2023/10/04 by Gyutaek Oh, Baekgyu Choi, Oh, Gyutaek +5 · 3 citations
Biochemistry, Genetics and Molecular Biology · #Artificial Intelligence (cs.AI) #Cancer-related molecular mechanisms research #FOS: Biological sciences #FOS: Computer and information sciences #Genomics (q-bio.GN) #Machine Learning (cs.LG) #MicroRNA in disease regulation #Quantitative Methods (q-bio.QM) #Single-cell and spatial transcriptomics
paper · pdf · doi:10.48550/arxiv.2310.02713
openalex publication_date 2023/10/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Single-cell RNA sequencing (scRNA-seq) has made significant strides in unraveling the intricate cellular diversity within complex tissues. This is particularly critical in the brain, presenting a greater diversity of cell types than other tissue types, to gain a deeper understanding of brain function within various cellular contexts. However, analyzing scRNA-seq data remains a challenge due to inherent measurement noise stemming from dropout events and the limited utilization of extensive gene expression information. In this work, we introduce scHyena, a foundation model designed to address these challenges and enhance the accuracy of scRNA-seq analysis in the brain. Specifically, inspired by the recent Hyena operator, we design a novel Transformer architecture called singe-cell Hyena (scHyena) that is equipped with a linear adaptor layer, the positional encoding via gene-embedding, and a bidirectional Hyena operator. This enables us to process full-length scRNA-seq data without losing any information from the raw data. In particular, our model learns generalizable features of cells and genes through pre-training scHyena using the full length of scRNA-seq data. We demonstrate the superior performance of scHyena compared to other benchmark methods in downstream tasks, including cell type classification and scRNA-seq imputation.