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Representation learning of RNA velocity reveals robust cell transitions
[article]
2021
bioRxiv
pre-print
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Here, we present VeloAE, a tailored representation learning method to learn a low-dimensional representation of RNA velocity on which cell transitions can be robustly estimated. ...
RNA velocity is a promising technique to reveal transient cellular dynamics among a heterogeneous cell population and quantify their transitions from single-cell transcriptome experiments. ...
The compact form of projecting velocities of all cells using matrix algebra is: V z = encode(S + V) − encode(S) ∈ R N ×dz (6)
Cell transitions from low-dimensional representations 225 With low-dimensional ...
doi:10.1101/2021.03.19.436127
fatcat:jg6ibnbg7ffnvffn2ziuwo5e5y
PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells
2019
Genome Biology
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Single-cell RNA-seq quantifies biological heterogeneity across both discrete cell types and continuous cell transitions. ...
Partition-based graph abstraction (PAGA) provides an interpretable graph-like map of the arising data manifold, based on estimating connectivity of manifold partitions ( https://github.com/theislab/paga ...
Finally, we show how PAGA abstracts transition graphs, for instance, from RNA velocity and compare to previous trajectory-inference algorithms. ...
doi:10.1186/s13059-019-1663-x
pmid:30890159
pmcid:PMC6425583
fatcat:vn5exscyl5bhxevezn2kvsymiu
Mapping Vector Field of Single Cells
[article]
2019
bioRxiv
pre-print
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Understanding how gene expression in single cells progress over time is vital for revealing the mechanisms governing cell fate transitions. ...
This work thus foreshadows the possibility of predicting long-term trajectories of cells during a dynamic process instead of short time velocity estimates. ...
One additional benefit of studying protein velocity is that, compared to RNA expression, proteins are often a better representation of cell states because they are the direct executors of biological processes ...
doi:10.1101/696724
fatcat:xoqks7ostnan3hiucv6q4yio6i
scDVF: Data-driven Single-cell Transcriptomic Deep Velocity Field Learning with Neural Ordinary Differential Equations
[article]
2022
bioRxiv
pre-print
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Recent advances in single-cell RNA sequencing technology provided unprecedented opportunities to simultaneously measure the gene expression profile and the transcriptional velocity of individual cells, ...
Benchmarking with state-of-the-art vector-field learning methods shows that scDVF can improve representation accuracy by at least 50%. ...
Acknowledgments Funding: Research reported in this publication was supported by the National Institutes of Health under award numbers [insert here]. ...
doi:10.1101/2022.02.15.480564
fatcat:ky7fssxkyzeqncatyeavfphdwq
Revealing routes of cellular differentiation by single-cell RNA-seq
2018
Current Opinion in Systems Biology
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It remains one of the major challenges to understand how nature has designed such robust and reproducible regulatory mechanisms. ...
The recent availability of large-scale sensitive single-cell RNAseq protocols has enabled the generation of snapshot data covering the entire spectrum of cell states in a system of interest. ...
Acknowledgements I thank Nina Cabezas-Wallscheid, Roman Sankowski, and Josip Herman for critical reading of the manuscript. The work was financially supported by the Max Planck Society. ...
doi:10.1016/j.coisb.2018.07.006
fatcat:t44u27lsarei7n2osuwvqlubay
Protein velocity and acceleration from single-cell multiomics experiments
2020
Genome Biology
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To enable such temporal analysis from multimodal single-cell experiments, we introduce an extension of the RNA velocity method that leverages estimates of unprocessed transcript and protein abundances ...
The simultaneous quantification of protein and RNA makes possible the inference of past, present, and future cell states from single experimental snapshots. ...
Peer review information Barbara Cheifet was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team. ...
doi:10.1186/s13059-020-1945-3
pmid:32070398
fatcat:zgprgoev4bb2fgllowr7y4pfeu
Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction
[article]
2022
bioRxiv
pre-print
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RNA velocity infers the direction and speed of transcriptional changes in individual cells, yet it is unclear how these temporal gene expression modalities may be leveraged for predictive modeling of cellular ...
Current methods for analyzing single-cell datasets have relied primarily on static gene expression measurements to characterize the molecular state of individual cells. ...
This poses two fundamental challenges for robust prediction of the dynamic progression of cell state. ...
doi:10.1101/2022.03.01.482381
fatcat:zvs6pljzqrbplmur4vj6bpecza
CellRank for directed single-cell fate mapping
[article]
2020
bioRxiv
pre-print
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Our approach combines the robustness of trajectory inference with directional information from RNA velocity, derived from ratios of spliced to unspliced reads. ...
Computational trajectory inference enables the reconstruction of cell-state dynamics from single-cell RNA sequencing experiments. ...
