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Computational Tools for Quantifying Concordance in Single-Cell Fate

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Computational Stem Cell Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1975))

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Abstract

Cells are dynamic biological systems that interact with each other and their surrounding environment. Understanding how cell extrinsic and intrinsic factors control cell fate is fundamental to many biological experiments. However, due to transcriptional heterogeneity or microenvironmental fluctuations, cell fates appear to be random. Individual cells within well-defined subpopulations vary with respect to their proliferative potential, survival, and lineage potency. Therefore, methods to quantify fate outcomes for heterogeneous populations that consider both the stochastic and deterministic features of single-cell dynamics are required to develop accurate models of cell growth and differentiation. To study random versus deterministic cell behavior, one requires a probabilistic modelling approach to estimate cumulative incidence functions relating the probability of a cell’s fate to its lifetime and to model the deterministic effect of cell environment and inheritance, i.e., nature versus nurture. We have applied competing risks statistics, a branch of survival statistics, to quantify cell fate concordance from cell lifetime data. Competing risks modelling of cell fate concordance provides an unbiased, robust statistical modelling approach to model cell growth and differentiation by estimating the effect of cell extrinsic and heritable factors on the cause-specific cumulative incidence function.

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References

  1. Dowling MR, Kan A, Heinzel S, Zhou JHS, Marchingo JM, Wellard CJ, Markham JF, Hodgkin PD (2014) Stretched cell cycle model for proliferating lymphocytes. Proc Natl Acad Sci 111(17):6377–6382

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Duffy KR, Wellard CJ, Markham JF, Zhou JHS, Holmberg R, Hawkins ED, Hasbold J, Dowling MR, Hodgkin PD (2012) Activation-induced B cell fates are selected by intracellular stochastic competition. Science 335(6066):338

    Article  CAS  PubMed  Google Scholar 

  3. Cornwell JA, Hallett RM, der Mauer SA, Motazedian A, Schroeder T, Draper JS, Harvey RP, Nordon RE (2016) Quantifying intrinsic and extrinsic control of single-cell fates in cancer and stem/progenitor cell pedigrees with competing risks analysis. Sci Rep 6:27100. https://doi.org/10.1038/srep27100

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Scherf N, Franke K, Glauche I, Kurth I, Bornhäuser M, Werner C, Pompe T, Roeder I (2012) On the symmetry of siblings: automated single-cell tracking to quantify the behavior of hematopoietic stem cells in a biomimetic setup. Exp Hematol 40(2):119–130.e119

    Article  PubMed  Google Scholar 

  5. Nordon RE, Ko KH, Odell R, Schroeder T (2011) Multi-type branching models to describe cell differentiation programs. J Theor Biol 277(1):7–18. https://doi.org/10.1016/j.jtbi.2011.02.006

    Article  PubMed  Google Scholar 

  6. Kaplan EL, Meier P (1958) Nonparametric-estimation from incomplete observations. J Am Stat Assoc 53(282):457–481. https://doi.org/10.2307/2281868

    Article  Google Scholar 

  7. Scheike TH, Zhang MJ (2008) Flexible competing risks regression modeling and goodness-of-fit. Lifetime Data Anal 14(4):464–483. https://doi.org/10.1007/s10985-008-9094-0

    Article  PubMed  PubMed Central  Google Scholar 

  8. Scheike TH, Sun YQ (2012) On cross-odds ratio for multivariate competing risks data. Biostatistics 13(4):680–694. https://doi.org/10.1093/biostatistics/kxs017

    Article  PubMed  PubMed Central  Google Scholar 

  9. Fine JP, Gray RJ (1999) A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 94(446):496–509. https://doi.org/10.2307/2670170

    Article  Google Scholar 

  10. Gray RJ (1988) A class of K-sample tests for comparing the cumulative incidence of a competing risk. Ann Stat 16(3):1141–1154. https://doi.org/10.1214/aos/1176350951

    Article  Google Scholar 

  11. Cox DR (1972) Regression models and life-tables. J R Stat Soc B 34(2):187–18+

    Google Scholar 

  12. Scheike TH, Holst KK, Hjelmborg JB (2014) Estimating twin concordance for bivariate competing risks twin data. Stat Med 33(7):1193–1204. https://doi.org/10.1002/sim.6016

    Article  PubMed  Google Scholar 

  13. Larson MG (1984) Covariate analysis of competing-risks data with log-linear models. Biometrics 40(2):459–469. https://doi.org/10.2307/2531398

    Article  CAS  PubMed  Google Scholar 

  14. Chen BE, Kramer JL, Greene MH, Rosenberg PS (2008) Competing risks analysis of correlated failure time data. Biometrics 64(1):172–179. https://doi.org/10.1111/j.1541-0420.2007.00868.x

    Article  PubMed  Google Scholar 

  15. Scheike TH, Sun Y (2012) On cross-odds ratio for multivariate competing risks data. Biostatistics (Oxford, England) 13(4):680–694. https://doi.org/10.1093/biostatistics/kxs017

    Article  Google Scholar 

  16. Scheike TH, Zhang M-J (2011) Analyzing competing risk data using the R timereg package. J Stat Softw 38(2):i02

    Article  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Department of Life Sciences, Faculty of Dentistry, University of Sydney, Westmead Centre for Oral Health, Westmead Hospital, Westmead, NSW, Australia

    J. A. Cornwell

  2. Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW, Australia

    R. E. Nordon

Authors
  1. J. A. Cornwell
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  2. R. E. Nordon
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Corresponding author

Correspondence to R. E. Nordon .

Editor information

Editors and Affiliations

  1. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Patrick Cahan

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Cornwell, J.A., Nordon, R.E. (2019). Computational Tools for Quantifying Concordance in Single-Cell Fate. In: Cahan, P. (eds) Computational Stem Cell Biology. Methods in Molecular Biology, vol 1975. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9224-9_6

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  • DOI: https://doi.org/10.1007/978-1-4939-9224-9_6

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9223-2

  • Online ISBN: 978-1-4939-9224-9

  • eBook Packages: Springer Protocols

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