xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data

arXiv:2311.15156v2 Announce Type: replace
Abstract: Advances in high-throughput sequencing technology have led to significant progress in measuring gene expressions at the single-cell level. The amount of publicly available single-cell RNA-seq (scRNA-seq) data is already surpassing 50M records for humans with each record measuring 20,000 genes. This highlights the need for unsupervised representation learning to fully ingest these data, yet classical transformer architectures are prohibitive to train on such data in terms of both computation and memory. To address this challenge, …

arxiv cs.ai cs.lg data q-bio.gn representation rna rna-seq scalable type