环境微生物样品真菌群落BIOLOG分析方法

利用BIOLOG YT、FF微孔板分别考察了4个真菌群落代谢活性及群落间的代谢相似性,并与聚合酶链式反应-变性梯度凝胶电泳(PCR-DGGE)结构相似性分析对比试图探讨代谢相似性与结构相似性的内在联系,探讨了超低温冻存法作为样品保存手段对真菌群落特征BIOLOG分析结果的影响.结果表明:两种微孔板所反映的代谢相似性聚类分析结果完全不同, FF板所反映的代谢相似性聚类分析规律与PCR-DGGE提供的种群结构聚类分析规律一致;超低温冻存处理影响显著影响BIOLOG YT代谢活性(P = 0.023)和BIOLOG FF多样性指数(H')(P = 0.041),但对两种微孔板所反映的其它指数如代谢活性、丰富度指数(S)、多样性指数(H')分析结果均无显著性影响(P > 0.05).     

Phylogenetic and physiological characterization of organic waste-degrading bacterial communities

One of the greatest challenges in contemporary society is to reduce and treat household solid waste. The choice of inoculum to be used for start-up in reactors that degrade organic waste is critical to the success of organic waste treatment. In this study, the functional diversity, phylogenetic identification, and biogas production of bacterial communities from six inoculum sources were investigated. We used BIOLOG EcoPlates to evaluate the metabolic abilities of the bacterial communities, followed 16S rRNA gene sequence analysis to determine the phylogenetic affiliation of the bacteria responsible for carbon consumption. We observed great diversity in the physiological profiles. Of the six inocula tested, the sludge from an upflow anaerobic sludge blanket reactor (SRU) contained the most diverse, metabolically versatile microbiota and was characterized by the highest level of biogas production. By contrast, the sludge of the anaerobic lagoon (SAL) showed the worst performance in BIOLOG EcoPlates assays, but it exhibited the most diversity and generated the second largest amount of biogas. The bacterial isolates retrieved from BIOLOG EcoPlates were characterized as aerobic and/or facultative anaerobic, and were mainly Gram-negative. Phylogenetic analysis revealed that the isolates belonged to three major phyla: Proteobacteria, Firmicutes and Actinobacteria, represented by 33 genera. Proteobacteria exhibited the most diversity. The distribution of the bacterial genera differed considerably among the six inocula. Pseudomonas and Bacillus, which are able to degrade a wide range of proteins and carbohydrates, predominated in five of the six inocula. Analysis of the bacterial communities in this study indicates that both SRU and SAL microbiota are candidates for start-up inocula in anaerobic reactors. These start-up inocula must be studied further in order to identify their practical applications in degrading organic waste.     

Statistical exploration of variation in quantitative two-dimensional gel electrophoresis data

Two-dimensional gel electrophoresis is a major technique in global analysis at the protein level. This paper presents an examination of spot volume data from three gel sets with radioactively labeled yeast Saccharomyces cerevisiae proteins. A strong variance versus mean dependence in data was found to be stabilized by applying a shifted logarithmic transformation. However, transformed data showed a remaining substantial variance heterogeneity for different proteins. Furthermore, examination of studentized residuals revealed that transformed data were approximately normally distributed and that there were spatial correlations among the measurement errors in the gel.     

Logarithmic Transformation is Essential for Statistical Analysis of Fungicide EC50 Values

