Filtering for highly variable genes and high quality spots improves phylogenetic analysis of cancer spatial transcriptomics Visium data

Abstract

Phylogenetic relationship of cells within tumours can help us to understand how cancer develops in space and time, iden-tify driver mutations and other evolutionary events that enable can-cer growth and spread. Numerous studies have reconstructed phylo-genies from single-cell DNA-seq data. Here we are looking into the problem of phylogenetic analysis of spatially resolved near single-cell RNA-seq data, which is a cost-efficient alternative (or complemen-tary) data source that integrates multiple sources of evolutionary information including point mutations, copy-number changes, and epimutations. Recent attempts to use such data, although promis-ing, raised many methodological challenges. Here, we explored data-preprocessing and modelling approaches for evolutionary analyses of Visium spatial transcriptomics data. We conclude that using only highly variable genes and accounting for heterogeneous RNA capture across tissue-covered spots improves the reconstructed topological relationships and influences estimated branch lengths.