GFETM: Genome foundation-based embedded topic model for scATAC-seq modeling

Single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq) enables investigation of open chromatin landscapes at single-cell resolution. However, analyzing scATAC-seq data remains challenging due to inherent sparsity and noise. Genome foundation models (GFMs), pre-trained on extensive DNA sequence datasets, have demonstrated effectiveness in genome analysis. Because open chromatin regions (OCRs) harbor salient sequence features, we hypothesized that leveraging GFMs' sequence embeddings could enhance scATAC-seq modeling accuracy and generalizability. We introduce the genome foundation embedded topic model (GFETM), an interpretable deep learning framework combining GFMs with the embedded topic model (ETM) for scATAC-seq analysis. By integrating DNA sequence embeddings extracted by a GFM from OCRs, GFETM demonstrates superior accuracy and generalizability and captures cell-state-specific transcription factor (TF) activity with both zero-shot inference and attention-mechanism analysis. Finally, the topic mixtures inferred by GFETM reveal biologically meaningful epigenomic signatures of kidney diabetes. A record of this paper's transparent peer review process is included in the supplemental information.