ArticleScientific reports2019
Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- NeMO Analytics: a compendium of transcriptomic data for the exploration of neocortical development.Nature neuroscience · 2026Article
- TransST: transfer learning embedded spatial factor modeling of spatial transcriptomics data.BMC bioinformatics · 2025Article
- Optical sorting: past, present and future.Light, science & applications · 2025Review
- PrCRS: a prediction model of severe CRS in CAR-T therapy based on transfer learning.BMC bioinformatics · 2024Article
- Towards a more general drug target interaction prediction model using transfer learning.Procedia computer science · 2023Article
- Transfer learning for genotype-phenotype prediction using deep learning models.BMC bioinformatics · 2022Article
- Challenges in translational machine learning.Human genetics · 2022Review
- Single Cell Self-Paced Clustering with Transcriptome Sequencing Data.International journal of molecular sciences · 2022Article
- Transfer learning compensates limited data, batch effects and technological heterogeneity in single-cell sequencing.NAR genomics and bioinformatics · 2021Article
- Biologically relevant transfer learning improves transcription factor binding prediction.Genome biology · 2021Article
- Transfer learning between preclinical models and human tumors identifies a conserved NK cell activation signature in anti-CTLA-4 responsive tumors.Genome medicine · 2021Article
- Binding affinity prediction for binary drug-target interactions using semi-supervised transfer learning.Journal of computer-aided molecular design · 2021Article
- Contrastive self-supervised clustering of scRNA-seq data.BMC bioinformatics · 2021Article
- Single-Cell Techniques and Deep Learning in Predicting Drug Response.Trends in pharmacological sciences · 2020Review
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7 authors.
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Abstract
In many research areas scientists are interested in clustering objects within small datasets while making use of prior knowledge from large reference datasets. We propose a method to apply the machine learning concept of transfer learning to unsupervised clustering problems and show its effectiveness in the field of single-cell RNA sequencing (scRNA-Seq). The goal of scRNA-Seq experiments is often the definition and cataloguing of cell types from the transcriptional output of individual cells. To improve the clustering of small disease- or tissue-specific datasets, for which the identification of rare cell types is often problematic, we propose a transfer learning method to utilize large and well-annotated reference datasets, such as those produced by the Human Cell Atlas. Our approach modifies the dataset of interest while incorporating key information from the larger reference dataset via Non-negative Matrix Factorization (NMF). The modified dataset is subsequently provided to a clustering algorithm. We empirically evaluate the benefits of our approach on simulated scRNA-Seq data as well as on publicly available datasets. Finally, we present results for the analysis of a recently published small dataset and find improved clustering when transferring knowledge from a large reference dataset. Implementations of the method are available at https://github.com/nicococo/scRNA.
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