Extreme Value Distribution Based Gene Selection Criteria for Discriminant Microarray Data Analysis Using Logistic Regression

One important issue commonly encountered in the analysis of microarray data is to decide which and how many genes should be selected for further studies. For discriminant microarray data analyses based on statistical models, such as the logistic regression models, gene selection can be accomplished by a comparison of the maximum likelihood of the model given the real data, $\hat{L}(D|M)$, and the expected maximum likelihood of the model given an ensemble of surrogate data with randomly permuted label, $\hat{L}(D_0|M)$. Typically, the computational burden for obtaining $\hat{L}(D_0|M)$ is immense, often exceeding the limits of computing available resources by orders of magnitude. Here, we propose an approach that circumvents such heavy computations by mapping the simulation problem to an extreme-value problem. We present the derivation of an asymptotic distribution of the extreme-value as well as its mean, median, and variance. Using this distribution, we propose two gene selection criteria, and we apply them to two microarray datasets and three classification tasks for illustration.Comment: to be published in Journal of Computational Biology (2004

Computational Analysis of Molecular Interaction Networks Underlying Change of HIV-1 Resistance to Selected Reverse Transcriptase Inhibitors

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Towards a molecular definition of worker sterility: differential gene expression and reproductive plasticity in honey bees

We show that differences in the reproductive development of honey bee workers are associated with locus-specific changes to abundance of messenger RNA. Using a cross-fostering field experiment to control for differences related to age and environment, we compared the gene expression profiles of functionally sterile workers (wild-type) and those from a mutant strain in which workers are reproductively active (anarchist). Among the set of three genes that are significantly differentially expressed are two major royal jelly proteins that are up-regulated in wild-type heads. This discovery is consistent with sterile workers synthesizing royal jelly as food for developing brood. Likewise, the relative underexpression of these two royal jellies in anarchist workers is consistent with these workers’ characteristic avoidance of alloparental behaviour, in favour of selfish egg-laying. Overall, there is a trend for the most differentially expressed genes to be up-regulated in wild-type workers. This pattern suggests that functional sterility in honey bee workers may generally involve the expression of a suite of genes that effectively ‘switch’ ovaries off, and that selfish reproduction in honey bee workers, though rare, is the default developmental pathway that results when ovary activation is not suppressed

Conserved IKAROS-regulated genes associated with B-progenitor acute lymphoblastic leukemia outcome

Genetic alterations disrupting the transcription factor IKZF1 (encoding IKAROS) are associated with poor outcome in B lineage acute lymphoblastic leukemia (B-ALL) and occur in >70% of the high-risk BCR-ABL1+ (Ph+) and Ph-like disease subtypes. To examine IKAROS function in this context, we have developed novel mouse models allowing reversible RNAi-based control of Ikaros expression in established B-ALL in vivo. Notably, leukemias driven by combined BCR-ABL1 expression and Ikaros suppression rapidly regress when endogenous Ikaros is restored, causing sustained disease remission or ablation. Comparison of transcriptional profiles accompanying dynamic Ikaros perturbation in murine B-ALL in vivo with two independent human B-ALL cohorts identified nine evolutionarily conserved IKAROS-repressed genes. Notably, high expression of six of these genes is associated with inferior event-free survival in both patient cohorts. Among them are EMP1, which was recently implicated in B-ALL proliferation and prednisolone resistance, and the novel target CTNND1, encoding P120-catenin. We demonstrate that elevated Ctnnd1 expression contributes to maintenance of murine B-ALL cells with compromised Ikaros function. These results suggest that IKZF1 alterations in B-ALL leads to induction of multiple genes associated with proliferation and treatment resistance, identifying potential new therapeutic targets for high-risk disease

Series evaluation of Tweedie exponential dispersion model densities

Exponential dispersion models, which are linear exponential families with a dispersion parameter, are the prototype response distributions for generalized linear models. The Tweedie family comprises those exponential dispersion models with power mean-variance relationships. The normal, Poisson, gamma and inverse Gaussian distributions belong to the Tweedie family. Apart from these special cases, Tweedie distributions do not have density functions which can be written in closed form. Instead, the densities can be represented as infinite summations derived from series expansions. This article describes how the series expansions can be summed in an numerically efficient fashion. The usefulness of the approach is demonstrated, but full machine accuracy is shown not to be obtainable using the series expansion method for all parameter values. Derivatives of the density with respect to the dispersion parameter are also derived to facilitate maximum likelihood estimation. The methods are demonstrated on two data examples and compared with with Box-Cox transformations and extended quasi-likelihoood

An improved saddlepoint approximation based on the negative binomial distribution for the general birth process

Daniels (JAP 1982) gave a saddlepoint approximation to the probabilities of a general birth process. This paper gives an improved approximation which is only slightly more complex than Daniels' approximation and which has considerably reduced relative error in most cases. The new approximation has the characteristic that it is exact whenever the birth rates can be reordered into a linear increasing sequence