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

Authors

Ziwei Chen, Bingwei Zhang, Linrui Tang, Fuzhou Gong, Lin Wan, Liang Ma

Published in

Genome research. Aug 21, 2026. Epub Aug 21, 2026.

Abstract

The advent of single-cell lineage-tracing technologies has enabled the simultaneous profiling of gene expression and lineage barcodes. However, accurate, high-resolution reconstruction of cell lineage trees remains challenging because most existing approaches treat these modalities separately and therefore fail to fully exploit their complementary information. Here we present BiLinT, a Bayesian framework that jointly models multimodal single-cell lineage-tracing data for lineage tree reconstruction. BiLinT integrates barcode evolution (a continuous-time Markov chain) with gene expression dynamics (an Ornstein-Uhlenbeck process) within a unified probabilistic model. Across synthetic and real datasets, BiLinT provides accurate lineage-tree reconstruction and reveals differentiation-associated clonal structure and developmental fate biases.

PMID:
42629163
Bibliographic data and abstract were imported from PubMed on 22 Aug 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.