Half maximal (50%) effective concentration (EC50) values are widely used to express fungicide potency and sensitivity of plant pathogens. This study explored the necessity of logarithmic transformation for statistical analysis of EC50 values. The results demonstrated that without logarithmic transformation, none of the five sets of epoxiconazole EC50 data (n = 26–33) against Sclerotinia sclerotiorum fitted a normal distribution. But after logarithmic transformation, four of the five datasets became normally distributed. Of the five sets of pyraclostrobin EC50 data (n = 29–32), only one dataset fitted a normal distribution. After logarithmic transformation, four datasets became normally distributed. Logarithmic transformation transformed the heterogeneity of variance across the five sets of epoxiconazole EC50 data to homogeneity but failed to improve the heterogeneity of variance across the five sets of pyraclostrobin EC50 data. For 150 isolates' EC50 values to epoxiconazole and 153 isolates' EC50 values to pyraclostrobin, the intervals of arithmetic means ± standard deviations (SD) covered 85.3% and 90.2% of data points, respectively, whereas the intervals of geometric means (*) multiplied/divided by the multiplicative SD (S*) covered 69.3% and 70.9% of data points, respectively, which approximated the theoretical value of 68.3%. Distribution normality and homogeneity of variance are prerequisites for analysis of variance (anova ) and the two parameters could be improved by logarithmic transformation, therefore, power and efficiency of statistical tests on EC50 data will be greatly enhanced by this kind of transformation.     

On rethinking allometry

Analysis of sets of intra-species and inter-species allometric relationships reveals that the inter-species data generally fit an exponential model better than a linear model. The intra-species data seem equally suited to either model. Skewness of the data and the effect of logarithmic transformations on correlation coefficients are examined in the light of these findings. Inter-species data are approximately lognormally distributed and logarithmic transformations are necessary to produce linear relationships. As a consequence, correlation coefficients usually increase after logarithmic transformation of inter-species data.     

Binomial transformation applied to presence-absence community data

Community data is often transformed or standardized to meet the requirements and assumptions of multivariate analysis. While these methods are usually appropriate for abundance data, they are seldom applied to presence-absence data. Here, a method of transforming a binary matrix using the binomial probability is described. Number of trials (n), number of successes (x) and probability of success (p) are necessary to compute the binomial probability. Successes were defined as the number of sites where the species occurrence can be considered; trials were equal and greater than the number of successes. The actual occurrence of each species along the gradient was considered the probability of success. The Mantel statistic associated with the binomially transformed distance matrix and the distance matrix based on binary data were used to choose an appropriate binomial transformation. The chosen binomial transformation gave greater value to species indicating habitat typologies. Binomially transformed data rendered results closer to expectations.     

Evaluating the improvement in diagnostic utility from adding new predictors

Multiple diagnostic tests and risk factors are commonly available for many diseases. This information can be either redundant or complimentary. Combining them may improve the diagnostic/predictive accuracy, but also unnecessarily increase complexity, risks, and/or costs. The improved accuracy gained by including additional variables can be evaluated by the increment of the area under (AUC) the receiver‐operating characteristic curves with and without the new variable(s). In this study, we derive a new test statistic to accurately and efficiently determine the statistical significance of this incremental AUC under a multivariate normality assumption. Our test links AUC difference to a quadratic form of a standardized mean shift in a unit of the inverse covariance matrix through a properly linear transformation of all diagnostic variables. The distribution of the quadratic estimator is related to the multivariate Behrens–Fisher problem. We provide explicit mathematical solutions of the estimator and its approximate non‐central F‐distribution, type I error rate, and sample size formula. We use simulation studies to prove that our new test maintains prespecified type I error rates as well as reasonable statistical power under practical sample sizes. We use data from the Study of Osteoporotic Fractures as an application example to illustrate our method.     

Stochastic ICA contrast maximisation using OJA's nonlinear PCA algorithm

Independent Component Analysis (ICA) is an important extension of linear Principal Component Analysis (PCA). PCA performs a data transformation to provide independence to second order, that is, decorrelation. ICA transforms data to provide approximate independence up to and beyond second order yielding transformed data with fully factorable probability densities. The linear ICA transformation has been applied to the classical statistical signal-processing problem of Blind Separation of Sources (BSS), that is, separating unknown original source signals from a mixture whose mode of mixing is undetermined. In this paper it is shown that Oja's Nonlinear PCA algorithm performs a general stochastic online adaptive ICA. This analysis is corroborated with three simulations. The first separates unknown mixtures of original natural images, which have sub-Gaussian densities, the second separates linear mixtures of natural speech whose densities are super-Gaussian. Finally unknown mixtures of original images, which have both sub- and super-Gaussian densities are separated.     

