ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis
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Abstract
Single-cell RNA-seq data allows insight into normal cellular function and various disease states through molecular characterization of gene expression on the single cell level. Dimensionality reduction of such high-dimensional data sets is essential for visualization and analysis, but single-cell RNA-seq data are challenging for classical dimensionality-reduction methods because of the prevalence of dropout events, which lead to zero-inflated data. Here, we develop a dimensionality-reduction method, (Z)ero (I)nflated (F)actor (A)nalysis (ZIFA), which explicitly models the dropout characteristics, and show that it improves modeling accuracy on simulated and biological data sets.
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Both scGPT and Geneformer produce cell embeddings intended to project potentially noisy gene expression measurements to a more biologically relevant latent space, and then these cell embeddings are fine-tuned for cell type classification.
“Both scGPT and Geneformer produce cell embeddings intended to project potentially noisy gene expression measurements to a more biologically relevant latent space [18–21], and then these cell embeddings are fine-tuned for cell type classification.”
From Zero-shot evaluation reveals limitations of single-cell foundation models (2025) · cites this paper as [18]
The Introduction describes projecting high-dimensional single-cell gene-expression measurements into a low-dimensional latent space to reveal patterns, and discusses noise from dropout events. It does not discuss scGPT or Geneformer, so it cannot establish that those models produce embeddings for this purpose. The fine-tuning clause follows the citation and is not assessed as attributed to this source. (LLM, conf=0.84)
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Cites
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“To improve its run time we reduced the number of genes to 1356 (selecting only genes that have non-zero values in 90% of samples) but the performance of ZIFA on this data was not good (much lower than the results presented in the comparison table) and so we left it out.”
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“These embeddings are intended to project potentially noisy gene expression measurements to a more biologically relevant latent space and to thus improve our ability to resolve cell types, consistent with previous machine learning methods in this field (including scVI) [30, 31, 24, 32].”
ResultMissing Data and Technical Variability in Single-Cell RNA-Sequencing Experiments
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- Authors
- Pierson, E., & Yau, C.
- Affiliations
- University of Oxford (GB)
- Centre for Human Genetics (GB)
- Corresponding author
- Yau, Christopher
- Venue
- Genome Biology
- Published
- 2015-11-02
- Volume
- 16
- Issue
- 1
- Pages
- 241-241
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC4630968
- OpenAlex
- W2152692691
- Cited by
- 577
- References
- 7
- Citations by year
- 2026: 12 · 2025: 29 · 2024: 37 · 2023: 57 · 2022: 61 · 2021: 63 · 2020: 82 · 2019: 90 · 2018: 73 · 2017: 48 · 2016: 23 · 2014: 2
- FWCI
- 20.85
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Genome biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (submittedVersion, other-oa)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion, cc-by)
- Funders
- Medical Research Council (MR/L001411/1)
- Wellcome Trust (090532/Z/09/Z)
- Li Ka Shing Foundation
- 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 › Gene Regulatory Network Analysis
- Keywords
- dimensionality reduction, zero-inflated data, dropout events, single-cell RNA sequencing, gene expression, factor analysis, high-dimensional data visualization
- MeSH terms
- Software, Models, Statistical, Sequence Analysis, RNA, Gene Expression Profiling, Principal Component Analysis, Single-Cell Analysis
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