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MapCell: Learning a Comparative Cell Type Distance Metric With Siamese Neural Nets With Applications Toward Cell-Type
1Molecular Engineering Laboratory, Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, Singapore.
Frontiers in Cell and Developmental Biology
|November 19, 2021
Summary
MapCell uses deep metric learning with Siamese Neural Networks (SNNs) to identify similar cells across single-cell RNA sequencing (scRNA-seq) experiments. This approach enables accurate cell type annotation transfer, even across diverse datasets and species.
Area of Science:
- Computational Biology
- Bioinformatics
- Genomics
Background:
- Large-scale single-cell RNA sequencing (scRNA-seq) data generation presents analysis challenges.
- Existing methods often rely on dataset alignment and re-clustering.
- Identifying similar cells and transferring annotations across experiments is crucial for knowledge extraction.
Purpose of the Study:
- To explore deep metric learning for cell similarity identification in scRNA-seq data.
- To develop a method for accurate cell type annotation transfer across diverse scRNA-seq experiments.
- To assess the generalizability of learned distance metrics.
Main Methods:
- Developed MapCell, a few-shot learning approach.
- Utilized Siamese Neural Networks (SNNs) to learn a generalizable distance metric.
- Applied the learned metric to transfer cell type annotations.
Main Results:
- Demonstrated accurate annotation transfer across different scRNA-seq platforms, batches, and species.
- Showcased the effectiveness of SNN-derived distance metrics with minimal training data.
- Enabled the flagging of novel cell types within datasets.
Conclusions:
- Deep metric learning, via MapCell and SNNs, offers a powerful approach for scRNA-seq data analysis.
- MapCell facilitates robust cell type annotation transfer, overcoming data heterogeneity.
- The method enhances the utility of large-scale scRNA-seq datasets for biological discovery.
