TY - GEN
T1 - A new spectrum estimation method in unevenly sampling space
AU - Xian, Jun
AU - Wu, Shuan-Hu
AU - Liew, Alan
AU - Smith, David
AU - Yan, Hong
PY - 2006
Y1 - 2006
N2 - 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 shows our algorithm is accurate compared with Lomb-Scargle algorithm. © 2006 IEEE.
AB - 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 shows our algorithm is accurate compared with Lomb-Scargle algorithm. © 2006 IEEE.
KW - B-spline
KW - Eriodically expressed gene
KW - Lomb-scargle algorithm
KW - Signal reconstruction
KW - Spectrum estimation
KW - Unevenly sampled data
UR - https://www.scopus.com/pages/publications/33947227958
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-33947227958&origin=recordpage
U2 - 10.1109/ICMLC.2006.259011
DO - 10.1109/ICMLC.2006.259011
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 1424400619
SN - 9781424400614
VL - 2006
SP - 4273
EP - 4277
BT - Proceedings of the 2006 International Conference on Machine Learning and Cybernetics
T2 - 2006 International Conference on Machine Learning and Cybernetics
Y2 - 13 August 2006 through 16 August 2006
ER -