LVPT: Lazy Velocity Pseudotime Inference Method.

Shuainan Mao, Jiajia Liu, Weiling Zhao, Xiaobo Zhou
Author Information
  1. Shuainan Mao: The Department of Biotherapy and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610041, China.
  2. Jiajia Liu: Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA. ORCID
  3. Weiling Zhao: Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
  4. Xiaobo Zhou: Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.

Abstract

The emergence of RNA velocity has enriched our understanding of the dynamic transcriptional landscape within individual cells. In light of this breakthrough, we embarked on integrating RNA velocity with cellular pseudotime inference, aiming to improve the prediction of cell orders along biological trajectories beyond existing methods. Here, we developed LVPT, a novel method for pseudotime and trajectory inference. LVPT introduces a lazy probability to indicate the probability that the cell stays in the original state and calculates the transition matrix based on RNA velocity to provide the probability and direction of cell differentiation. LVPT shows better and comparable performance of pseudotime inference compared with other existing methods on both simulated datasets with different structures and real datasets. The validation results were consistent with prior knowledge, indicating that LVPT is an accurate and efficient method for pseudotime inference.

Keywords

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Grants

  1. R01 CA241930/NCI NIH HHS
  2. U01 AR069395/NIAMS NIH HHS
  3. R01 GM123037/NIGMS NIH HHS

MeSH Term

Cell Differentiation
Probability
RNA

Chemicals

RNA

Word Cloud

Created with Highcharts 10.0.0inferencepseudotimecellLVPTRNAvelocityprobabilityexistingmethodsmethodtrajectorydatasetsemergenceenrichedunderstandingdynamictranscriptionallandscapewithinindividualcellslightbreakthroughembarkedintegratingcellularaimingimprovepredictionordersalongbiologicaltrajectoriesbeyonddevelopednovelintroduceslazyindicatestaysoriginalstatecalculatestransitionmatrixbasedprovidedirectiondifferentiationshowsbettercomparableperformancecomparedsimulateddifferentstructuresrealvalidationresultsconsistentpriorknowledgeindicatingaccurateefficientLVPT:LazyVelocityPseudotimeInferenceMethodrandomwalksingle

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