Evidence map›Paper›PMID 33338399›Full record

ArticleCell systems2021

Benchmarking Computational Doublet-Detection Methods for Single-Cell RNA Sequencing Data.

Nan Miles Xi, Jingyi Jessica Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 130 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
130citing papers in PubMed, 2 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

130 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  10. The roles and heterogeneity of CD8International journal of molecular medicine · 2026
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70 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

2 authors.

Nan Miles XiDepartment of Statistics, University of California, Los Angeles, CA 90095-1554, USA.
Jingyi Jessica LiDepartment of Statistics, University of California, Los Angeles, CA 90095-1554, USA; Department of Human Genetics, University of California, Los Angeles, CA 90095-7088, USA; Department of Computational Medicine, University of California, Los Angeles, CA 90095-1766, USA. Electronic address: jli@stat.ucla.edu.

Funding

Robust Identification and accurate quantification of RNA transcripts on a system wide scaleR01GM120507 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LI, JINGYI JESSICA · 2016 to 2021
$1.7M
NIGMS NIH HHS R01 GM120507
6 · The paper itself

Abstract

In single-cell RNA sequencing (scRNA-seq), doublets form when two cells are encapsulated into one reaction volume. The existence of doublets, which appear to be-but are not-real cells, is a key confounder in scRNA-seq data analysis. Computational methods have been developed to detect doublets in scRNA-seq data; however, the scRNA-seq field lacks a comprehensive benchmarking of these methods, making it difficult for researchers to choose an appropriate method for specific analyses. We conducted a systematic benchmark study of nine cutting-edge computational doublet-detection methods. Our study included 16 real datasets, which contained experimentally annotated doublets, and 112 realistic synthetic datasets. We compared doublet-detection methods regarding detection accuracy under various experimental settings, impacts on downstream analyses, and computational efficiencies. Our results show that existing methods exhibited diverse performance and distinct advantages in different aspects. Overall, the DoubletFinder method has the best detection accuracy, and the cxds method has the highest computational efficiency. A record of this paper's transparent peer review process is included in the Supplemental Information.

Indexed as

BenchmarkingHumansRNA-SeqSingle-Cell Analysiscell clusteringdifferential gene expressiondoublet detectionparallel computingreproducibilityscRNA-seqsoftware implementationtrajectory inference

Identifiers

PMID33338399
PMCPMC7897250

What OpenQuestion holds

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Registered trials

None linked

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.