Deep Lineage: Single-Cell Lineage Tracing and Fate Inference Using Deep Learning
Sadria, M.; Zhang, A.; Bader, G.
Show abstract
Recent advances in single-cell RNA-sequencing and lineage tracing techniques have provided valuable insights into the temporal changes in gene expression during development, tumour progression, and disease onset. However, there are few computational methods available to analyze this information to help understand multicellular dynamics. We introduce Deep Lineage, a novel deep-learning method for analyzing time-series single-cell RNA-sequencing with matched lineage-tracing data. Our method accurately predicts early cell fate biases and gene expression profiles at different time points within a clone, surpassing current state-of-the-art methods in fate prediction accuracy. Additionally, through in silico perturbations in cellular reprogramming and hematopoiesis development data, we show that Deep Lineage can accurately model dynamic multicellular responses while identifying key genes and pathways associated with cell fate determination.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Learning the rules of cell competition without prior scientific knowledge 94%
- Gene set inference from single-cell sequencing data using a hybrid of matrix factorization and variational autoencoders 94%
- Inferring spatial single-cell-level interactions through interpreting cell state and niche correlations learned by self-supervised graph transformer 94%
Similar papers in this journal
- scDREAMER: atlas-level integration of single-cell datasets using deep generative model paired with adversarial classifier 96%
- A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples 95%
- DUBStepR: correlation-based feature selection for clustering single-cell RNA sequencing data 95%
Similar papers in this journal
Similar papers in this journal
- Unsupervised discovery of dynamic cell phenotypic states from transmitted light movies 95%
- The shape of cancer relapse: Topological data analysis predicts recurrence in paediatric acute lymphoblastic leukaemia 94%
- Tempora: cell trajectory inference using time-series single-cell RNA sequencing data 94%
Similar papers in this journal
- Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data 96%
- Scalable identification of lineage-specific gene regulatory networks from metacells with NetID 96%
- A Message Passing Framework for Precise Cell State Identification with scClassify2 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.