Statistical evidence for the presence of trajectory in single-cell data.

Lovemore Tenha, Mingzhou Song
Author Information
  1. Lovemore Tenha: Department of Computer Science, New Mexico State University, Las Cruces, USA. ORCID
  2. Mingzhou Song: Department of Computer Science, New Mexico State University, Las Cruces, USA. joemsong@cs.nmsu.edu. ORCID

Abstract

BACKGROUND: Cells progressing from an early state to a developed state give rise to lineages in cell differentiation. Knowledge of these lineages is central to developmental biology. Each biological lineage corresponds to a trajectory in a dynamical system. Emerging single-cell technologies such as single-cell RNA sequencing can capture molecular abundance in diverse cell types in a developing tissue. Many computational methods have been developed to infer trajectories from single-cell data. However, to our knowledge, none of the existing methods address the problem of determining the existence of a trajectory in observed data before attempting trajectory inference.
RESULTS: We introduce a method to identify the existence of a trajectory using three graph-based statistics. A permutation test is utilized to calculate the empirical distribution of the test statistic under the null hypothesis that a trajectory does not exist. Finally, a p-value is calculated to quantify the statistical significance for the presence of trajectory in the data.
CONCLUSIONS: Our work contributes new statistics to assess the level of uncertainty in trajectory inference to increase the understanding of biological system dynamics.

Keywords

References

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Grants

  1. 1661331/Division of Biological Infrastructure
  2. 2016-51181-25408/National Institute of Food and Agriculture

MeSH Term

Cell Differentiation
Single-Cell Analysis

Word Cloud

Created with Highcharts 10.0.0trajectorysingle-celldatainferencestatisticsstatedevelopedlineagescellbiologybiologicalsystemsequencingmethodsexistencetestpresenceBACKGROUND:CellsprogressingearlygiverisedifferentiationKnowledgecentraldevelopmentallineagecorrespondsdynamicalEmergingtechnologiesRNAcancapturemolecularabundancediversetypesdevelopingtissueManycomputationalinfertrajectoriesHoweverknowledgenoneexistingaddressproblemdeterminingobservedattemptingRESULTS:introducemethodidentifyusingthreegraph-basedpermutationutilizedcalculateempiricaldistributionstatisticnullhypothesisexistFinallyp-valuecalculatedquantifystatisticalsignificanceCONCLUSIONS:workcontributesnewassessleveluncertaintyincreaseunderstandingdynamicsStatisticalevidenceDevelopmentalGraph-basedMinimumspanningtreeSingle-cellTrajectory

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