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Related Concept Videos

The Cell Cycle Control System02:11

The Cell Cycle Control System

The cell cycle is an organized set of events that leads the cell to divide into two daughter cells, each containing chromosomes identical to the parent cell. It is the cell cycle that leads to the formation of an entire organism from a single-cell zygote. Besides, cell division also functions in the renewal or repair of tissues in adult multicellular eukaryotes. For example, in the bone marrow, the stem cells divide to form new blood cells. Although essential for several functions, cell...
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Studying Cell Cycle-regulated Gene Expression by Two Complementary Cell Synchronization Protocols
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Granger causality analysis of human cell-cycle gene expression profiles.

Radhakrishnan Nagarajan1, Meenakshi Upreti

  • 1University of Arkansas for Medical Sciences, USA. rnagarajan@uams.edu

Statistical Applications in Genetics and Molecular Biology
|September 4, 2010
PubMed
Summary

Granger causality (GC) tests for gene expression networks can be misleading. Vector autoregressive (VAR) parameter estimation is crucial for accurate interpretation of GC results, preventing spurious functional relationships.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Genomics

Background:

  • Granger causality (GC) is used to infer functional relationships from time series data, particularly in gene expression studies.
  • Vector autoregressive (VAR) models are employed to analyze temporal dependencies in biological systems.
  • Previous applications of GC in gene expression analysis have not fully accounted for underlying process parameters.

Purpose of the Study:

  • To investigate the influence of Vector Autoregressive (VAR) process parameters on Granger Causality (GC) test results in human cell-cycle gene expression data.
  • To determine if auto-regulatory feedback and noise variance impact the statistical significance and interpretation of GC.
  • To provide a more robust framework for inferring functional gene relationships from temporal expression profiles.

Main Methods:

  • Analysis of human cell-cycle gene expression data modeled as a first-order bivariate VAR process.
  • Analytical derivation of the contribution of VAR parameters (auto-regulatory feedback, noise variance) to mean-squared forecast error.
  • VAR parameter estimation to assess auto-regulatory feedback and noise variance discrepancies.
  • Reinvestigation of published GC analysis case studies on the same dataset.

Main Results:

  • VAR process parameters, specifically auto-regulatory feedback and noise variance, significantly influence the mean-squared forecast error and thus the statistical significance of GC.
  • Discrepancies in noise variance, potentially arising from artifacts, can lead to the spurious identification of functional relationships between genes.
  • Significant auto-regulatory feedback and noise variance differences were observed in cell-cycle gene expression profiles via VAR parameter estimation.

Conclusions:

  • Blind inference of functional gene relationships based solely on GC statistical significance is discouraged.
  • VAR parameter estimation is essential for accurate interpretation of GC results in gene expression time series.
  • Accounting for VAR model parameters enhances the reliability of inferring gene regulatory networks.