in at Scopus site)Abstract
Single-cell RNA sequencing (scRNA-seq) has revolutionized molecular biology and genomics by enabling the profiling of individual cell types, providing insights into cellular heterogeneity. Deep learning methods have become popular in single cell analysis for tasks such as dimension reduction, cell clustering, and data imputation. In this work, we introduce DropDAE, a denoising autoencoder (DAE) model enhanced with contrastive learning, to specifically address the dropout events in scRNA-seq data, where certain genes show very low or even zero expression levels due to technical limitations. DropDAE uses the architecture of a denoising autoencoder to recover the underlying data patterns while leveraging contrastive learning to enhance group separation. Our extensive evaluations across multiple simulation settings based on synthetic data and a real-world dataset demonstrate that DropDAE not only reconstructs data effectively but also further improves clustering performance, outperforming existing methods in terms of accuracy and robustness.
Author List
Juan W, Ahn KW, Chen YG, Lin CWAuthors
Kwang Woo Ahn PhD Director, Professor in the Data Science Institute department at Medical College of WisconsinYi-Guang Chen PhD Professor in the Pediatrics department at Medical College of Wisconsin
Chien-Wei Lin PhD Associate Professor in the Data Science Institute department at Medical College of Wisconsin
