Transferable representations of single-cell transcriptomic data

Abstract

Advances in single-cell RNA-seq (scRNA-seq) technologies are enabling the construction of large-scale, human-annotated reference cell atlases, creating unprecedented opportunities to accelerate future research. However, effectively leveraging information from these atlases, such as clustering labels or cell type annotations, remains challenging due to substantial technical noise and sparsity in scRNA-seq measurements. To address this problem, we present HD-AE, a deep autoencoder designed to extract integrated low-dimensional representations of scRNA-seq measurements across datasets from different labs and experimental conditions (https://github.com/suinleelab/HD-AE). Unlike previous approaches, HD-AEs representations successfully transfer to new query datasets without needing to retrain the model. Researchers without substantial computational resources or machine learning expertise can thus leverage the robust representations learned by pretrained HD-AE models to compare embeddings of their own data with previously generated sets of reference embeddings.