Accurate Identification of Locally Aneuploid Cells by Incorporating Cytogenetic Information in Single Cell Data Analysis

Authors

Ziyi Li
Ruoxing Li
Irene Ganan-Gomez
Hussein A Abbas
Guillermo Garcia-Manero
Wei Sun

Publication Date

10-15-2024

Journal

Scientific Reports

Abstract

Single-cell RNA sequencing is a powerful tool to investigate the cellular makeup of tumor samples. However, due to the sparse data and the complex tumor microenvironment, it can be challenging to identify neoplastic cells that play important roles in tumor growth and disease progression. This is especially relevant for blood cancers, where neoplastic cells may be highly similar to normal cells. To address this challenge, we have developed partCNV and partCNVH, two methods for rapid and accurate detection of aneuploid cells with local copy number deletion or amplification. PartCNV uses an expectation-maximization (EM) algorithm with mixtures of Poisson distributions and incorporates cytogenetic information to guide the classification. PartCNVH further improves partCNV by integrating a hidden Markov model for feature selection. We have thoroughly evaluated the performance of partCNV and partCNVH through simulation studies and real data analysis using three scRNA-seq datasets from blood cancer patients. Our results show that partCNV and partCNVH have favorable accuracy and provide more interpretable results compared to existing methods. In the real data analysis, we have identified multiple biological processes involved in the oncogenesis of myelodysplastic syndromes and acute myeloid leukemia.

Keywords

Humans, Single-Cell Analysis, Aneuploidy, Algorithms, Cytogenetic Analysis, Markov Chains, Sequence Analysis, RNA, Leukemia, Myeloid, Acute, Data Analysis

DOI

10.1038/s41598-024-75226-2

PMID

39406835

PMCID

PMC11480446

PubMedCentral® Posted Date

10-15-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes