CIRI‐Deep Enables Single‐Cell and Spatial Transcriptomic Analysis of Circular RNAs with Deep Learning

Abstract Circular RNAs (circRNAs) are a crucial yet relatively unexplored class of transcripts known for their tissue‐ and cell‐type‐specific expression patterns. Despite the advances in single‐cell and spatial transcriptomics, these technologies face difficulties in effectively profiling circRNAs due to inherent limitations in circRNA sequencing efficiency. To address this gap, a deep learning model, CIRI‐deep, is presented for comprehensive prediction of circRNA regulation on diverse types of RNA‐seq data. CIRI‐deep is trained on an extensive dataset of 25 million high‐confidence circRNA regulation events and achieved high performances on both test and leave‐out data, ensuring its accuracy in inferring differential events from RNA‐seq data. It is demonstrated that CIRI‐deep and its adapted version enable various circRNA analyses, including cluster‐ or region‐specific circRNA detection, BSJ ratio map visualization, and trans and cis feature importance evaluation. Collectively, CIRI‐deep's adaptability extends to all major types of RNA‐seq datasets including single‐cell and spatial transcriptomic data, which will undoubtedly broaden the horizons of circRNA research.