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False signals induced by single-cell imputation
Open accessAbstract
Background: Single-cell RNASeq is a powerful tool for measuring gene expression at the resolution of individual cells. A significant challenge in the analysis of this data is the large amount of zero values, representing either missing data or no expression. Several imputation approaches have been proposed to deal with this issue, but since these methods generally rely on structure inherent to the dataset under consideration they may not provide any additional information. Methods: We evaluated the risk of generating false positive or irreproducible results when imputing data with five different methods. We applied each method to a variety of simulated datasets as well as to permuted real single-cell RNASeq datasets and consider the number of false positive gene-gene correlations and differentially expressed genes. Using matched 10X Chromium and Smartseq2 data from the Tabula Muris database we examined the reproducibility of markers before and after imputation. Results: The extent of false-positive signals introduced by imputation varied considerably by method. Data smoothing based methods, MAGIC and knn-smooth, generated a very high number of false-positives in both real and simulated data. Model-based imputation methods typically generated fewer false-positives but this varied greatly depending on how well datasets conformed to the underlying model. Furthermore, only SAVER exhibited reproducibility comparable to unimputed data across matched data. Conclusions: Imputation of single-cell RNASeq data introduces circularity that can generate false-positive results. Thus, statistical tests applied to imputed data should be treated with care. Additional filtering by effect size can reduce but not fully eliminate these effects. Of the methods we considered, SAVER was the least likely to generate false or irreproducible results, thus should be favoured over alternatives if imputation is necessary.
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“Inappropriate use of these published methods or incorrect selection of user settings can result in a bias in data analysis (such as by introducing artificial, non-biological signals) and false conclusions [41, 42].”
From Benchmarking of computational demultiplexing methods for single-nucleus RNA sequencing data (2025) · cites this paper as [41]
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“Indeed, false correlation signals have been reported as a result of expression recovery (Andrews & Hemberg, 2018).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Andrews & Hemberg, 2018
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Metadata
- Authors
- Andrews, T. S., & Hemberg, M.
- Affiliations
- Wellcome Sanger Institute (GB)
- Corresponding author
- Hemberg, Martin
- Venue
- F1000Research
- Publisher
- F1000 Research Ltd
- Published
- 2018-11-02
- Volume
- 7
- Pages
- 1740
- ISSN
- 2046-1402 (ISSN-L)
- OpenAlex
- W4231157025
- Cited by
- 142
- References
- 25
- Citations by year
- 2026: 3 · 2025: 14 · 2024: 14 · 2023: 20 · 2022: 24 · 2021: 20 · 2020: 35 · 2019: 12
- FWCI
- 5.97
- Type
- journal article
- Language
- en
- Update notices
- new_version (2019-03-05)
- Open access
- gold
- doi.org
- Locations
- F1000Research (publishedVersion, cc-by)
- Funders
- Wellcome Trust (SangerInstituteCoreFunding)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- single-cell RNA sequencing, missing data imputation, false positives, zero values, gene-gene correlation, differentially expressed genes, savers, magic, model-based imputation, Tabula Muris
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