scLongTree: an accurate computational tool to infer the longitudinal tree for scDNAseq data
Khan, R.; Mallory, X.
Show abstract
A subclonal tree that depicts the evolution of cancer cells is of interest in understanding how cancer grows, prognosis and treatment of cancer. Longitudinal single-cell DNA sequencing data (scDNA-seq) is the single-cell DNA sequencing data sequenced at different time points. It provides more knowledge of the order of the mutations than the scDNA-seq taken at only one time point, and thus facilitates the inference of the subclonal tree. There is only one existing tool LACE that can infer a subclonal tree based on the longitudinal scDNA-seq, and it is limited in accuracy and scale. We presented scLongTree, a computational tool that can accurately infer the longitudinal subclonal tree based on the longitudinal scDNA-seq. ScLongTree can be scalable to hundreds of mutations, and outper-formed state-of-the-art methods SCITE, SiCloneFit and LACE on a comprehensive simulated dataset. The test on a real dataset SA501 showed that scLongTree can more accurately interpret the progres-sive growth of the tumor than LACE. ScLongTree is freely available on https://github.com/compbio-mallory/sc longitudinal infer.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Large-scale Inference of Cell Lineage Trees and Genotype Calling from Noisy Single-Cell Data Using Efficient Local Search 97%
- TREE-QMC: Improving quartet graph construction for scalable and accurate species tree estimation from gene trees 95%
- Single-cell methylation sequencing data reveal succinct metastatic migration histories and tumor progression models 95%
Similar papers in this journal
- Bayesian non-parametric clustering of single-cell mutation profiles 97%
- Single-cell mutation calling and phylogenetic tree reconstruction with loss and recurrence 96%
- Accurate and Efficient Cell Lineage Tree Inference from Noisy Single Cell Data: the Maximum Likelihood Perfect Phylogeny Approach 96%
Similar papers in this journal
- DelSIEVE: cell phylogeny model of single nucleotide variants and deletions from single-cell DNA sequencing data 97%
- CNETML: Maximum likelihood inference of phylogeny from copy number profiles of spatio-temporal samples 96%
- scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.