XClone: detection of allele-specific subclonal copy number variations from single-cell transcriptomic data

Huang, R.; Huang, X.; Tong, Y.; Yan, H. Y. N.; Leung, S. Y.; Stegle, O.; Huang, Y.

doi:10.1101/2023.04.03.535352

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bioRxiv · 10.1101/2023.04.03.535352

XClone: detection of allele-specific subclonal copy number variations from single-cell transcriptomic data

Huang, R.·Huang, X.·Tong, Y.·Yan, H. Y. N.·Leung, S. Y.·Stegle, O.·Huang, Y.

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Abstract

Somatic copy number alterations (CNAs) are major mutations that contribute to the development and progression of various cancers. Despite a few computational methods proposed to detect CNAs from single-cell transcriptomic data, the technical sparsity of such data makes it challenging to identify allele-specific CNAs, particularly in complex clonal structures. In this study, we present a statistical method, XClone, that strengthens the signals of read depth and allelic imbalance by effective smoothing on cell neighborhood and gene coordinate graphs to detect haplotype-aware CNAs from scRNA-seq data. By applying XClone to multiple datasets with challenging compositions, we demonstrated its ability to robustly detect different types of allele-specific CNAs and potentially indicate whole genome duplication, therefore enabling the discovery of corresponding subclones and the dissection of their phenotypic impacts.

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BibTeX RIS

Huang, R., Huang, X., Tong, Y., Yan, H. Y. N., Leung, S. Y., Stegle, O., Huang, Y.. 2023-04-05. XClone: detection of allele-specific subclonal copy number variations from single-cell transcriptomic data. https://doi.org/10.1101/2023.04.03.535352

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