一种新的生态监测数据质量评估方法——以CERN乔木生长数据为例

将数据可靠性作为有序变量进行分级,在理论上使数据可靠性与主要生态过程、次级生态过程、外部过程等数据源建立关联,构建了一种生态监测数据质量评估方法,提供了一个新的数据质量指数.它通过观察记录的合格率来估计数据集的质量,其检测结果包括了每一条数据的可靠性级别、标记为离群或错误数据的原因,以及完整数据集的质量指数值.将该方法应用于CERN的两个乔木生长数据集,发现该数据质量指数可以定量评估乔木生长数据集的质量.该方法为相关软件的开发提供了基础.     

Bailey＇s方法在生境选择研究中的应用

使用已经发表的4组生境选择数据计算Bonferroni和Bailey＇s置信区间,以比较分析Bailey＇s方法在研究“利用一可利用型”生境选择数据时的优势。用Bailey＇s方法对4组数据中的两组做出了与Bonferroni方法不同的统计推断,特别是当Bonferroni方法对其中的一组数据无法对动物是否具有生境选择性做出判断时,而Bailey＇s方法却发现了差异。如同Cherry（1996）指出的,Bonferroni方法是基于拟合优度卡方检验的,要求大样本的独立数据,当出现小样本时会因为连续性和一致性缺失而做出错误推断。Bailey＇s方法因为使用了连续性校正因子克服了这一缺陷,而且使用时无须进行拟合优度卡方检验,是一种简便可靠的生境选择数据分析方法。建议在推荐Bailey＇s方法的同时,应适当增大样本量、控制同时分析的生境类型数量,以控制分析时Ⅰ类和Ⅱ类的错误率。     

具有左截断、右删失寿命数据类型的生命表编制方法

左截断数据是一类特殊的寿命数据类型,其定义为一些动物个体并非在初始时间（出生或孵化）而是在某个时间（年龄）延滞之后才进入调查取样范围而收集到的一类寿命数据。传统的乘积限估计法只能处理寿终数据和右删失数据,对左截断数据则无能为力。本文提出一种对乘积限估计法改进方法,此种方法能同时处理寿终数据、右删失数据和左截断数据,从而有效地利用了左截断数据所含有的物种存活信息。     

遗传优化算法在基因数据分类中的应用

本文提出了一种基于遗传算法的基因微阵列数据特征提取方法。首先对原始数据进行标准化,然后利用方差分析方法对数据进行降低维数处理,最后利用遗传算法对数据进行优化。针对基因数据对遗传算子和适应度函数进行设置,优化数据集选取特征基因,得到较小的特征子集。为了验证选取的特征,利用样本划分法通过判别分析建立分类器进行判定。实验论证此方法具有理想的分类效果,算法稳定、效率高。     

结合蛋白质相互作用数据进行基因表达数据聚类

提出了一种蛋白质相互作用的相似性度量,将其与基因表达数据的相似性度量相结合,定义了一种融合的距离度量,并且将这种融合的距离度量用于改进现有的K—means聚类方法。经过实际数据的检验,改进后的K—means方法比常用的其它几种聚类方法具有更好的效果,说明结合蛋白质相互作用数据可以使得基因表达聚类的结果更有生物意义。     

基于多分类支持向量机的基因表达系列分析

基因表达系列分析(Serial analysis of gene expression,SAGE)是一种基因表达数据,反映了细胞内的动态变化。模式识别和可视化方法是分析SAGE数据的基本工具,但是由于缺乏描述数据的统计特性,传统的聚类分析技术不适用于SAGE数据的分析。本文提出了一种基于多分类和支持向量机的SAGE数据的分析法。经过对模拟数据和人类癌症SAGE数据的分析,基于径向基核函数的多分类支持向量机算法"一对一"(one-against-one,OAO)算法提供了比PoissonC和PoissonS更好的分类结果。     

Bailey’s方法在生境选择研究中的应用

使用已经发表的4组生境选择数据计算Bonferroni和Bailey’s置信区间,以比较分析Bailey’s方法在研究“利用－可利用型”生境选择数据时的优势。用Bailey’s方法对4组数据中的两组做出了与Bonferroni方法不同的统计推断,特别是当Bonferroni方法对其中的一组数据无法对动物是否具有生境选择性做出判断时,而Bailey’s方法却发现了差异。如同Cherry(1996)指出的,Bonferroni方法是基于拟合优度卡方检验的,要求大样本的独立数据,当出现小样本时会因为连续性和一致性缺失而做出错误推断。Bailey’s方法因为使用了连续性校正因子克服了这一缺陷,而且使用时无须进行拟合优度卡方检验,是一种简便可靠的生境选择数据分析方法。建议在推荐Bailey’s方法的同时,应适当增大样本量、控制同时分析的生境类型数量,以控制分析时I类和II类的错误率。     

