ArticleCommunications biology2023
scPML: pathway-based multi-view learning for cell type annotation from single-cell RNA-seq data.
Article in Communications biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- NeMO Analytics: a compendium of transcriptomic data for the exploration of neocortical development.Nature neuroscience · 2026Article
- scGALA advances graph link prediction-based cell alignment for comprehensive data integration and harmonization.Nature communications · 2025Article
- Self-Supervised Graph Representation Learning for Single-Cell Classification.Interdisciplinary sciences, computational life sciences · 2025Article
- scValue: value-based subsampling of large-scale single-cell transcriptomic data for machine and deep learning tasks.Briefings in bioinformatics · 2025Article
- Decoupled GNNs based on multi-view contrastive learning for scRNA-seq data clustering.Briefings in bioinformatics · 2025Article
- Consensus representation of multiple cell-cell graphs from gene signaling pathways for cell type annotation.BMC biology · 2025Article
- cfDiffusion: diffusion-based efficient generation of high quality scRNA-seq data with classifier-free guidance.Briefings in bioinformatics · 2024Article
- scGAA: a general gated axial-attention model for accurate cell-type annotation of single-cell RNA-seq data.Scientific reports · 2024Article
- scBOL: a universal cell type identification framework for single-cell and spatial transcriptomics data.Briefings in bioinformatics · 2024Article
- scPML: pathway-based multi-view learning for cell type annotation from single-cell RNA-seq data.Communications biology · 2023Article
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7 authors.
Funding
Abstract
Recent developments in single-cell technology have enabled the exploration of cellular heterogeneity at an unprecedented level, providing invaluable insights into various fields, including medicine and disease research. Cell type annotation is an essential step in its omics research. The mainstream approach is to utilize well-annotated single-cell data to supervised learning for cell type annotation of new singlecell data. However, existing methods lack good generalization and robustness in cell annotation tasks, partially due to difficulties in dealing with technical differences between datasets, as well as not considering the heterogeneous associations of genes in regulatory mechanism levels. Here, we propose the scPML model, which utilizes various gene signaling pathway data to partition the genetic features of cells, thus characterizing different interaction maps between cells. Extensive experiments demonstrate that scPML performs better in cell type annotation and detection of unknown cell types from different species, platforms, and tissues.
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Registered trials
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.