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Cited by 9Sepsis Diagnosis and TreatmentSingle-cell and spatial transcriptomicsGene expression and cancer classification

PAGE-based transfer learning from single-cell to bulk sequencing enhances model generalization for sepsis diagnosis

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Jin, Nana, Nan, Chuanchuan, Li, Wanyang, Lin, Peijing, Xin, Yu, Wang, Jun, Chen, Yuelong, Wang, Yuanhao +5 more·2024·Briefings in Bioinformatics
DOI 10.1093/bib/bbae661PMC PMC11962595
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Abstract

Sepsis, caused by infections, sparks a dangerous bodily response. The transcriptional expression patterns of host responses aid in the diagnosis of sepsis, but the challenge lies in their limited generalization capabilities. To facilitate sepsis diagnosis, we present an updated version of single-cell Pair-wise Analysis of Gene Expression (scPAGE) using transfer learning method, scPAGE2, dedicated to data fusion between single-cell and bulk transcriptome. Compared to scPAGE, the upgrade to scPAGE2 featured ameliorated Differentially Expressed Gene Pairs (DEPs) for pretraining a model in single-cell transcriptome and retrained it using bulk transcriptome data to construct a sepsis diagnostic model, which effectively transferred cell-layer information from single-cell to bulk transcriptome. Seven datasets across three transcriptome platforms and fluorescence-activated cell sorting (FACS) were used for performance validation. The model involved four DEPs, showing robust performance across next-generation sequencing and microarray platforms, surpassing state-of-the-art models with an average AUROC of 0.947 and an average AUPRC of 0.987. Analysis of scRNA-seq data reveals higher cell proportions with JAM3-PIK3AP1 expression in sepsis monocytes, decreased ARG1-CCR7 in B and T cells. Elevated IRF6-HP in sepsis monocytes confirmed by both scRNA-seq and an independent cohort using FACS. Both the superior performance of the model and the in vitro validation of IRF6-HP in monocytes emphasize that scPAGE2 is effective and robust in the construction of sepsis diagnostic model. We additionally applied scPAGE2 to acute myeloid leukemia and demonstrated its superior classification performance. Overall, we provided a strategy to improve the generalizability of classification model that can be adapted to a broad range of clinical prediction scenarios.

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  • Omics data were absent from the multimodal studies included in this review.

    “although omics data have been used in unimodal AI models for the ICU [74–76], they were absent from the multimodal studies included in this review.”

    From Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review (2026) · cites this paper as [75]

  • Recent omics-based diagnostic models illustrate both the promise of transferring single-cell or molecular signatures across bulk and single-cell sequencing data and the dependence of such models on platform compatibility, normalization, and cohort-specific processing choices.

    “Recent omics-based diagnostic models illustrate both the promise of transferring single-cell or molecular signatures across bulk and single-cell sequencing data and the dependence of such models on platform compatibility, normalization, and cohort-specific processing choices [45–47].”

    From Validation is not enough: Longitudinal evidence of post-deployment fragility in clinical AI systems (2026) · cites this paper as [47]

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Authors
Jin, N., Nan, C., Li, W., Lin, P., Xin, Y., Wang, J., Chen, Y., Wang, Y., Yu, K., Wang, C., Chen, C., Geng, Q., & Cheng, L.
Affiliations
  • Jinan University (CN)
  • Harbin Medical University (CN)
  • University of Copenhagen (DK)
  • Chinese University of Hong Kong (HK)
Corresponding author
  • Chen, Chunbo
  • Geng, Qingshan
  • Cheng, Lixin
ORCID
Venue
Briefings in Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2024-11-22
Volume
26
Issue
1
ISSN
  • 1467-5463 (ISSN-L)
  • 1477-4054
DOI
10.1093/bib/bbae661
PMC
PMC11962595
OpenAlex
W4405687099
Cited by
9
References
47
Citations by year
2026: 6 · 2025: 3
FWCI
2.75
Type
journal article
Language
en
Open access
Licence
Locations
  • Briefings in Bioinformatics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
Funders
  • Shenzhen Science and Technology Research and Development Fund (JCYJ20210324114000001)
  • Shenzhen Science and Technology Program (RKX20220705152810024, JCYJ20220530152409020)
  • Shenzhen Medical Research Fund (A2303033)
  • National Natural Science Foundation of China (32370711)
Topics
  • Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
  • 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
Keywords
sepsis diagnosis, data fusion, host immune response, monocytes, model generalization
MeSH terms
Machine Learning, Humans, Sepsis, Gene Expression Profiling, Single-Cell Analysis, Transcriptome

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