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.

通过介绍 scHyena 模型，本研究提出了一种解决脱落事件和基因表达信息利用有限的测量噪声带来的挑战，以提高脑部 scRNA-seq 分析准确性的新方法，展示了该模型在细胞类型分类和 scRNA-seq 填补方面相比其他基准方法的卓越性能。