Igniting full-length isoform analysis in single-cell and spatial RNA-seq data with FLAMESv2

Abstract Long-read single-cell RNA-sequencing enables the profiling of RNA isoform expression and alternative splicing at single cell resolution. However, diverse single-cell technologies and sparse isoform data demand flexible and accurate analysis tools. We introduce FLAMESv2, a highly modular and protocol-agnostic R/Bioconductor package for long-read single-cell RNA-seq data analysis. FLAMESv2 supports a wide range of single-cell and spatial protocols, is highly configurable, scales to allow multi-sample analysis and provides versatile visualisation and analysis outputs. We demonstrate its compatibility with both droplet-based and combinatorial barcoding single-cell methods, as well as spatial transcriptomics workflows. Benchmarking confirms FLAMESv2 achieves field-leading performance across key analysis tasks. Applying FLAMESv2 to in vitro differentiation of stem cells into neurons, we identify cell-types, differentiation trajectories, expression of annotated and novel isoforms and isoform expression diversity and heterogeneity within individual cells. FLAMESv2 provides a comprehensive, flexible approach to analysing long-read single-cell RNA-sequencing, unlocking this powerful methodology for RNA isoform characterisation. Competing Interest Statement Y.D.J.P., R.D.P., A.L., Q.G., N.M.D., M.B.C. and Y.Y. have received support from Oxford Nanopore Technologies (ONT) to present their findings at scientific conferences. However, ONT played no role in the study design, execution, analysis, or publication of this research. Footnotes Added benchmarking against other software. Updated Figure 2 to include benchmarking results.