Benchmark data for Single Cell library preparations and sequencing technology + Spatial
Single cell and spatial transcriptomics have dramatically changed how we can profile RNA from heterogenous biological samples. Combining single cell and spatial profiling with long read RNA-Seq promises to enable the discovery and quantification of individual RNA isoforms at the single-cell level. However, highly multiplexed data such as from a single cell experiment only generates a limited number of reads for each cell, constituting a major challenge for transcript discovery and quantification with existing approaches that usually have limited power for samples with low sequencing depth.  Here we present Bambu-Clump, a computational method that performs transcript discovery and quantification from single cell and spatial long read RNA-Seq data using information from both each cell and the cell cluster. Using this approach, Bambu-Clump provides the most accurate transcript discovery compared to other existing methods, and improves transcript quantification compared to methods that rely on estimates derived from single cells. We apply Bambu-Clump to identify fusion transcripts in single-cells,  compare 5' and 3' selection protocols, and identify novel isoform cell-type markers in spatial mouse brain data. Together, Bambu-Clump provides an easy-to-use, efficient, and accurate method for analysing individual isoform expression for single cells and cell clusters across multiple datasets and replicates from long read RNA-Seq.