Decoding cell identity with multi-scale explainable deep learning
Zhu, J.; Zhang, Z.; Xiang, Y.; Xie, B.; Dong, X.; Xie, L.; Zhou, P.; Yao, R.; Wang, X.; Li, Y.; He, F.; Zhu, W.; Zhang, Z.; Chang, C.
Show abstract
Cells are the fundamental structural and functional units of life. Studying the definition and composition of different cell types can help us understand the complex mechanisms underlying biological diversity and functionality. The increasing volume of extensive single-cell omics data makes it possible to provide detailed characterisations of cell types. Recently, there has been a rise in deep learning-based approaches that generate cell type labels solely through mapping query data to reference data. However, these approaches lack multi-scale descriptions and interpretations of identified cell types. Here, we propose Cell Decoder, a biological prior knowledge informed model to achieve multi-scale representation of cells. We implemented automated machine learning and post-hoc analysis techniques to decode cell identity. We have shown that Cell Decoder compares favourably to existing methods, offering multi-view interpretability for decoding cell identity and data integration. Furthermore, we have showcased its applicability in uncovering novel cell types and states in both human bone and mouse embryonic contexts, thereby revealing the multi-scale heterogeneity inherent in cell identities.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- CellContrast: Reconstructing Spatial Relationships in Single-Cell RNA Sequencing Data via Deep Contrastive Learning 96%
- scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis 96%
- Density Physics-Informed Neural Network reveals sources of cell heterogeneity in signal transduction 95%
Similar papers in this journal
- scTrace+: enhance the cell fate inference by integrating the lineage-tracing and multi-faceted transcriptomic similarity information 97%
- Integrative, high-resolution analysis of single cell gene expression across experimental conditions with PARAFAC2-RISE 95%
- Ultrafast and interpretable single-cell 3D genome analysis with Fast-Higashi 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.