GRMT: Generative Reconstruction of Mutation Tree From Scratch Using Single-Cell Sequencing Data

Abstract

Single-cell sequencing (SCS) now promises the landscape of genetic diversity at single cell level, and is particularly useful to reconstruct the evolutionary history of tumor. There are multiple types of noise that make the SCS data notoriously error-prone, and significantly complicate tumor tree reconstruction. Existing methods for tumor phylogeny estimation suffer from either high computational intensity or low-resolution indication of clonal architecture, giving a necessity of developing new methods for efficient and accurate reconstruction of tumor trees. We introduce GRMT (Generative Reconstruction of Mutation Tree from scratch), a method for inferring tumor mutation tree from SCS data. GRMT exploits the k-Dollo parsimony model to allow each mutation to be gained once and lost at most k times. Under this constraint on mutation evolution, GRMT searches for mutation tree structures from a perspective of tree generation from scratch, and implements it to an iterative process that gradually increases the tree size by introducing a new mutation per time until a complete tree structure that contains all mutations is obtained. This enables GRMT to efficiently recover the chronological order of mutations and scale well to large datasets. Extensive evaluations on simulated and real datasets suggest GRMT outperforms the state-of-the-arts in multiple performance metrics. The GRMT software is freely available at https://github.com/qasimyu/grmt.

Editorial: Unsupervised Learning Models for Unlabeled Genomic, Transcriptomic & Proteomic Data

“…Specifically, Xie et al proposes a standard deviation and cosine similarity based unsupervised feature selection algorithms, which is capable of conducting gene selection for stable biomarkers of disease such as cancer through genomic data (Xie J. et al) For transcriptomic data analysis, there are two papers contributing to RNA data research as the roles of bioinformatics tools. One research in this Research Topic is focusing on in single-cell RNA sequencing (Yu et al, 2021), which aims to overcome the zero-inflated data caused by dropout events (Zhao et al), where Zhao et al proposes a dimensionality reduction approach on single-cell RNA sequencing data, which is based on a hierarchical autoencoder consisting of a deep count autoencoder for denoising and a graph autoencoder for dimensional reducing. Meanwhile, for long intergenic noncoding RNA (lincRNA) analysis, Lin and Ma proposes a nonnegative matrix factorization approach with co-regularization to predict disease-lincRNA associations (Lin and Ma), which integrates four types of information associated to lincRNA.…”

SCClone: Accurate Clustering of Tumor Single-Cell DNA Sequencing Data

“…So far, an arsenal of computational methods (Jahn et al, 2016;Zafar et al, 2017;El-Kebir, 2018;Chen et al, 2020;Myers et al, 2020;Yu et al, 2021) has been developed to reconstruct tumor phylogeny from single nucleotide variation (SNV) data of single cells. Typically, three popular evolutionary models, that is, the infinite sites model (ISM), the finite site model (FSM), and the Dollo parsimony model, are employed in these methods.…”

“…PhISCS-BnB (Sadeqi Azer et al, 2020) delivers perfect phylogeny using a branch and bound algorithm. Recently, GRMT (Yu et al, 2021) is proposed to reconstruct the mutation tree with a generative model. There are some methods that exploit additional data to improve the inference accuracy.…”

Assessing the Performance of Methods for Cell Clustering from Single-cell DNA Sequencing Data

“…In general, if a tool infers the evolutionary history of cancer cells, it can also characterize the clonality of the cells and infer the genotype of the mutation sites. Those tools that can jointly infer the evolutionary history and characterize the clonality of cancer cells include but are not limited to SCITE [27], OncoNEM [28], Sifit [29], SiCloneFit [30], SASC [31], SPhyR [32], and GRMT [33].…”