ArticleBMC bioinformatics2022
CAISC: A software to integrate copy number variations and single nucleotide mutations for genetic heterogeneity profiling and subclone detection by single-cell RNA sequencing.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Integrated multi-omics analysis and machine learning refine molecular subtypes and prognosis in hepatocellular carcinoma through O-linked glycosylation genes.Functional & integrative genomics · 2025Article
- De novo detection of somatic variants in high-quality long-read single-cell RNA sequencing data.Genome research · 2025Article
- De novo detection of somatic variants in high-quality long-read single-cell RNA sequencing data.bioRxiv : the preprint server for biology · 2024Article
- Phertilizer: Growing a clonal tree from ultra-low coverage single-cell DNA sequencing of tumors.PLoS computational biology · 2023Article
- New perspectives on biology, disease progression, and therapy response of head and neck cancer gained from single cell RNA sequencing and spatial transcriptomics.Oncology research · 2023Review
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundAlthough both copy number variations (CNVs) and single nucleotide variations (SNVs) detected by single-cell RNA sequencing (scRNA-seq) are used to study intratumor heterogeneity and detect clonal groups, a software that integrates these two types of data in the same cells is unavailable.
resultsWe developed Clonal Architecture with Integration of SNV and CNV (CAISC), an R package for scRNA-seq data analysis that clusters single cells into distinct subclones by integrating CNV and SNV genotype matrices using an entropy weighted approach. The performance of CAISC was tested on simulation data and four real datasets, which confirmed its high accuracy in sub-clonal identification and assignment, including subclones which cannot be identified using one type of data alone. Furthermore, integration of SNV and CNV allowed for accurate examination of expression changes between subclones, as demonstrated by the results from trisomy 8 clones of the myelodysplastic syndromes (MDS) dataset.
conclusionsCAISC is a powerful tool for integration of CNV and SNV data from scRNA-seq to identify clonal clusters with better accuracy than obtained from a single type of data. CAISC allows users to interactively examine clonal assignments.
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