Abstract

Replicative lifespan (RLS) of the budding yeast is the number of mother cell divisions until senescence and is instrumental to understanding mechanisms of cellular aging. Recent research has shown that replicative aging is heterogeneous, which argues for mixture modeling. The mixture model is a statistical method to infer subpopulations of the heterogeneous population. Mixture modeling is a relatively underdeveloped area in the study of cellular aging. There is no open access software currently available that assists extensive comparison among mixture modeling methods. To address these needs, we developed an R package called fitmix that facilitates the computation of well-known distributions utilized for RLS data and other lifetime datasets. This package can generate a group of functions for the estimation of probability distributions and simulation of random observations from well-known finite mixture models including Gompertz, Log-logistic, Log-normal, and Weibull models. To estimate and compute the maximum likelihood estimates of the model parameters, the Expectation–Maximization (EM) algorithm is employed.

Highlights

  • An increase in mortality rate is typically interpreted as aging

  • This study aims to characterize the practicality of the fitmix package and demonstrate its traits using the analysis of yeast replicative lifespan data [9,14,21]

  • We have developed and introduced an R package called fitmix that provides several models and estimation techniques for modeling replicative lifespan distributions

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Summary

Introduction

The budding yeast S. cerevisiae has revealed many significant properties in the mechanism of eukaryotic lifespan regulation [1,2,3]. Analysis of the RLS data has been conducted with nonparametric methods or using typical parametric survival models [7]. Survival analysis of the lifespan datasets has been generally delineated with the standard lifespan statistical distributions such as Gompertz, Weibull, Logistic, and Log-logistic [8]. In addition to these conventional statistical distribution models, several models for RLS data of budding yeast have been recently published [9]

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