Clustering and classification problems in genetics through U-statistics

ABSTRACT

Genetic data are frequently categorical and have complex dependence structures that are not always well understood. For this reason, clustering and classification based on genetic data, while highly relevant, are challenging statistical problems. Here we consider a versatile U-statistics-based approach for non-parametric clustering that allows for an unconventional way of solving these problems. In this paper we propose a statistical test to assess group homogeneity taking into account multiple testing issues and a clustering algorithm based on dissimilarities within and between groups that highly speeds up the homogeneity test. We also propose a test to verify classification significance of a sample in one of two groups. We present Monte Carlo simulations that evaluate size and power of the proposed tests under different scenarios. Finally, the methodology is applied to three different genetic data sets: global human genetic diversity, breast tumour gene expression and Dengue virus serotypes. These applications showcase this statistical framework's ability to answer diverse biological questions in the high dimension low sample size scenario while adapting to the specificities of the different datatypes.     

An R package for testing goodness of fit: goft

This R package implements three types of goodness-of-fit tests for some widely used probability distributions where there are unknown parameters, namely tests based on data transformations, on the ratio of two estimators of a dispersion parameter, and correlation tests. Most of the considered tests have been proved to be powerful against a wide range of alternatives and some new ones are proposed here. The package's functionality is illustrated with several examples by using some data sets from the areas of environmental studies, biology and finance, among others.     

False discovery rates for large-scale model checking under certain dependence

In many scientific fields, it is interesting and important to determine whether an observed data stream comes from a prespecified model or not, particularly when the number of data streams is of large scale, where multiple hypotheses testing is necessary. In this article, we consider large-scale model checking under certain dependence among different data streams observed at the same time. We propose a false discovery rate (FDR) control procedure to check those unusual data streams. Specifically, we derive an approximation of false discovery and construct a point estimate of FDR. Theoretical results show that, under some mild assumptions, our proposed estimate of FDR is simultaneously conservatively consistent with the true FDR, and hence it is an asymptotically strong control procedure. Simulation comparisons with some competing procedures show that our proposed FDR procedure behaves better in general settings. Application of our proposed FDR procedure is illustrated by the StarPlus fMRI data.     

Statistical hypothesis testing for the shape of impulse response function

The problem of estimation of unknown response function of a time-invariant continuous linear system is considered. Integral sample input–output cross-correlogram is taken as an estimator of the response function. The inputs are supposed to be zero-mean stationary Gaussian process. A criterion on the shape of impulse response function is given. For this purpose, we apply a theory of square–Gaussian random processes and estimate the probability that supremum of square–Gaussian process exceeds the level specified by some function.     

非支配排序遗传算法(NSGA)算子分析

介绍了一种基于遗传算法的多目标进化算法-非支配排序遗传算法(NSGA)。并用NSGA对三个多目标优化问题进行了详尽的计算,对采用不同的算子和不同的算子取值进行了对照,初步得出了一组适用于不同类型问题的NSGA的遗传算子取值,对其他同类问题的计算提供了参考。     

Error-correcting pooling designs associated with the dual space of unitary space and ratio efficiency comparison

In this paper, we construct a d ^z -disjunct matrix with subspaces in a dual space of Unitary space , then give its several properties. As the smaller the ratio efficiency is, the better the pooling design is. We compare the ratio efficiency of this construction with others, such as the ratio efficiency of the construction of set, the general space and the dual space of symplectic space. In addition, we find it smaller under some conditions. Supported by NSF of the Education Department of Hebei Province (2007127) and NSF of Hebei Normal University (L2004B04).     

Hypothesis testing in mixture regression models

Summary.  We establish asymptotic theory for both the maximum likelihood and the maximum modified likelihood estimators in mixture regression models. Moreover, under specific and reasonable conditions, we show that the optimal convergence rate of n ^−1/4 for estimating the mixing distribution is achievable for both the maximum likelihood and the maximum modified likelihood estimators. We also derive the asymptotic distributions of two log-likelihood ratio test statistics for testing homogeneity and we propose a resampling procedure for approximating the p -value. Simulation studies are conducted to investigate the empirical performance of the two test statistics. Finally, two real data sets are analysed to illustrate the application of our theoretical results.     

Tests for cycling in a signalling pathway

Summary.  Cellular signalling pathways, mediating receptor activity to nuclear gene activation, are generally regarded as feed forward cascades. We analyse measured data of a partially observed signalling pathway and address the question of possible feed-back cycling of involved biochemical components between the nucleus and cytoplasm. First we address the question of cycling in general, starting from basic assumptions about the system. We reformulate the problem as a statistical test leading to likelihood ratio tests under non-standard conditions. We find that the modelling approach without cycling is rejected. Afterwards, to differentiate two different transport mechanisms within the nucleus, we derive the appropriate dynamical models which lead to two systems of ordinary differential equations. To compare both models we apply a statistical testing procedure that is based on bootstrap distributions. We find that one of both transport mechanisms leads to a dynamical model which is rejected whereas the other model is satisfactory.     

On the Screening of Large Numbers of Significance Tests

A brief review is given of procedures for the collective analysis of a large number of significance tests. A simple procedure previously supplied for isolating 'real' effects on the basis of a large number of significance tests is generalized to deal with two-sided tests and is also related more explicitly to the false discovery rate.     

Two Novel Evolutionary Formulations of the Graph Coloring Problem

We introduce two novel evolutionary formulations of the problem of coloring the nodes of a graph. The first formulation is based on the relationship that exists between a graph's chromatic number and its acyclic orientations. It views such orientations as individuals and evolves them with the aid of evolutionary operators that are very heavily based on the structure of the graph and its acyclic orientations. The second formulation, unlike the first one, does not tackle one graph at a time, but rather aims at evolving a program to color all graphs belonging to a class whose members all have the same number of nodes and other common attributes. The heuristics that result from these formulations have been tested on some of the Second DIMACS Implementation Challenge benchmark graphs, and have been found to be competitive when compared to the several other heuristics that have also been tested on those graphs.