LineageVAE: Reconstructing Historical Cell States and Transcriptomes toward Unobserved Progenitors.
Bioinformatics
; 2024 Aug 22.
Article
em En
| MEDLINE
| ID: mdl-39172488
ABSTRACT
MOTIVATION Single-cell RNA sequencing (scRNA-seq) enables comprehensive characterization of the cell state. However, its destructive nature prohibits measuring gene expression changes during dynamic processes such as embryogenesis. Although recent studies integrating scRNA-seq with lineage tracing have provided clonal insights between progenitor and mature cells, challenges remain. Because of their experimental nature, observations are sparse, and cells observed in the early state are not the exact progenitors of cells observed at later time points. To overcome these limitations, we developed LineageVAE, a novel computational methodology that utilizes deep learning based on the property that cells sharing barcodes have identical progenitors. RESULTS:
LineageVAE is a deep generative model that transforms scRNA-seq observations with identical lineage barcodes into sequential trajectories toward a common progenitor in a latent cell state space. This method enables the reconstruction of unobservable cell state transitions, historical transcriptomes, and regulatory dynamics at a single-cell resolution. Applied to hematopoiesis and reprogrammed fibroblast datasets, LineageVAE demonstrated its ability to restore backward cell state transitions and infer progenitor heterogeneity and transcription factor activity along differentiation trajectories. AVAILABILITY AND IMPLEMENTATION The LineageVAE model was implemented in Python using the PyTorch deep learning library. The code is available on GitHub at https//github.com/LzrRacer/LineageVAE/. SUPPLEMENTARY INFORMATION Available at Bioinformatics online.
deep generative model; deep learning; differentiation; dimensionality reduction; generalized linear model (GLM); hematopoiesis; lineage tracing; machine learning; neural network; reprogramming; single-cell RNA sequencing (scRNA-seq); time series data; transcription factor activity; tumor evolution; variational autoencoder (VAE)
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Japão