TY - GEN
T1 - Pareto-gamma statistic reveals global rescaling in transcriptomes of low and high aggressive breast cancer phenotypes
AU - Chua, Alvin L.S.
AU - Ivshina, Anna V.
AU - Kuznetsov, Vladimir A.
PY - 2006
Y1 - 2006
N2 - We propose a novel mixture probability model for the probability distribution function (PDF) of microarray signals, which comprises a noise and a signal component. The noise term, due to non-specific mRNA hybridization, is given by a lognormal distribution; and the true signal, from specific mRNA hybridization, is described by the generalized Pareto-gamma (GPG) function. The model, applied to expression data of 251 human breast cancer tumors on the Affymetrix microarray platform, yields accurate fits for all tumor samples. We observe that (i) high aggressive cancers have, in general, broader right tails in the GPG than low aggressive cancers; (ii) the exponent parameter value of the GPG distribution is not constant and correlates strongly with ∼4000 expressed genes and several "gold standard" clinical risk factors. These results can not be obtained from so-called "scale-free network" models. We conclude that an accurate parameterization of scale-dependent GPG function could provide robust prognostic benefits for cancer patients.
AB - We propose a novel mixture probability model for the probability distribution function (PDF) of microarray signals, which comprises a noise and a signal component. The noise term, due to non-specific mRNA hybridization, is given by a lognormal distribution; and the true signal, from specific mRNA hybridization, is described by the generalized Pareto-gamma (GPG) function. The model, applied to expression data of 251 human breast cancer tumors on the Affymetrix microarray platform, yields accurate fits for all tumor samples. We observe that (i) high aggressive cancers have, in general, broader right tails in the GPG than low aggressive cancers; (ii) the exponent parameter value of the GPG distribution is not constant and correlates strongly with ∼4000 expressed genes and several "gold standard" clinical risk factors. These results can not be obtained from so-called "scale-free network" models. We conclude that an accurate parameterization of scale-dependent GPG function could provide robust prognostic benefits for cancer patients.
UR - https://www.scopus.com/pages/publications/33750079016
U2 - 10.1007/11818564_7
DO - 10.1007/11818564_7
M3 - Conference contribution
SN - 3540374469
SN - 9783540374466
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 49
EP - 59
BT - Pattern Recognition in Bioinformatics - International Workshop, PRIB 2006, Proceedings
PB - Springer Verlag
T2 - International Workshop on Pattern Recognition in Bioinformatics, PRIB 2006
Y2 - 20 August 2006 through 20 August 2006
ER -