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Cited by 31Single-cell and spatial transcriptomicsGene expression and cancer classificationBioinformatics and Genomic Networks

Phenotype prediction from single-cell RNA-seq data using attention-based neural networks

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Mao, Yuzhen, Lin, Yen-Yi, Wong, Nelson K Y, Volik, Stanislav, Sar, Funda, Collins, Colin, Ester, Martin·2024·Bioinformatics
DOI 10.1093/bioinformatics/btae067PMC PMC10902676

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Abstract

Motivation: A patient’s disease phenotype can be driven and determined by specific groups of cells whose marker genes are either unknown or can only be detected at late-stage using conventional bulk assays such as RNA-Seq technology. Recent advances in single-cell RNA sequencing (scRNA-seq) enable gene expression profiling in cell-level resolution, and therefore have the potential to identify those cells driving the disease phenotype even while the number of these cells is small. However, most existing methods rely heavily on accurate cell type detection, and the number of available annotated samples is usually too small for training deep learning predictive models. Results: Here, we propose the method ScRAT for phenotype prediction using scRNA-seq data. To train ScRAT with a limited number of samples of different phenotypes, such as coronavirus disease (COVID) and non-COVID, ScRAT first applies a mixup module to increase the number of training samples. A multi-head attention mechanism is employed to learn the most informative cells for each phenotype without relying on a given cell type annotation. Using three public COVID datasets, we show that ScRAT outperforms other phenotype prediction methods. The performance edge of ScRAT over its competitors increases as the number of training samples decreases, indicating the efficacy of our sample mixup. Critical cell types detected based on high-attention cells also support novel findings in the original papers and the recent literature. This suggests that ScRAT overcomes the challenge of missing marker genes and limited sample number with great potential revealing novel molecular mechanisms and/or therapies. Availability and implementation: The code of our proposed method ScRAT is published at https://github.com/yuzhenmao/ScRAT.

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  • ScRAT was developed to bridge scRNA-seq data with clinical phenotypes, facilitating phenotype-specific cell-type identification and high-resolution disease classification.

    “To address this, an attention-based neural network, ScRAT, was developed to bridge scRNA-seq data with clinical phenotypes [137], facilitating phenotype-specific cell-type identification and high-resolution disease classification.”

    From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [137]

    The Introduction and Conclusion describe ScRAT as an attention-based method that links scRNA-seq cells to phenotypes and identifies phenotype-relevant cell types. However, the Experimental setup (§5.1.1) evaluates binary COVID-19 diagnosis, severity, and stage tasks; it does not establish the broader claim of high-resolution disease classification. (LLM, conf=0.84)

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Research works printed but not linked: 2

  1. Carratino L, Cissé M, Jenatton R, Vert JP. On mixup regularization. J Mach Learn Res 2022;23: 14632– 62.
  2. Dosovitskiy A, Beyer L, Kolesnikov A et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929. 2020 Oct 22. · arXiv

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Authors
Mao, Y., Lin, Y.-Y., Wong, N. K. Y., Volik, S., Sar, F., Collins, C., & Ester, M.
Affiliations
  • Simon Fraser University (CA)
  • University of British Columbia (CA)
  • BC Cancer Agency (CA)
  • Vancouver Prostate Centre (CA)
Corresponding author
  • Collins, Colin
  • Ester, Martin
ORCID
Venue
Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2024-02-01
Volume
40
Issue
2
ISSN
  • 1367-4803 (ISSN-L)
  • 1367-4811
DOI
10.1093/bioinformatics/btae067
PMC
PMC10902676
OpenAlex
W4392103664
Cited by
31
References
48
Citations by year
2026: 12 · 2025: 14 · 2024: 5
FWCI
4.22
Type
journal article
Language
en
Open access
Licence
Locations
  • Bioinformatics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • NSERC Discovery Grant “Transfer
  • Canadian Institutes of Health Research (PJT-175238)
  • Cancer Research Society (840847)
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 › Bioinformatics and Genomic Networks
Keywords
phenotype prediction, single-cell RNA sequencing, attention-based neural networks, multi-head attention, mixup augmentation, limited training data, COVID-19 pandemic, marker genes
MeSH terms
RNA-Seq, Single-Cell Gene Expression Analysis, Humans, Phenotype, Cluster Analysis, Neural Networks, Computer, Sequence Analysis, RNA, Gene Expression Profiling, Single-Cell Analysis

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