Hostname: page-component-8448b6f56d-42gr6 Total loading time: 0 Render date: 2024-04-24T00:22:46.726Z Has data issue: false hasContentIssue false

Extensions of the bifurcating autoregressive model for cell lineage studies

Published online by Cambridge University Press:  14 July 2016

R. M. Huggins*
Affiliation:
La Trobe University
I. V. Basawa*
Affiliation:
University of Georgia
*
Postal address: Department of Statistical Science, La Trobe University, Bundoora 3083, Australia. Email address: r.huggins@latrobe.edu.au.
∗∗Postal address: Department of Statistics, University of Georgia, Athens, GA 30602–1952, USA.

Abstract

The bifurcating autoregressive model has been used previously to model cell lineage data. A feature of this model is that each line of descendants from an initial cell follows an AR(1) model, and that the environmental effects on sisters are correlated. However, this model concentrates on modelling the correlations between mother and daughter cells and between sister cells, and does not explain the large correlations between more distant relatives observed by some authors. Here the model is extended, firstly by allowing lines of descent to follow an ARMA(p,q) model rather than an AR(1) model, and secondly by allowing correlations between the environmental effects of relatives more distant than sisters. The models are applied to several data sets consisting of independent cell lineage trees.

Type
Short Communications
Copyright
Copyright © Applied Probability Trust 1999 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aptech Systems (1992). GAUSS. Aptech Systems Inc., Maple Valley, WA.Google Scholar
Brockwell, P. J., and Davis, R. A. (1987). Time Series: Theory and Methods. Springer, New York.Google Scholar
Brooks, R. F., Riddle, P. N., Richmond, F. N., and Marsden, J. (1983). The G1 distribution of ‘G1-less’ V79 Chinese hamster cells. Experimental Cell Research 148, 127142.Google Scholar
Cowan, R. (1984). Statistical concepts in the analysis of cell lineage data. In Proc. 1983 Workshop on Cell Growth and Division, La Trobe University, Bundoora, Australia, pp. 1822.Google Scholar
Cowan, R., and Staudte, R. G. (1986). The bifurcating autoregression model in cell lineage studies. Biometrics 42, 769783.Google Scholar
Huggins, R. M. (1996). Robust inference for variance components models for single trees of cell lineage data. Ann. Statist. 24, 11451160.CrossRefGoogle Scholar
Huggins, R. M., and Staudte, R. G. (1994). Variance components models for dependent cell populations. J. Amer. Statist. Assoc. 89, 1929.Google Scholar
Powell, E. O. (1955). Some features of the generation times of individual bacteria. Biometrika 42, 1644.Google Scholar
Powell, E. O. (1956). An improved culture chamber for the study of living bacteria. J. R. Micr. Soc. 75, 235.Google Scholar
Powell, E. O. (1958). An outline of the pattern of bacterial generation times. J. Gen. Microbiol. 18, 382417.Google Scholar
Powell, E. O., and Errington, F. P. (1963). Generation times of individual bacteria: some corroborative measurements. J. Gen. Microbiol. 31, 315327.CrossRefGoogle ScholarPubMed
Staudte, R. G. (1992). A bifurcating autoregression model for cell lineage data with varying generation means. J. Theoret. Biol. 156, 183195.CrossRefGoogle Scholar
Staudte, R. G., Guiguet, M., and Collyn D'Hooghe, M. (1984). Additive models for dependent cell populations. J. Theoret. Biol. 109, 127146.Google Scholar
Staudte, R. G., Zhang, J., Huggins, R. M. and Cowan, R. (1996). A reexamination of the cell lineage data of E. O. Powell. Biometrics 52, 12141222.Google Scholar