Bayesian inference of lineage trees by joint analysis of single-cell multimodal lineage-tracing data with BiLinT

Chen, Z.; Zhang, B.; Tang, L.; Gong, F.; Wan, L.; Ma, L.

doi:10.1101/2025.09.21.677669

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bioRxiv · 10.1101/2025.09.21.677669

Bayesian inference of lineage trees by joint analysis of single-cell multimodal lineage-tracing data with BiLinT

Chen, Z.·Zhang, B.·Tang, L.·Gong, F.·Wan, L.·Ma, L.

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Abstract

The advent of single-cell lineage-tracing technologies has enabled simultaneous measurement of gene expression and lineage barcodes. However, integrating these modalities for high-resolution lineage reconstruction has remained challenging due to limitations of methods that analyze modalities separately. In response, we present BiLinT, a Bayesian framework for reconstructing high-resolution cell lineage trees by jointly modeling single-cell multimodal lineage-tracing data. BiLinT integrates lineage barcode evolution (modeled via a continuous-time Markov chain) and gene expression dynamics (modeled via an Ornstein-Uhlenbeck process) within a unified probabilistic model. Applications to synthetic and real datasets demonstrate improved accuracy and reveal developmental fate biases.

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Chen, Z., Zhang, B., Tang, L., Gong, F., Wan, L., Ma, L.. 2025-09-23. Bayesian inference of lineage trees by joint analysis of single-cell multimodal lineage-tracing data with BiLinT. https://doi.org/10.1101/2025.09.21.677669

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