Robust analysis of allele-specific copy number alterations from scRNA-seq data with XClone

Rongting Huang, Xianjie Huang, Yin Tong, Helen Y. N. Yan, Suet Yi Leung, Oliver Stegle and Yuanhua Huang ()
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Rongting Huang: The University of Hong Kong
Xianjie Huang: The University of Hong Kong
Yin Tong: The University of Hong Kong, Queen Mary Hospital
Helen Y. N. Yan: The University of Hong Kong, Queen Mary Hospital
Suet Yi Leung: The University of Hong Kong, Queen Mary Hospital
Oliver Stegle: German Cancer Research Center (DKFZ)
Yuanhua Huang: The University of Hong Kong

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-51026-0

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