综合ChIP-chip数据、基因敲除数据和表达谱数据重构基因调控网络

揭示生物体内在的调控机制是生物信息学的一项重要研究内容．各种高通量生物数据的涌现,为从基因组的尺度上重构基因调控网络提供了可能．由于单数据源仅能提供关于调控关系的片面信息且存在噪声,因此整合多种生物学数据的方法有望得到可靠性较高的调控网络．提出了一种综合ChIP-chip数据、knock out (敲除)数据和各种条件下的表达谱数据来推断调控关系的新方法．ChIP-chip数据和knock out 数据能分别提供转录因子和目标基因对关系的直接物理结合和功能关系的证据,这两类数据的整合有望获得较高的识别准确率．但这两类数据的重合性通常较低,基于共调控的基因通常具有较高的表达相似性这一假设,在一定程度上降低了这两类数据重合性较低所带来的影响．算法所识别的大部分调控关系都被YEASTRACT,高质量ChIP-chip数据和文献所验证,从而证明了该方法在调控关系的预测上具有较高的准确性．与其他方法的比较,也表明了该方法具有较高的预测性能．     

一种基于支持向量机的自适应肿瘤分类检测算法

根据肿瘤分类检测模型的特点,提出了一种新的算法,该算法结合使用了基因选择和数据抽取的有效方法,并在此基础上使用支持向量机对基因表达数据进行分类或者检测。其中乳腺癌的分类交叉验证结果由88．46％提高到100．0％,急性白血病的也由71．05％提高至100．0％。实验结果说明了这一方法的有效性,为在大量的基因表达数据中提高检测癌症的准确性提出了一种比较通用的方法。     

离子阱串联质谱仪质荷比测量误差统计分析

离子阱串联质谱仪是蛋白质组研究中一种高通量,高灵敏度的分析仪器。目前对影响离子阱质谱仪质荷比测量误差的因素和数据产出后系统误差的校正方法还没有系统的分析。利用两批蛋白质标准品的数据和统计方法分析了离子阱质谱仪质荷比测量误差的分布规律,对测量的质荷比和信号强度对测量误差的影响进行了分析。在此基础上,提出了一种对质谱数据进行系统误差再校正的方法和一种根据信号强度确定误差容限的模型。     

Is the Exact Test Better than the Asymptotic Test for Testing Marginal Homogeneity in 2 × 2 Tables?

McNemar test is commonly used to test for the marginal homogeneity in 2 × 2 contingency tables. McNemar test is an asymptotic test based either on standard normal distribution or on the chi‐square distribution. When the total sample size is small, an exact version of McNemar test is available based on the binomial probabilities. The example in the paper came from a clinical study to investigate the effect of epidermal growth factor for children who had microvillus inclusion diseases. There were only six observations available. The test results differ between the exact test and the asymptotic test. It is a common belief that with this small sample size the exact test be used. However, we claim that McNemar test performs better than the exact test even when the sample size is small. In order to investigate the performances of McNemar test and the exact test, we identify the parameters that affect the test results and then perform sensitivity analysis. In addition, through Monte Carlo simulation studies we compare the empirical sizes and powers of these tests as well as other asymptotic tests such as Wald test and the likelihood ratio test.     

Test of Random Subject Effects In Heteroskedastic Linear Models

A new test of random subject effects in linear regression models is presented. The test is robust against heteroskedasticity and its asymptotic distribution is derived under a sequence of local alternatives. The finite sample properties of the test are studied in a simulation experiment and an empirical example. The results presented show that the new test is to be preferred over earlier test proposed.     

A new location-scale test based on a combination of the ideas of Levene and Lepage

Lepage's test combines the Wilcoxon rank-sum and the Ansari-Bradley statistics. We propose to replace the latter statistic by a Wilcoxon rank-sum calculated after Levene's transformation. We use the medians for this transformation, i.e. absolute deviations from sample medians are calculated. The new location-scale test can be carried out as a permutation test based on permutations of the original observations, the Levene transformation has to be applied for each permutation in an intermediate step to calculate the test statistic. Simulations indicate that the new test can be more powerful than an O'Brien-type test and Lepage's test, the latter is the standard nonparametric location-scale test. The new test is illustrated using real data about colony sizes of yellow-eyed penguins and an SAS program to perform the test is freely available.     

