Evidence map›Paper›PMID 36163550›Full record

ArticleNature biotechnology2023

Haplotype-aware analysis of somatic copy number variations from single-cell transcriptomes.

Teng Gao, Ruslan Soldatov, Hirak Sarkar, Adam Kurkiewicz, Evan Biederstedt, Po-Ru Loh, Peter V Kharchenko

Abstract read
In one paragraph

Article in Nature biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 117 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
117citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

117 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  9. Pervasive Somatic Mutations inbioRxiv : the preprint server for biology · 2026
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57 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Teng GaoDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0002-0196-689X
Ruslan SoldatovDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Hirak SarkarDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Adam KurkiewiczDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Evan BiederstedtDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Po-Ru LohDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0001-5542-9064
Peter V KharchenkoDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. peter.kharchenko@post.harvard.edu.ORCID http://orcid.org/0000-0002-6036-5875

Funding

Revealing somatic genome alterations and their clinical sequelae: Ultrasensitive computational detection of mosaic structural variantsDP2ES030554 · NIEHS · BRIGHAM AND WOMEN'S HOSPITAL · PI LOH, PO-RU · 2018 to 2018
$2.7M
NIEHS NIH HHS DP2 ES030554
6 · The paper itself

Abstract

Genome instability and aberrant alterations of transcriptional programs both play important roles in cancer. Single-cell RNA sequencing (scRNA-seq) has the potential to investigate both genetic and nongenetic sources of tumor heterogeneity in a single assay. Here we present a computational method, Numbat, that integrates haplotype information obtained from population-based phasing with allele and expression signals to enhance detection of copy number variations from scRNA-seq. Numbat exploits the evolutionary relationships between subclones to iteratively infer single-cell copy number profiles and tumor clonal phylogeny. Analysis of 22 tumor samples, including multiple myeloma, gastric, breast and thyroid cancers, shows that Numbat can reconstruct the tumor copy number profile and precisely identify malignant cells in the tumor microenvironment. We identify genetic subpopulations with transcriptional signatures relevant to tumor progression and therapy resistance. Numbat requires neither sample-matched DNA data nor a priori genotyping, and is applicable to a wide range of experimental settings and cancer types.

Indexed as

Multiple MyelomaTranscriptomeDNA Copy Number VariationsHaplotypesHumansPhylogenySingle-Cell AnalysisTumor Microenvironment

Identifiers

PMID36163550
PMCPMC10289836

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.