Multiple-trait genome-wide association study based on principal component analysis for residual covariance matrix

Given the drawbacks of implementing multivariate analysis for mapping multiple traits in genome-wide association study (GWAS), principal component analysis (PCA) has been widely used to generate independent ‘super traits'' from the original multivariate phenotypic traits for the univariate analysis. However, parameter estimates in this framework may not be the same as those from the joint analysis of all traits, leading to spurious linkage results. In this paper, we propose to perform the PCA for residual covariance matrix instead of the phenotypical covariance matrix, based on which multiple traits are transformed to a group of pseudo principal components. The PCA for residual covariance matrix allows analyzing each pseudo principal component separately. In addition, all parameter estimates are equivalent to those obtained from the joint multivariate analysis under a linear transformation. However, a fast least absolute shrinkage and selection operator (LASSO) for estimating the sparse oversaturated genetic model greatly reduces the computational costs of this procedure. Extensive simulations show statistical and computational efficiencies of the proposed method. We illustrate this method in a GWAS for 20 slaughtering traits and meat quality traits in beef cattle.     

Further probes into quantitative aspects of competitive binding assays: allowance for effects of antigen multivalency in immunoassays

Effects of antigen multivalency on procedures for the analysis of immunoassays are examined on the basis of a theoretical expression developed in the context of quantitative affinity chromatography [Nichol, L. W., Ward, L. D., and Winzor, D. J. (1981) Biochemistry 20, 4856-4860] but which is also pertinent to antigen-antibody interactions that may be described in terms of a single intrinsic association constant. Quantitative relationships are generated which provide the basis for more rigorous logit-log analyses of radioimmunoassays in which the antigen is multivalent, and an additional, theoretically superior, linear transform of the basic expression is developed. Simulated binding data for a tetravalent antigen system are then used to demonstrate the curvilinearity of the conventional Scatchard plot for such a system despite the homogeneity of binding sites, and the application of the various linear transforms involving logarithmic functions. Of particular interest in that regard is the observation that the traditional logit-log analyses yield linear plots with the predicted slope of unity even though antigen univalence is an implicit assumption in their application. Results obtained in a solid-phase radioimmunoassay of triiodothyronine are then presented to provide, for that system at least, experimental justification of the above-mentioned assumption that the antibody-antigen interactions may be described in terms of a single intrinsic association constant. Finally, an enzyme-linked immunoassay of ferritin is used to illustrate the possibility that a linear Scatchard plot may be obtained with a multivalent antigen under conditions where steric factors restrict participation of an antigen molecule to a single interaction with immobilized antibody.     

Fingerprinting of Mixed Bacterial Strains and BIOLOG Gram-Negative (GN) Substrate Communities by Enterobacterial Repetitive Intergenic Consensus Sequence-PCR (ERIC-PCR)

PCR-based genomic fingerprinting by use of enterobacterial repetitive intergenic consensus primers (ERIC-PCR) was evaluated for its use in fingerprinting DNA of mixed Gram-negative bacterial strains and BIOLOG Gram-negative (GN) microplate substrate communities. ERIC-PCR fingerprints of six different pure bacterial strains and a combined mixture of the strains were compared with fingerprints obtained by two more established methods: amplified ribosomal DNA restriction analysis (ARDRA) and random amplified polymorphic DNA analysis (RAPD-PCR). The ERIC-PCR fingerprint of the mixed strains was highly reproducible and was more species-specific and representative of the individual strain fingerprints than the ARDRA and RAPD-PCR fingerprints, respectively. ERIC-PCR fingerprinting of model and rhizosphere BIOLOG GN substrate communities also provided clearly distinguishable fingerprints. Results of this study suggest that ERIC-PCR represents a rapid and highly discriminating method for fingerprinting DNA of mixed Gram-negative bacterial strains and BIOLOG GN substrate communities. Received: 11 September 1998 / Accepted: 29 October 1998     

