Detecting periodically patterns in unevenly spaced gene expression time series

被引：0
|
作者：
Xian, Jun [1 ]
Wang, Jinping [2 ]
Dai, Dao-Qing [3 ]
机构：
[1] Sun Yat Sen Zhongshan Univ, Dept Math, Guangzhou 510275, Guangdong, Peoples R China
[2] Ningbo Univ, Dept Math, Ningbo, Zhejiang, Peoples R China
[3] Sun Yat Sen Zhongshan Univ, Ctr Comp Vis, Guangzhou 510275, Guangdong, Peoples R China
来源：
2006 8TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, VOLS 1-4 | 2006年
基金：
中国博士后科学基金;
关键词：
spectrum estimation; periodically expressed gene; unevenly sampled data; Lomb-Scargle algorithm; signal reconstruction; B-spline;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Spectrum estimation is a popular method for identifying periodically expressed genes in microarray time series analysis. For unevenly sampled data, a common technique is applying the Lomb-Scargle algorithm. The performance of this method suffers from the effect of noise in the data. In this paper, we propose a new spectrum estimation algorithm for unevenly sampled data. The new method is based on signal reconstructing technic in aliased shift-invariant signal spaces and a direct spectrum estimation formula was derived based on B-spline basis. The new algorithm is very flexible and can reduce the effect of noise by adjusting: the order of B-spline basis. The test on simulated noisy signal and typical periodically expressed gene data showed our algorithm is accurate compared with Lomb-Scargle algorithm.
引用
收藏
页码：162 / +
页数：2
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