Detection of cell-cyclic elements in mis-sampled gene expression data using a robust Capon estimator

Abstract

This item was submitted to Loughborough's Institutional Repository (https://dspace.lboro.ac.uk/) by the author and is made available under the following Creative Commons Licence conditions.For the full text of this licence, please go to: http://creativecommons.org/licenses/by-nc-nd/2.5/ DETECTION OF CELL-CYCLIC ELEMENTS IN MIS-SAMPLED GENE EXPRESSION DATA USING A ROBUST CAPON ESTIMATOR Thomas Bowles, Andreas Jakobsson and Jonathon ChambersCentre for Digital Signal Processing Research King's College London Strand, London, WC2R 2LS, U.K. ABSTRACTWe 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.

Reduced-dimension robust capon beamforming using Krylov-subspace techniques

“…Due to their widespread popularity and the fact that they have addressed the mismatch problem in several application areas (see, e.g., [22][23][24][25][26][27][28][29]), herein, we focus on using RCB techniques for alleviating the effects of ASV mismatch. Other notable recent methods for addressing the mismatch problem can be found in [30][31][32][33].…”

Detecting Periodic Genes from Irregularly Sampled Gene Expressions: A Comparison Study

“…Wichert et al [ 8 ] applied the traditional periodogram on various data sets. Bowles et al [ 9 ] compared Capon and robust Capon methods in terms of their ability to identify a predetermined frequency using evenly sampled data sets, under the assumption of a known period. Lichtenberg et al [ 10 ] compared [ 1 6 7 ] while proposing a new score by combining the periodicity and regulation magnitude.…”