GraphVelo allows inference of multi-modal single cell velocities and molecular mechanisms DOI Creative Commons

Yuhao Chen,

Yan Zhang,

Jiaqi Gan

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 7, 2024

RNA velocities and generalizations emerge as powerful approaches for extracting time-resolved information from high-throughput snapshot single-cell data. Yet, several inherent limitations restrict applying the to genes not suitable velocity inference due complex transcriptional dynamics, low expression, or lacking splicing data of non-transcriptomic modality. Here, we present GraphVelo, a graph-based machine learning procedure that uses input inferred existing methods infers vectors lying in tangent space low-dimensional manifold formed by single cell GraphVelo preserves vector magnitude direction during transformations across different representations. Tests on multiple synthetic experimental scRNA-seq including viral-host interactome multi-omics datasets demonstrate together with downstream generalized dynamo analyses, extends multi-modal reveals quantitative nonlinear regulation relations between genes, virus host cells, layers gene regulation.

Language: Английский

Eomes expression identifies the early bone marrow precursor to classical NK cells DOI
Zhitao Liang, Hope D. Anderson, Veronica Locher

et al.

Nature Immunology, Journal Year: 2024, Volume and Issue: 25(7), P. 1172 - 1182

Published: June 13, 2024

Language: Английский

Citations

10
Dissection and integration of bursty transcriptional dynamics for complex systems DOI
Cheng Gao,

Suriyanarayanan Vaikuntanathan,

Samantha J. Riesenfeld

et al.

Proceedings of the National Academy of Sciences, Journal Year: 2024, Volume and Issue: 121(18)

Published: April 26, 2024

RNA velocity estimation is a potentially powerful tool to reveal the directionality of transcriptional changes in single-cell RNA-sequencing data, but it lacks accuracy, absent advanced metabolic labeling techniques. We developed an approach,

Language: Английский

Citations

5
Systems immunology approaches to study T cells in health and disease DOI Creative Commons
Aaron Yang, Amanda C. Poholek

npj Systems Biology and Applications, Journal Year: 2024, Volume and Issue: 10(1)

Published: Oct. 9, 2024

T cells are dynamically regulated immune that implicated in a variety of diseases ranging from infection, cancer and autoimmunity. Recent advancements sequencing methods have provided valuable insights the transcriptional epigenetic regulation various disease settings. In this review, we identify key sequencing-based been applied to understand transcriptomic epigenomic diseases.

Language: Английский

Citations

0
VAPOR: Variational autoencoder with transport operators decouples co-occurring biological processes in development DOI Open Access
Jie Sheng, Daifeng Wang

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 29, 2024

Emerging single-cell and spatial transcriptomic data enable the investigation of gene expression dynamics various biological processes, especially for development. To this end, existing computational methods typically infer trajectories that sequentially order cells revealing changes in development, e.g., to assign a pseudotime each cell indicating ordering. However, these can aggregate different processes undergo simultaneously-such as maturation specialized function differentiation into specific types-which do not occur on same timescale. Therefore, single axis may distinguish from co-occurring processes.

Language: Английский

Citations

0
GraphVelo allows inference of multi-modal single cell velocities and molecular mechanisms DOI Creative Commons

Yuhao Chen,

Yan Zhang,

Jiaqi Gan

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 7, 2024

RNA velocities and generalizations emerge as powerful approaches for extracting time-resolved information from high-throughput snapshot single-cell data. Yet, several inherent limitations restrict applying the to genes not suitable velocity inference due complex transcriptional dynamics, low expression, or lacking splicing data of non-transcriptomic modality. Here, we present GraphVelo, a graph-based machine learning procedure that uses input inferred existing methods infers vectors lying in tangent space low-dimensional manifold formed by single cell GraphVelo preserves vector magnitude direction during transformations across different representations. Tests on multiple synthetic experimental scRNA-seq including viral-host interactome multi-omics datasets demonstrate together with downstream generalized dynamo analyses, extends multi-modal reveals quantitative nonlinear regulation relations between genes, virus host cells, layers gene regulation.

Language: Английский

Citations

0