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Related Experiment Video

Updated: Mar 24, 2026

Analysis of Cell Cycle Position in Mammalian Cells

Analysis of Cell Cycle Position in Mammalian Cells

Published on: January 21, 2012

Circular piecewise regression with applications to cell-cycle data.

Cristina Rueda¹, Miguel A Fernández¹, Sandra Barragán¹

¹Departamento de Estadística e I.O., Universidad de Valladolid, 47011 Valladolid, Spain.

|March 19, 2016

Summary

This summary is machine-generated.

This study introduces a new circular regression model for analyzing gene expression data. The flexible model allows different parameters for various circular sectors, improving analysis of cell-cycle gene phases.

Keywords:

Change points Circular data Circular-circular regression Gene expression Generalized AIC Von Mises distribution

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Measuring Cell Cycle Progression Kinetics with Metabolic Labeling and Flow Cytometry

Measuring Cell Cycle Progression Kinetics with Metabolic Labeling and Flow Cytometry

Published on: May 22, 2012

Area of Science:

Statistics
Bioinformatics
Genomics

Background:

Circular regression models are used in diverse fields, including biology.
Analyzing gene expression in oscillatory systems, particularly cell-cycle genes, presents unique challenges.
Existing models may not adequately capture phase relationships in genes across species with different periods.

Purpose of the Study:

To propose a flexible piecewise circular regression model for analyzing gene expression data.
To model the relationships among the phases of cell-cycle genes in two species.
To provide a robust methodology for understanding oscillatory gene behavior.

Main Methods:

Development of a piecewise circular regression model with sector-specific parameters.
Derivation of maximum likelihood estimators for model parameters.
Implementation of a model selection procedure using generalized degrees of freedom.

Main Results:

The proposed model offers flexibility by allowing different parameters across circular sectors.
Maximum likelihood estimators were successfully derived for the model.
A model selection procedure was established for choosing optimal model configurations.

Conclusions:

The novel circular regression model effectively analyzes cell-cycle gene expression data.
The methodology provides a powerful tool for understanding gene phase relationships in oscillatory systems.
The approach was validated using two distinct cell-cycle data sets.