Tritschler for helping us with the biological interpretation of results, F. Paul for guidance regarding PCCA and GPCCA, G. ...
doi:10.1101/2020.10.19.345983
fatcat:zqtjfglgvjgddahsulxgvgxyfi
Concepts and limitations for learning developmental trajectories from single cell genomics
2019
Development
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Nowadays, tens of thousands of cells are routinely sequenced in single cell-based methods and even more are expected to be analysed in the future. ...
Single cell genomics has become a popular approach to uncover the cellular heterogeneity of progenitor and terminally differentiated cell types with great precision. ...
We would like to thank Anika Böttcher for helping with conceptualisation of this Review.
Competing interests The authors declare no competing or financial interests. ...
doi:10.1242/dev.170506
pmid:31249007
fatcat:fhvgg6uezndonefqb3cdt4gkvy
Generalizing RNA velocity to transient cell states through dynamical modeling
[article]
2019
bioRxiv
pre-print
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The introduction of RNA velocity in single cells has opened up new ways of studying cellular differentiation. ...
This generalizes RNA velocity to a wide variety of systems comprising transient cell states, which are common in development and in response to perturbations. ...
As such, PAGA 7 has made a first suggestion for inferring directed abstracted representations of trajectories through RNA velocity. ...
doi:10.1101/820936
fatcat:irokqdgucjgjxnrur2lshnrmde
Cytopath: Simulation based inference of differentiation trajectories from RNA velocity fields
[article]
2020
bioRxiv
pre-print
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We report Cytopath, a method for trajectory inference that takes advantage of transcriptional activity information from RNA velocity of single cells to perform trajectory inference. ...
Trajectory inference constitutes a frequent step in interpreting single-cell RNA sequencing studies. ...
For Cytopath, in addition to the projection the original RNA velocity field was used to infer cell to cell transitions. Pancreatic endocrinogenesis. ...
doi:10.1101/2020.12.21.423801
fatcat:6tdjyccjrfcplm53zcpctfriuq
Deciphering Cell Fate Decision by Integrated Single-Cell Sequencing Analysis
2020
Annual Review of Biomedical Data Science
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Here, we review single-cell RNA sequencing and sequencing of other modalities as methods to elucidate the molecular underpinnings of lineage specification. ...
mechanistic insights into the process of cell fate decision. ...
RNA velocity is a notable exception, as the ratio of spliced and unspliced mRNA is used to predict the future state of the cell. ...
doi:10.1146/annurev-biodatasci-111419-091750
pmid:32780577
pmcid:PMC7115822
fatcat:ecayod5czbaapoazv7mbbpaxf4
Modeling Global Dynamics from Local Snapshots with Deep Generative Neural Networks
[article]
2019
arXiv
pre-print
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We perform three case studies in which we apply DyMoN to different types of biological systems and extract features of the dynamics in each case by examining the learned model. ...
In order to model the dynamics of such systems given snapshot data, or local transitions, we present a deep neural network framework we call Dynamics Modeling Network or DyMoN. ...
the transition velocity vector ∆ θ (x). ...
arXiv:1802.03497v5
fatcat:vmweffqakbexreof6hyeigoio4
Defining Epidermal Basal Cell States during Skin Homeostasis and Wound Healing Using Single-Cell Transcriptomics
2020
Cell Reports
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Pseudotemporal trajectory and RNA velocity analyses predict a quasi-linear differentiation hierarchy where basal cells progress from Col17a1Hi/Trp63Hi state to early-response state, proliferate at the ...
Using single-cell RNA sequencing coupled with RNAScope and fluorescence lifetime imaging, we identify three non-proliferative and one proliferative basal cell state in homeostatic skin that differ in metabolic ...
ACKNOWLEDGMENTS We thank the Genomics High Throughput Facility and the Institute for Immunology FACS Core Facility at University of California, Irvine (UCI) for expert service. ...
doi:10.1016/j.celrep.2020.02.091
pmid:32187560
pmcid:PMC7218802
fatcat:ug7tadqpmvco3f3ixv2xhdyf7a
1458 EMT-inhibiting transcription factor Ovol2 regulates directional cell migration and proliferation in adult skin epithelia
2018
Journal of Investigative Dermatology
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Graphical Abstract Highlights d scRNA-seq identifies four epidermal basal cell states in homeostatic adult skin d Computational analysis supports a "hierarchical" model of epidermal homeostasis d Basal ...
cell states are metabolically distinct and spatially partitioned in wounded skin d Epidermal basal cells show enhanced cell fate and state plasticity during wound healing ...
Inclusion of these two HFSC clusters in RNA velocity analysis of WO epidermal basal cells revealed velocity arrows pointing from Cd34 Low HFSCs to ER/GA epidermal basal cells ( Figure S7G ), raising the ...
doi:10.1016/j.jid.2018.03.1476
fatcat:2m5yr37qmree7kgsbzvyvx42ai
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