Robust Detection of Periodic Patterns in Gene Expression Microarray Data using Topological Signal Analysis

被引：0
|
作者：
Emrani, Saba [1 ]
Krim, Hamid [1 ]
机构：
[1] North Carolina State Univ, Dept Elect & Comp Engn, Raleigh, NC 27695 USA
来源：
2014 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP) | 2014年
关键词：
Gene expression; microarrays; topological signal analysis; periodicity detection; biomedical signal processing; IDENTIFICATION;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we present a new approach for analyzing gene expression data that builds on topological characteristics of time series. Our goal is to identify cell cycle regulated genes in micro array dataset. We construct a point cloud out of time series using delay coordinate embeddings. Persistent homology is utilized to analyse the topology of the point cloud for detection of periodicity. This novel technique is accurate and robust to noise, missing data points and varying sampling intervals. Our experiments using Yeast Saccharomyces cerevisiae dataset substantiate the capabilities of the proposed method.
引用
收藏
页码：1406 / 1409
页数：4
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