ArticleBioinformatics (Oxford, England)2019
LAmbDA: label ambiguous domain adaptation dataset integration reduces batch effects and improves subtype detection.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed.
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- SCIPAC: quantitative estimation of cell-phenotype associations.Genome biology · 2024Article
- CellSTAR: a comprehensive resource for single-cell transcriptomic annotation.Nucleic acids research · 2024Article
- Single-cell type annotation with deep learning in 265 cell types for humans.Bioinformatics advances · 2024Article
- Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis.bioRxiv : the preprint server for biology · 2023Article
- ProtoCell4P: an explainable prototype-based neural network for patient classification using single-cell RNA-seq.Bioinformatics (Oxford, England) · 2023Article
- Cell type matching across species using protein embeddings and transfer learning.Bioinformatics (Oxford, England) · 2023Article
- TripletCell: a deep metric learning framework for accurate annotation of cell types at the single-cell level.Briefings in bioinformatics · 2023Article
- Reference-free and cost-effective automated cell type annotation with GPT-4 in single-cell RNA-seq analysis.Research square · 2023Article
- deCS: A Tool for Systematic Cell Type Annotations of Single-cell RNA Sequencing Data among Human Tissues.Genomics, proteomics & bioinformatics · 2023Article
- Domain adaptation for supervised integration of scRNA-seq data.Communications biology · 2023Article
- Evaluation of single-cell RNAseq labelling algorithms using cancer datasets.Briefings in bioinformatics · 2023Article
- Single-cell RNA-seq methods to interrogate virus-host interactions.Seminars in immunopathology · 2023Review
- Evaluation of classification in single cell atac-seq data with machine learning methods.BMC bioinformatics · 2022Article
- Detecting retinal neural and stromal cell classes and ganglion cell subtypes based on transcriptome data with deep transfer learning.Bioinformatics (Oxford, England) · 2022Article
- Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease.Genome medicine · 2022Article
- scSorter: assigning cells to known cell types according to marker genes.Genome biology · 2021Article
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9 authors.
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Abstract
motivationRapid advances in single cell RNA sequencing (scRNA-seq) have produced higher-resolution cellular subtypes in multiple tissues and species. Methods are increasingly needed across datasets and species to (i) remove systematic biases, (ii) model multiple datasets with ambiguous labels and (iii) classify cells and map cell type labels. However, most methods only address one of these problems on broad cell types or simulated data using a single model type. It is also important to address higher-resolution cellular subtypes, subtype labels from multiple datasets, models trained on multiple datasets simultaneously and generalizability beyond a single model type.
resultsWe developed a species- and dataset-independent transfer learning framework (LAmbDA) to train models on multiple datasets (even from different species) and applied our framework on simulated, pancreas and brain scRNA-seq experiments. These models mapped corresponding cell types between datasets with inconsistent cell subtype labels while simultaneously reducing batch effects. We achieved high accuracy in labeling cellular subtypes (weighted accuracy simulated 1 datasets: 90%; simulated 2 datasets: 94%; pancreas datasets: 88% and brain datasets: 66%) using LAmbDA Feedforward 1 Layer Neural Network with bagging. This method achieved higher weighted accuracy in labeling cellular subtypes than two other state-of-the-art methods, scmap and CaSTLe in brain (66% versus 60% and 32%). Furthermore, it achieved better performance in correctly predicting ambiguous cellular subtype labels across datasets in 88% of test cases compared with CaSTLe (63%), scmap (50%) and MetaNeighbor (50%). LAmbDA is model- and dataset-independent and generalizable to diverse data types representing an advance in biocomputing. AVAILABILITY AND IMPLEMENTATION: github.com/tsteelejohnson91/LAmbDA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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