Kulman, E. | Search bioRxiv

bioRxiv2025

Phylogenetic Inference of Copy Number Alterations and Single Nucleotide Variants from Longitudinal Single-Cell Sequencing

Longitudinal phylogenetic reconstruction reveals how cancers evolve over time and respond to treatments. Advances in targeted single-cell sequencing, combined with longitudinal sampling, now enable detailed longitudinal tracking of single nucleotide variants (SNVs) and copy number alterations (CNAs) at single-cell resolution. Here, we introduce LoPhy, the first method designed to reconstruct the evolution of SNVs and CNAs from these new longitudinal single-cell data. LoPhy is a sequential tree-building algorithm that reconstructs longitudinally-consistent phylogenies of SNVs and CNAs by maximizing a new factorized tree reconstruction objective. The algorithm incrementally grows a clone tree, adding SNVs and CNAs in the order they are observed across time points. Applied to a cohort of 15 acute myeloid leukemias (AMLs) and 4 TP53-mutated AMLs, LoPhy produced phylogenies that are biologically and temporally consistent with clinical observations, with many inferred CNAs validated by orthogonal bulk sequencing from the same cancer. These reconstructions highlight the role of CNAs in disease progression and resistance, revealing that AML clones selected after therapy are often defined by both large-scale CNAs and SNVs. More broadly, LoPhy can help uncover how SNVs and CNAs jointly shape the evolutionary trajectories of individual cancers at single-cell resolution. The LoPhy source code is available under a CC-BY-ND license at https://github.com/ethanumn/LoPhy. Author summaryLongitudinal single-cell DNA sequencing is increasingly used to track somatic mutations in cancer, including single nucleotide variants (SNVs) and copy number alterations (CNAs). Phylogenetic analysis of such data can reveal the mutations that characterize key subpopulations of cancerous cells and how they evolve over time. However, no existing methods are designed to reconstruct the joint evolution of SNVs and CNAs from longitudinal single-cell data. To address this gap, we developed LoPhy, an algorithm that infers a phylogenetic tree from longitudinal single-cell data to capture the joint evolutionary history of SNVs and CNAs in an individual cancer. We applied LoPhy to simulated datasets and 19 acute myeloid leukemias (AMLs). LoPhys reconstructions reveal that AML subpopulations selected after treatment are frequently defined by both SNVs and large-scale CNAs--highlighting that joint longitudinal modeling of SNVs and CNAs is crucial for understanding disease progression and therapeutic resistance.

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bioRxiv2024

BiGCN: Leveraging Cell and Gene Similarities for Single-cell Transcriptome Imputation with Bi-Graph Convolutional Networks

MotivationRNA profiling at the single-cell level is essential for characterizing the molecular activities and functions of individual cells. The current technical limitations of single-cell RNA sequencing (scRNA-seq) technologies can lead to a phenomenon known as "dropout", where a significant portion of gene expression is not captured. Dropout is particularly prominent in genes with low or sparse expression, greatly impacting the reliability and interpretability of scRNA-seq data. Consequently, various techniques have been developed to estimate missing gene expression using imputation, often by either modeling similarities in gene expression among cells or using gene co-expression, but rarely both. ResultsIn this study, we introduce a Bi-Graph Convolutional Network (BiGCN), a deep learning method that leverages both cell similarities and gene co-expression to capture cell-type-specific gene co-expression patterns for imputing scRNA-seq data. BiGCN constructs both a cell similarity graph and a gene co-expression graph, and employs them for convolutional smoothing in a dual two-layer Graph Convolutional Networks (GCNs). The embeddings from the two GCNs can subsequently be combined to facilitate the final imputation. BiGCN demonstrates superior performance compared to state-of-the-art imputation methods on both real and simulated scRNA-seq data. Additionally, BiGCN outperforms existing methods when tasked with clustering cells into cell types. We also perform a novel validation using a PBMC scRNA-seq dataset, and this experiment supports that BiGCNs imputations are more realistic than competing imputation methods. In both the imputation and the cluster tasks, BiGCN consistently outperformed two variants of BiGCN that solely relied on either the gene co-expression graph or cell similarity graph. This indicates that the two graphs offer complimentary information for imputation and cell clustering, underscoring the importance of incorporating both types of information. Code Availabilityhttps://github.com/inoue0426/scBiGCN. Contactkuang@umn.edu

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