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Updated: Mar 22, 2026

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

OncoNEM: inferring tumor evolution from single-cell sequencing data.

Edith M Ross¹, Florian Markowetz²

¹Cancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, UK.

|April 17, 2016

Summary

This summary is machine-generated.

OncoNEM infers cancer

Keywords:

Cancer evolution Phylogenetic tree Single-cell sequencing Tumor evolution Tumor heterogeneity

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Comparative Lesions Analysis Through a Targeted Sequencing Approach

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Published on: November 5, 2019

Area of Science:

Computational Biology and Bioinformatics
Cancer Genomics
Evolutionary Biology

Background:

Single-cell sequencing offers high-resolution insights into cancer's genetic heterogeneity and clonal evolution.
Existing methods for inferring tumor evolution from single-cell data are less developed than those for bulk sequencing.

Purpose of the Study:

To introduce OncoNEM, a novel probabilistic method for reconstructing intra-tumor evolutionary lineage trees.
To enable the inference of tumor evolution directly from single-cell sequencing data, specifically somatic single nucleotide variants.

Main Methods:

Developed OncoNEM, a probabilistic computational method.
Utilized somatic single nucleotide variants from single cells to infer evolutionary relationships.
Assessed OncoNEM's performance through simulation studies and benchmarking against existing methods.

Main Results:

OncoNEM successfully identifies homogeneous cellular subpopulations and infers their genotypes.
The method reconstructs evolutionary lineage trees representing the relationships between subpopulations.
Benchmarking demonstrated OncoNEM's robustness and competitive performance compared to other methods.

Conclusions:

OncoNEM provides a robust computational framework for inferring cancer evolutionary trees from single-cell sequencing data.
The method is applicable to diverse cancer types, as demonstrated in case studies of bladder cancer and essential thrombocythemia.