The Wavelet Transformation for Temporal Gene Expression Analysis
Abstract
A variety of high throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy for can be applied in the analysis of data sets of thousands of genes during cellular differentiation and response.
Cite
Text
Song et al. "The Wavelet Transformation for Temporal Gene Expression Analysis." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2005. doi:10.1109/CVPR.2005.540Markdown
[Song et al. "The Wavelet Transformation for Temporal Gene Expression Analysis." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2005.](https://mlanthology.org/cvprw/2005/song2005cvprw-wavelet/) doi:10.1109/CVPR.2005.540BibTeX
@inproceedings{song2005cvprw-wavelet,
title = {{The Wavelet Transformation for Temporal Gene Expression Analysis}},
author = {Song, Jiuzhou Z. and Duan, Kan-Ming and Surette, Michael G.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2005},
pages = {148},
doi = {10.1109/CVPR.2005.540},
url = {https://mlanthology.org/cvprw/2005/song2005cvprw-wavelet/}
}