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Unraveling causal gene regulation from the RNA velocity graph using Velorama

View ORCID ProfileRohit Singh, Alexander P. Wu, Anish Mudide, Bonnie Berger
doi: https://doi.org/10.1101/2022.10.18.512766
Rohit Singh
†Computer Science and Artificial Intelligence Lab., MIT, Cambridge, MA 02139
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  • For correspondence: rsingh@mit.edu bab@mit.edu
Alexander P. Wu
†Computer Science and Artificial Intelligence Lab., MIT, Cambridge, MA 02139
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Anish Mudide
§Philips Exeter Academy, Exeter, NH 03883. Work performed while AM was a summer student at MIT
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Bonnie Berger
¶Computer Science and Artificial Intelligence Lab and Dept. of Mathematics, MIT, Cambridge, MA 02139
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  • For correspondence: rsingh@mit.edu bab@mit.edu
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Abstract

Gene regulatory network (GRN) inference that incorporates single-cell RNA-seq (scRNA-seq) differentiation trajectories or RNA velocity can reveal causal links between transcription factors and their target genes. However, current GRN inference methods require a total ordering of cells along a linear pseudotemporal axis, which is biologically inappropriate since trajectories with branches cannot be reduced to a single time axis. Such orderings are especially difficult to derive from RNA velocity studies since they characterize each cell’s state transition separately. Here, we introduce Velorama, a novel conceptual approach to causal GRN inference that newly represents scRNA-seq differentiation dynamics as a partial ordering of cells and operates on the directed acyclic graph (DAG) of cells constructed from pseudotime or RNA velocity measurements. To our knowledge, Velorama is the first GRN inference method that can work directly with RNA velocity-based cell-to-cell transition probabilities. On a standard set of synthetic datasets, we first demonstrate Velorama’s use with just pseudotime, finding that it improves area under the precision-recall curve (AUPRC) by 1.25-3x over state-of-the-art approaches. Using RNA velocity instead of pseudotime as the input to Velorama further improves AUPRC by an additional 1.75-3x. We also applied Velorama to study cell differentiation in pancreas, dentate gyrus, and bone marrow from real datasets and obtained intriguing evidence for the relationship between regulator interaction speeds and mechanisms of gene regulatory control during differentiation. We expect Velorama to be a critical part of the RNA velocity toolkit for investigating the causal drivers of differentiation and disease.

Software availability https://cb.csail.mit.edu/cb/velorama

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • http://cb.csail.mit.edu/cb/velorama

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 21, 2022.
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Unraveling causal gene regulation from the RNA velocity graph using Velorama
Rohit Singh, Alexander P. Wu, Anish Mudide, Bonnie Berger
bioRxiv 2022.10.18.512766; doi: https://doi.org/10.1101/2022.10.18.512766
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Unraveling causal gene regulation from the RNA velocity graph using Velorama
Rohit Singh, Alexander P. Wu, Anish Mudide, Bonnie Berger
bioRxiv 2022.10.18.512766; doi: https://doi.org/10.1101/2022.10.18.512766

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