Characterizing the dynamic connectivity between genes by variable parameter regression and Kalman filtering based on temporal gene expression data.
BACKGROUND
One popular method for analyzing functional connectivity between genes is to cluster genes with similar expression profiles. The most popular metrics measuring the similarity (or dissimilarity) among genes include Pearson's correlation, linear regression coefficient and Euclidean distance. As these metrics only give some constant values, they can only depict a stationary connectivity between genes. However, the functional connectivity between genes usually changes with time. Here, we introduce a novel insight for characterizing the relationship between genes and find out a proper mathematical model, variable parameter regression and Kalman filtering to model it.


RESULTS
We applied our algorithm to some simulated data and two pairs of real gene expression data. The changes of connectivity in simulated data are closely identical with the truth and the results of two pairs of gene expression data show that our method has successfully demonstrated the dynamic connectivity between genes.


BACKGROUND
jiangtz@nlpr.ia.ac.cn.
Cui
Qinghua
Q
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, People's Republic of China.
Liu
Bing
B
Jiang
Tianzi
T
Ma
Songde
S
D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D025941:Protein Interaction Mapping / Q000379:methods; D011506:Proteins / Q000235:genetics* / Q000378:metabolism*; D012044:Regression Analysis; D015398:Signal Transduction / Q000502:physiology*; D013223:Statistics as Topic; D013598:Systems Theory; D013997:Time Factors
D011506:Proteins