Identifying Non-random Patterns from Gene Expression Profiles

Authors:
Radhakrishnan Nagarajan;Meenakshi Upreti;Mariofanna Milanova
Affiliations:
Department of Biostatistics,;Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, USA;Department of Computer Sciences, University of Arkansas at Little Rock, USA
Venue:
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Year:
2008

Citing 7
Cited 0

Testing for nonlinearity in time series: the method of surrogate data

Conference proceedings on Interpretation of time series from nonlinear mechanical systems
Digital signal processing (3rd ed.): principles, algorithms, and applications

Digital signal processing (3rd ed.): principles, algorithms, and applications
Surrogate time series

Physica D
Comparing the similarity of time-series gene expression using signal processing metrics

Computers and Biomedical Research
Analyzing time series gene expression data

Bioinformatics
Classification using functional data analysis for temporal gene expression data

Bioinformatics
Unbiased pattern detection in microarray data series

Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

There has been considerable interest in identifying biologically relevant genes from temporal microarray gene expression profiles using linear and nonlinear measures. The present study uses two distinct approaches namely: classical order zero-crossing count (ZCC) and Lempel-Ziv (LZ) complexity in identifying non-random patterns from temporal gene expression profiles. While the former captures the linear statistical properties of the time series such a power-spectrum, the latter has been used to capture nonlinear dynamical properties of gene expression profiles. The results presented elucidate that ZCC can perform better than LZ in identifying biologically relevant genes. The robustness of the findings are established on the given gene expression profiles as well as their noisy versions. The performance of these two techniques is demonstrated on publicly available yeast cell-cycle gene expression data. A possible explanation for the better performance of the ZCC over LZ complexity may be attributed to inherent cyclic patterns characteristic of the yeast cell-cycle experiment. Finally we discuss the biological relevance of new genes identified using ZCC not previously reported.