TY - GEN
T1 - A sensitivity analysis of microarray feature selection and classification under measurement noise
AU - Sontrop, Herman
AU - Van den Ham, René
AU - Moerland, Perry
AU - Reinders, Marcel J. T.
AU - Verhaegh, Wim F. J.
PY - 2009
Y1 - 2009
N2 - Microarray experiments typically generate data with a fairly high level of technical noise. Whereas this noise information is sometimes used in tests for differential expression and in clustering tasks, its effect on classification has remained underexposed. In this paper we assess the stability of microarray feature selection and classification under measurement noise. We do so by repeating the experiments many times, using perturbed expression measurements, based on reported uncertainty information. For a well-known study from the literature, the experiments show that the feature selection outcome can vary considerably, and that classification is quite unstable for 7 out of the 106 validation samples, in the sense that over 25% of the perturbations are not assigned to their original class. We also show that classification stability decreases when fewer genes are selected.
AB - Microarray experiments typically generate data with a fairly high level of technical noise. Whereas this noise information is sometimes used in tests for differential expression and in clustering tasks, its effect on classification has remained underexposed. In this paper we assess the stability of microarray feature selection and classification under measurement noise. We do so by repeating the experiments many times, using perturbed expression measurements, based on reported uncertainty information. For a well-known study from the literature, the experiments show that the feature selection outcome can vary considerably, and that classification is quite unstable for 7 out of the 106 validation samples, in the sense that over 25% of the perturbations are not assigned to their original class. We also show that classification stability decreases when fewer genes are selected.
UR - https://www.scopus.com/pages/publications/70349495489
U2 - 10.1109/GENSIPS.2009.5174352
DO - 10.1109/GENSIPS.2009.5174352
M3 - Conference contribution
SN - 9781424447619
T3 - 2009 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2009
BT - 2009 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2009
T2 - 2009 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2009
Y2 - 17 May 2009 through 21 May 2009
ER -