Detection of cell-cyclic elements in mis-sampled gene expression data.pdf (283.55 kB)
Detection of cell-cyclic elements in mis-sampled gene expression data using a robust Capon estimator
conference contribution
posted on 2010-02-05, 14:37 authored by Thomas Bowles, Andreas Jakobsson, Jonathon ChambersWe present a method for the estimation of possible cell cyclic
elements in mis-sampled microarray data. Accurate assessment
of the frequency content of microarray data gives insight
into genes which could be cell-cycle regulated. Cell
cycle regulation is one component of the complex network
of genetic regulatory processes and is especially relevant to
the study of cancer. As cDNA microarray experiments involve
human sampling of cell populations, slight variations
in the sampling times invariably occur. Here, we propose estimating
the frequency content of microarray data using the
recent robust Capon estimator, and formulate a suitable uncertainty
region to minimize over. The estimator is shown
to yield robust estimates with real microarray data and to
identify cell-cyclic genes that elude both the traditional Periodogram
and the Capon spectral estimator.
History
School
- Mechanical, Electrical and Manufacturing Engineering
Citation
BOWLES, T., JAKOBSSON, A. and CHAMBERS, J.A., 2004. Detection of cell-cyclic elements in mis-sampled gene expression data using a robust Capon estimator. IN: Proceedings of 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '04), Montreal, Quebec, 17-21 May, Vol. 5, pp. 17-20Publisher
© IEEEVersion
- AM (Accepted Manuscript)
Publication date
2004Notes
This is a conference paper [© IEEE]. It is also available at: http://ieeexplore.ieee.org/ Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.ISBN
0780384849Language
- en