Semiparametric transformation models for multiple continuous biomarkers in ROC analysis

Recent technological advances continue to provide noninvasive and more accurate biomarkers for evaluating disease status. One standard tool for assessing the accuracy of diagnostic tests is the receiver operating characteristic (ROC) curve. Few statistical methods exist to accommodate multiple continuous‐scale biomarkers in the framework of ROC analysis. In this paper, we propose a method to integrate continuous‐scale biomarkers to optimize classification accuracy. Specifically, we develop semiparametric transformation models for multiple biomarkers. We assume that unknown and marker‐specific transformations of biomarkers follow a multivariate normal distribution. Our models accommodate biomarkers subject to limits of detection and account for the dependence among biomarkers by including a subject‐specific random effect. We also propose a diagnostic measure using an optimal linear combination of the transformed biomarkers. Our diagnostic rule does not depend on any monotone transformation of biomarkers and is not sensitive to extreme biomarker values. Nonparametric maximum likelihood estimation (NPMLE) is used for inference. We show that the parameter estimators are asymptotically normal and efficient. We illustrate our semiparametric approach using data from the Endometriosis, Natural History, Diagnosis, and Outcomes (ENDO) study.     

Using uncorrelated discriminant analysis for tissue classification with gene expression data

The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high (in the thousands) compared to the number of data samples (in the tens or low hundreds); that is, the data dimension is large compared to the number of data points (such data is said to be undersampled). To cope with performance and accuracy problems associated with high dimensionality, it is commonplace to apply a preprocessing step that transforms the data to a space of significantly lower dimension with limited loss of the information present in the original data. Linear discriminant analysis (LDA) is a well-known technique for dimension reduction and feature extraction, but it is not applicable for undersampled data due to singularity problems associated with the matrices in the underlying representation. This paper presents a dimension reduction and feature extraction scheme, called uncorrelated linear discriminant analysis (ULDA), for undersampled problems and illustrates its utility on gene expression data. ULDA employs the generalized singular value decomposition method to handle undersampled data and the features that it produces in the transformed space are uncorrelated, which makes it attractive for gene expression data. The properties of ULDA are established rigorously and extensive experimental results on gene expression data are presented to illustrate its effectiveness in classifying tissue samples. These results provide a comparative study of various state-of-the-art classification methods on well-known gene expression data sets     

Functional mapping of dynamic traits with robust t-distribution

Functional mapping has been a powerful tool in mapping quantitative trait loci (QTL) underlying dynamic traits of agricultural or biomedical interest. In functional mapping, multivariate normality is often assumed for the underlying data distribution, partially due to the ease of parameter estimation. The normality assumption however could be easily violated in real applications due to various reasons such as heavy tails or extreme observations. Departure from normality has negative effect on testing power and inference for QTL identification. In this work, we relax the normality assumption and propose a robust multivariate t-distribution mapping framework for QTL identification in functional mapping. Simulation studies show increased mapping power and precision with the t distribution than that of a normal distribution. The utility of the method is demonstrated through a real data analysis.     

微生物生态研究中基于BIOLOG方法的数据分析

BIOLOG微平板法作为一种方便快速的微生物检验技术,已广泛应用于环境微生物检测,微生物生态研究等方面,发挥着越来越重要的作用。该方法可以获得关于微生物群落碳源利用能力的大量数据,反映出关于微生物活性的丰富信息。然而大量的数据也对解释和分析提出了挑战,分析了应用于BIOLOG产生数据的统计分析方法,对常用的AWCD值计算,多样性指数计算,主成分分析(PCA),聚类分析,相关、回归等方法深入探讨,阐述各自的功能、不足以及在应用中容易出现的问题。另外也对一些不常见的方法,如非参数多元分析(Non-Parametric version of MANOVA/Permutation version of MANOVA)、动力学参数分析、多元回归树、典范对应分析等也进行了讨论。通过对不同方法应用目标和原理的分析论述了各自优缺点,对微生物研究中基于BIOLOG方法数据分析的选择应用提供参考。     

