CluVar: clustering of variants using autoencoder for inferring cancer subclones from single cell RNA sequencing data

Abstract

Tumor tissues are composed of malignant subclones with diverse genetic profiles. Reconstructing the evolutionary trajectory of these subclones is crucial for understanding how tumors acquire malignant traits. However, current approaches to subclonal tree reconstruction are limited either by their reliance on single-cell DNA sequencing (scDNA-seq) that involve a small number of cells and thus yield low-resolution results, or using single-cell RNA sequencing (scRNA-seq) data, which despite including larger cell populations, remain susceptible to bias from high dropout rates and technical noise. Here, we introduce CluVar, an autoencoder-based framework for inferring the phylogeny of cancer subclones from scRNA-seq data using mutation profile analysis. To address the extensive missing variant information inherent in scRNA-seq datasets, CluVar incorporates a customized loss function and multiple hidden layers optimized for clustering. CluVar demonstrated superior performance in reconstructing phylogenetic trees of cancer subclones under a range of erroneous conditions. When applied to cancer scRNA-seq data, the phylogenetic tree predicted using CluVar aligned well with the transcriptomic profiles. These findings highlight its utility for tracing evolutionary trajectories and identifying novel variants associated with cancer progression.