A New Rank Test Against Ordered Alternatives in K-Sample Problems

The distribution-free test against ordered alternatives proposed by Jonckheere (1954) is based on the Kendall's rank correlation coefficient τ. A new rank test is proposed and illustrated with a numerical example. The proposed test is based on Spearman's σ and has similar functional structure as the Kruskal-Wallis test. A useful by-product is a test for departure from a trend.     

A goodness-of-fit test for capture-recapture model Mt under closure

A new, fully efficient goodness-of-fit test for the time-specific closed-population capture-recapture model Mt is presented. This test is based on the residual distribution of the capture history data given the maximum likelihood parameter estimates under model Mt, is partitioned into informative components, and is based on chi-square statistics. Comparison of this test with Leslie's test (Leslie, 1958, Journal of Animal Ecology 27, 84-86) for model Mt, using Monte Carlo simulations, shows the new test generally outperforms Leslie's test. The new test is frequently computable when Leslie's test is not, has Type I error rates that are closer to nominal error rates than Leslie's test, and is sensitive to behavioral variation and heterogeneity in capture probabilities. Leslie's test is not sensitive to behavioral variation in capture probabilities but, when computable, has greater power to detect heterogeneity than the new test.     

Statistical test for the comparison of samples from mutational spectra

The Monte Carlo estimate of the p value of the hypergeometric test is described and advocated for the testing of the hypothesis that different treatments induce the same mutational spectrum. The hypergeometric test is a generalization of Fisher's "exact" test for tables with more than two rows and two columns. Use of the test is demonstrated by the analysis of data from the characterization of nonsense mutations in the lacI gene of Escherichia coli. Unlike the chi-square test, the hypergeometric test remains valid when applied to sparse cross-classification tables. The hypergeometric test has the most discrimination power of any statistical test that could be employed routinely to compare samples from mutational spectra. Direct application of the hypergeometric test to large cross-classification tables is excessively computation intensive, but estimation of its p value via Monte Carlo techniques is practical.     

Mycoplasma: Growth Precipitation as a Serodiagnostic Method

A serological test is described which is designated the growth precipitation test and is based on the release of diffusible compounds from mycoplasma strains. During growth of mycoplasmas on solid substrates, precipitation lines are formed with antibodies from homologous antisera independent of heat-labile serum factors. The application of the test as a serodiagnostic tool was investigated by performing a complete serological cross-test with 10 mycoplasma strains. The test was compared with the growth inhibition test, the complement fixation test, and the indirect hemagglutination test. It is demonstrated that the growth precipitation test has a good degree of specificity. The species and subspecies used here showed cross-reactions between serologically closely related strains, but these heterologous reactions occurred only after longer incubation than the homologous reactions.     

An omnibus consistent adaptive percentile modified Wilcoxon rank sum test with applications in gene expression studies

Summary We present an adaptive percentile modified Wilcoxon rank sum test for the two‐sample problem. The test is basically a Wilcoxon rank sum test applied on a fraction of the sample observations, and the fraction is adaptively determined by the sample observations. Most of the theory is developed under a location‐shift model, but we demonstrate that the test is also meaningful for testing against more general alternatives. The test may be particularly useful for the analysis of massive datasets in which quasi‐automatic hypothesis testing is required. We investigate the power characteristics of the new test in a simulation study, and we apply the test to a microarray experiment on colorectal cancer. These empirical studies demonstrate that the new test has good overall power and that it succeeds better in finding differentially expressed genes as compared to other popular tests. We conclude that the new nonparametric test is widely applicable and that its power is comparable to the power of the Baumgartner‐Weiß‐Schindler test.     

Rank Tests for Complete Block Designs

In this article a general univariate K-sample rank test for complete block designs with proportional cell frequencies is derived. It is shown that the test statistic has under H₀ and for arbitrary scores asymptotically a X²-distribution with K — 1 degrees of freedom. Special cases of this test are the Kruskal-Wallis test and the Friedman test. The test is compared with the Benard-van-Elteren test, the Mack-Skillings test and a test proposed by Downton. Finally the application of the test is illustrated by two examples.     

A Modified Exact Test for 2 × 2 Contingency Tables

A modified exact test is proposed for 2×2 contingency tables. This test, which is based on a less conservative definition of the concept of significance (STONE, 1969) is compared with a modified form of Pearson's X² test and with Tocher's randomized exact (UMPU) test. The sizes of the new test lie near the nominal 0.05 levels while those of the X² test usually exceed the nominal level, sometimes by a factor of 2 or more. The power of the modified test is usually close to that of the UMPU test.