Allos: an integrated Python toolkit for isoform-level single-cell and spatial in-situ transcriptomics

Abstract

Single-cell RNA sequencing and spatial transcriptomics have transformed our understanding of the transcriptional landscape by enabling high-resolution profiling of gene expression. Yet most experimental pipelines and their associated analysis frameworks collapse transcript diversity into gene-level counts, obscuring alternative splicing and isoform usage. The increasing ability of long-read sequencing to recover full-length transcripts from single cells and spatially barcoded tissues has created a pressing need for computational frameworks to support the storage, analysis, and visualisation of isoform-resolved data. Existing tools for isoform and splicing analysis either specialise in bulk, single-cell, or spatial RNA-seq assays in isolation and remain fragmented across languages and data models, limiting interoperability and hindering widespread adoption. We present Allos, a Python framework for isoform-level single-cell and spatial transcriptomics analysis. Built on the AnnData data model, Allos natively represents transcript-level quantification and integrates directly with GTF/GFF and FASTA annotations. Allos enables differential isoform usage screening, multi-panel visualisation, structural transcript interpretation, and protein-level analysis across bulk, single-cell, and spatial assays from both long- and short-read sequencing. Its modular design and scverse compatibility allow isoform-resolved analyses to run alongside established gene-level workflows, linking transcript-level screening with structure-aware visualisation and downstream interpretation. Allos is open-source and available at https://github.com/cobioda/allos, with comprehensive documentation and tutorials provided online.