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

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ARCANE-ROG: Algorithm for reconstruction of cancer evolution from single-cell data using robust graph learning.

Akanksha Farswan¹, Ritu Gupta², Anubha Gupta¹

¹SBILab, Department of ECE, Indraprastha Institute of Information Technology, New Delhi, India.

Journal of Biomedical Informatics

|March 26, 2022

Summary

This summary is machine-generated.

We developed ARCANE-ROG, a graph learning method to reconstruct cancer evolution from single-cell data. This robust approach improves analysis of tumor heterogeneity and treatment-resistant clones, enhancing patient survival.

Keywords:

Clonal evolution Clustering Denoising Robust PCA Single-cell DNA Single-cell genomics

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Reusable Single Cell for Iterative Epigenomic Analyses

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Area of Science:

Genomics
Computational Biology
Cancer Research

Background:

Tumor heterogeneity, driven by diverse cancer cell clones, hinders effective cancer treatment and patient survival.
Single-cell sequencing offers insights into cellular variability but faces challenges like data noise and large size, increasing computational costs.
Accurate inference of clonal evolution is crucial for understanding and overcoming treatment resistance.

Purpose of the Study:

To introduce ARCANE-ROG, a novel graph learning-based algorithm for robust inference of clonal evolution from single-cell data.
To address computational challenges and improve the analysis of noisy and incomplete single-cell sequencing data.
To enhance the understanding of intra-tumor heterogeneity and identify treatment-resistant clones.

Main Methods:

ARCANE-ROG employs a joint framework for denoising and data imputation to handle noisy single-cell matrices while learning an adjacency graph.
The Leiden method is utilized to identify an optimal number of clusters within the processed data.
Clonal evolution trees are inferred using a minimum spanning tree algorithm.

Main Results:

ARCANE-ROG demonstrated significantly superior performance compared to the state-of-the-art method, RobustClone, across simulated and real datasets.
Key performance metrics, including reconstruction error, False Positive to False Negative (FPFN) ratio, tree distance error, and V-measure, showed significant improvement (p < 0.05).
The method enhances cluster assignment accuracy and the inference of clonal hierarchies.

Conclusions:

ARCANE-ROG provides a robust and computationally efficient solution for inferring cancer clonal evolution from single-cell data.
The proposed method offers significant improvements over existing approaches, aiding in the analysis of tumor heterogeneity.
Enhanced understanding of clonal evolution can lead to better therapeutic strategies for cancer patients.