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August 15, 2025BioinformaticsOpen Access

A Semi-Supervised Bayesian Approach for Marker Gene Trajectory Inference from Single-Cell RNA-Seq Data

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Authors

JWJunchao WangLSL. SunNWNana Wei

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Overview

BayesTraj demonstrates improved pseudotime inference and lineage assignments in single-cell RNA-seq data, highlighting its robustness.

Key Points

  • BayesTraj improves pseudotime inference and lineage assignments from single-cell RNA-seq data, enhancing trajectory accuracy.
  • It outperforms existing models by incorporating prior knowledge of lineage topology and marker-gene dynamics.
  • Using Hamiltonian Monte Carlo, the framework conducts posterior inference for lineage proportions and gene activation parameters.
  • Evaluations show that BayesTraj provides more stable reconstructions, addressing issues with noise and data sparsity.

Cite This Study

Wang et al. (2025) studied this question.

synapsesocial.com/papers/68a3654c0a429f797332af79https://doi.org/10.1093/bioinformatics/btaf454
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  4. 4Joint trajectory inference for single-cell genomics using deep learning with a mixture prior2024 · 10 citations
  5. 5Trajectory inference for aging time courses of single-cell data2026