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Aligning gene expression time series with time warping algorithms.

J Aach¹, G M Church

¹Department of Genetics and Lipper Center for Computational Genetics, Harvard Medical School, 200 Longwood Ave, Boston, MA 02115, USA.

Bioinformatics (Oxford, England)

|June 8, 2001

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Time warping algorithms align biological expression states across different time series, outperforming clustering. This method accurately maps corresponding states, even with noise, advancing biological time series analysis.

Area of Science:

Genomics
Systems Biology
Bioinformatics

Background:

Biological processes are increasingly studied using time series RNA expression data.
Common biological processes can vary in rate across experiments or individuals.
Mapping corresponding expression states between different time series is crucial.

Purpose of the Study:

To present and apply time warping algorithms for RNA and protein expression data.
To compare time warping with clustering for mapping time series states.
To analyze the impact of noise and sample size on time alignment quality.

Main Methods:

Implementation of two time warping algorithms: simple and interpolative.
Application of algorithms to published yeast RNA expression time series.

Related Experiment Videos

Development of graphics for visualizing alignment information.

Main Results:

Time warping demonstrates superiority over simple clustering for mapping corresponding time states.
The impact of statistical measurement noise and sample size on alignment quality is documented.
Issues related to statistical assessment of alignment quality using alignment scores are presented.

Conclusions:

Time warping is an effective method for aligning biological expression time series.
The study provides insights into factors affecting alignment accuracy.
Future directions include multiple time series alignment and applications to causality searches.