Estimating data transformations in nonlinear mixed effects models

A routine practice in the analysis of repeated measurement data is to represent individual responses by a mixed effects model on some transformed scale. For example, for pharmacokinetic, growth, and other data, both the response and the regression model are typically transformed to achieve approximate within-individual normality and constant variance on the new scale; however, the choice of transformation is often made subjectively or by default, with adoption of a standard choice such as the log. We propose a mixed effects framework based on the transform-both-sides model, where the transformation is represented by a monotone parametric function and is estimated from the data. For this model, we describe a practical fitting strategy based on approximation of the marginal likelihood. Inference is complicated by the fact that estimation of the transformation requires modification of the usual standard errors for estimators of fixed effects; however, we show that, under conditions relevant to common applications, this complication is asymptotically negligible, allowing straightforward implementation via standard software.     

Testing hypotheses about interclass correlations from familial data

Testing problems concerning interclass correlations from familial data are considered in the case where the number of siblings varies among families. Under the assumption of multivariate normality, two test procedures are proposed for testing the hypothesis that an interclass correlation is equal to a specified value. To compare the properties of the tests, including a likelihood ratio test, Monte Carlo experiments are performed. Several test statistics are derived for testing whether two variables about a parent and child are uncorrelated. The proposed tests are compared with previous test procedures, using Monte Carlo simulation. A general procedure for finding confidence intervals for interclass correlations is also derived.     

Transformation of human and murine fibroblasts without viral oncoproteins

Murine embryo fibroblasts are readily transformed by the introduction of specific combinations of oncogenes; however, the expression of those same oncogenes in human cells fails to convert such cells to tumorigenicity. Using normal human and murine embryonic fibroblasts, we show that the transformation of human cells requires several additional alterations beyond those required to transform comparable murine cells. The introduction of the c-Myc and H-RAS oncogenes in the setting of loss of p53 function efficiently transforms murine embryo fibroblasts but fails to transform human cells constitutively expressing hTERT, the catalytic subunit of telomerase. In contrast, transformation of multiple strains of human fibroblasts requires the constitutive expression of c-Myc, H-RAS, and hTERT, together with loss of function of the p53, RB, and PTEN tumor suppressor genes. These manipulations permit the development of transformed human fibroblasts with genetic alterations similar to those found associated with human cancers and define specific differences in the susceptibility of human and murine fibroblasts to experimental transformation.     

Flexible informatics for linking experimental data to mathematical models via DataRail

MOTIVATION: Linking experimental data to mathematical models in biology is impeded by the lack of suitable software to manage and transform data. Model calibration would be facilitated and models would increase in value were it possible to preserve links to training data along with a record of all normalization, scaling, and fusion routines used to assemble the training data from primary results. RESULTS: We describe the implementation of DataRail, an open source MATLAB-based toolbox that stores experimental data in flexible multi-dimensional arrays, transforms arrays so as to maximize information content, and then constructs models using internal or external tools. Data integrity is maintained via a containment hierarchy for arrays, imposition of a metadata standard based on a newly proposed MIDAS format, assignment of semantically typed universal identifiers, and implementation of a procedure for storing the history of all transformations with the array. We illustrate the utility of DataRail by processing a newly collected set of approximately 22 000 measurements of protein activities obtained from cytokine-stimulated primary and transformed human liver cells. AVAILABILITY: DataRail is distributed under the GNU General Public License and available at http://code.google.com/p/sbpipeline/     

Mixed-model analysis of a censored normal distribution with reference to animal breeding

A mixed-model procedure for analysis of censored data assuming a multivariate normal distribution is described. A Bayesian framework is adopted which allows for estimation of fixed effects and variance components and prediction of random effects when records are left-censored. The procedure can be extended to right- and two-tailed censoring. The model employed is a generalized linear model, and the estimation equations resemble those arising in analysis of multivariate normal or categorical data with threshold models. Estimates of variance components are obtained using expressions similar to those employed in the EM algorithm for restricted maximum likelihood (REML) estimation under normality.