Decomposing patient heterogeneity of single-cell cancer data by cross-attention neural networks
Subedi, S.; Park, Y. P.
Show abstract
Gene expression variation in cancer cells is attributed to many inherited and environmental factors, including genetic variants and cellular landscapes. Decomposing different sources of information is intractable with single-cell RNA-seq alone. However, we show that our new approach can split them with the help of multiple patients, assuming that cell types are widely shared and genetic effects are specifically present in a particular patient. Our approach based on a cross-attention neural network was applied to three different cancer types to identify cell types and patient-specific genetic effects in transcriptomic data. Residual expressions, excluding cell types, can implicate patient-specific disease mechanisms.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- uniPort: a unified computational framework for single-cell data integration with optimal transport 98%
- Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data 98%
- scMODAL: A general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links 97%
Similar papers in this journal
"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.