Precise identification of cancer cells from allelic imbalances in single cell transcriptomes

Trinh, Mi K; Pacyna, Clarissa N; Kildisiute, Gerda; Thevanesan, Christine; Piapi, Alice; Ambridge, Kirsty; Anderson, Nathaniel D.; Khabirova, Eleonora; Prigmore, Elena; Straathof, Karin; Behjati, Sam; Young, Matthew D.

doi:10.1038/s42003-022-03808-9

Cited by 14 publications

(11 citation statements)

References 18 publications

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“…To further analyze the effects of dataset imbalance in realistic scenarios, we considered the pancreatic ductal adenocarcinoma (PDAC) dataset of 8 batches comprising tumor samples across 8 different biopsies [20]. One major challenge in the analysis of PDAC data is accurate annotation of tumor cells, and being able to separate these from normal non-cancerous epithelial cells [38, 39]. As both acinar and ductal epithelial cells have been proposed as cell of origin candidates in PDAC across numerous studies [40, 41], reliably classifying tumor cells from these normal epithelial cell-types in scRNA-seq data remains a major computational challenge.…”

Section: Resultsmentioning

confidence: 99%

Section: Perturbation Analysis In Pdac Samples Reveals Tumor Compartm...mentioning

confidence: 99%

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The differential impacts of dataset imbalance in single-cell data integration

Maan

Zhang

et al. 2022

Preprint

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Single-cell transcriptomic data measured across distinct samples has led to a surge in computational methods for data integration. Few studies have explicitly examined the common case of cell-type imbalance between datasets to be integrated, and none have characterized its impact on downstream analyses. To address this gap, we developed the Iniquitate pipeline for assessing the stability of single-cell RNA sequencing (scRNA-seq) integration results after perturbing the degree of imbalance between datasets. Through benchmarking 5 state-of-the-art scRNA-seq integration techniques in 1600 perturbed integration scenarios for a multi-sample peripheral blood mononuclear cell (PBMC) cohort, our results indicate that dataset imbalance has significant impacts on downstream analyses and the biological interpretation of integration results. We observed significant variation in clustering, cell-type classification, marker-gene-based annotation, and query-to-reference mapping in imbalanced settings. Two key factors were found to lead to quantitation differences after scRNA-seq integration - the cell-type imbalance within and between samples (relative cell-type support) and the relatedness of cell-types across samples (minimum cell-type center distance). To account for evaluation gaps in imbalanced contexts, we developed novel clustering metrics robust to sample imbalance, including the balanced Adjusted Rand Index (bARI) and balanced Adjusted Mutual Information (bAMI). Our analysis quantifies biologically-relevant effects of dataset imbalance in integration scenarios, and introduces guidelines and novel metrics for integration of disparate datasets. The Iniquitate pipeline and balanced clustering metrics are available at https://github.com/hsmaan/Iniquitate and https://github.com/hsmaan/balanced-clustering, respectively.

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Section: Resultsmentioning

confidence: 99%

Section: Perturbation Analysis In Pdac Samples Reveals Tumor Compartm...mentioning

confidence: 99%

The differential impacts of dataset imbalance in single-cell data integration

Maan

Zhang

et al. 2022

Preprint

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“…Next, we consider single cell transcriptomes from 5 cancers, where the cancer cell transcriptomes have been previously identified [9][10][11][12] . As cancers are derived from a single cell, cancer cells must have the same X-inactivation status.…”

Section: Evaluation Of the Methodsmentioning

confidence: 99%

“…To validate the inactiveXX method, we used three samples with single cell transcriptomics, bulk DNA, and parental DNA to define a gold standard. To do this, we identified heterozygous SNPs in the bulk DNA, then phased them using the DNA from the parents, using the alleleIntegrator package 12 . This defined phased heterozygous SNPs on the X chromosome.…”

Section: Validation Of Inactivexx Methodsmentioning

confidence: 99%

X-inactivation states of single cell transcriptomes reveal cellular phylogenies in human females

Predeus

Arutyunyan

Jardine

et al. 2022

Preprint

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Human females undergo X-inactivation (Xi), whereby one copy of X is randomly inactivated early in development, then propagated through cell division. Because Xi state is inherited, its measurement in populations of cells encodes information about the phylogeny that created them and their relationships to other cells. We present a method, inactiveXX, to determine the Xi state of single cell transcriptomes, and demonstrate its accuracy using cancer and gold standard reference data. We apply inactiveXX to single cell transcriptomes from 190 human females, revealing that Xi in humans likely occurs around the 16 cell blastocyst stage and affects both embryonic and extra-embryonic tissues. We further find significant cell type specific variability in Xi skew, only detectable with cell type specific resolution, with certain cell types exhibiting strong population bottlenecks across tissues and disease state.

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“…Trinh et al (2022) andGao et al (2022) have demonstrated the feasibility of distinguishing normal and cancer cells using BAF signal. Here, we propose a convenient special case of our model that effectively detects the cluster of normal cells.…”

mentioning

confidence: 99%

Bayesian inference for copy number intra-tumoral heterogeneity from single-cell RNA-sequencing data

Qiao,

Kwok,

Qian

et al. 2023

Preprint

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High-resolution molecular characterization of intra-tumoral clonal structure defined by genomic and epigenomic alterations is crucial in understanding the natural history of tumors and advancing cancer treatment strategies. Copy number alterations (CNA) are of notable importance as both drivers and markers of clonal structure that can now be assayed at individual cell resolution. However, specific computational methods are needed for accurate inference of clonal profiles and cell states from sparse and noisy single-cell 'omics data. Here, we develop a new Bayesian model to utilize single-cell RNA sequencing (scRNA-seq) data for automatic analysis of intra-tumoral clonal structure with respect to CNAs, without reliance on prior knowledge. The model clusters cells into sub-tumoral clones while simultaneously identifying CNA events in each clone, jointly modelling input from gene expression and germline single-nucleotide polymorphisms. Unlike previous methods, our approach automatically infers the number of clones present in the tumor. In detailed simulation studies our model frequently achieves very high (>90%) cell clustering accuracy and high (>80%) CN state inference accuracy, even in settings of high variance and sparsity. Overall, our method compares strongly against existing software tools. Application to human metastatic melanoma tumor data demonstrates accurate clustering of tumor and non-tumor cells, and reveals clonal CNA profiles that highlight functional gene expression differences between clones from the same tumor. Our method is implemented in a publicly-available, open-source R package, Chloris.

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Precise identification of cancer cells from allelic imbalances in single cell transcriptomes

Cited by 14 publications

References 18 publications

The differential impacts of dataset imbalance in single-cell data integration

The differential impacts of dataset imbalance in single-cell data integration

X-inactivation states of single cell transcriptomes reveal cellular phylogenies in human females

Bayesian inference for copy number intra-tumoral heterogeneity from single-cell RNA-sequencing data

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