Illustrations and guidelines for selecting statistical methods for quantifying spatial pattern in ecological data

This paper aims to provide guidance to ecologists with limited experience in spatial analysis to help in their choice of techniques. It uses examples to compare methods of spatial analysis for ecological field data. A taxonomy of different data types is presented, including point‐ and area‐referenced data, with and without attributes. Spatially and non‐spatially explicit data are distinguished. The effects of sampling and other transformations that convert one data type to another are discussed; the possible loss of spatial information is considered.
Techniques for analyzing spatial pattern, developed in plant ecology, animal ecology, landscape ecology, geostatistics and applied statistics are reviewed briefly and their overlap in methodology and philosophy noted. The techniques are categorized according to their output and the inferences that may be drawn from them, in a discursive style without formulae. Methods are compared for four case studies with field data covering a range of types. These are: 1) percentage cover of three shrubs along a line transect; 2) locations and volume of a desert plant in a 1 ha area; 3) a remotely‐sensed spectral index and elevation from 10⁵ km² of a mountainous region; and 4) land cover from three rangeland types within 800 km² of a coastal region. Initial approaches utilize mapping, frequency distributions and variance‐mean indices. Analysis techniques we compare include: local quadrat variance, block quadrat variance, correlograms, variograms, angular correlation, directional variograms, wavelets, SADIE, nearest neighbour methods, Ripley's (t), and various landscape ecology metrics.
Our advice to ecologists is to use simple visualization techniques for initial analysis, and subsequently to select methods that are appropriate for the data type and that answer their specific questions of interest. It is usually prudent to employ several different techniques.     

PTR-ToF-MS and data mining methods: a new tool for fruit metabolomics

Proton Transfer Reaction-Mass Spectrometry (PTR-MS) in its recently developed implementation based on a time-of-flight mass spectrometer (PTR-ToF-MS) has been evaluated as a possible tool for rapid non-destructive investigation of the volatile compounds present in the metabolome of apple cultivars and clones. Clone characterization is a cutting-edge problem in technical management and royalty application, not only for apple, aiming at unveiling real properties which differentiate the mutated individuals. We show that PTR-ToF-MS coupled with multivariate and data mining methods may successfully be employed to obtain accurate varietal and clonal apple fingerprint. In particular, we studied the VOC emission profile of five different clones belonging to three well known apple cultivars, such as ??Fuji??, ??Golden Delicious?? and ??Gala??. In all three cases it was possible to set classification models which can distinguish all cultivars and some of the clones considered in this study. Furthermore, in the case of ??Gala?? we also identified estragole and hexyl 2-methyl butanoate contributing to such clone characterization. Beside its applied relevance, no data on the volatile profiling of apple clones are available so far, our study indicates the general viability of a metabolomic approach for volatile compounds in fruit based on rapid PTR-ToF-MS fingerprinting.     

MARAN: normalizing micro-array data

SUMMARY: MARAN is a web-based application for normalizing microarray data. MARAN comprises a generic ANOVA model, an option for Loess fitting prior to ANOVA analysis, and a module for selecting genes with significantly changing expression. AVAILABILITY: http://www.esat.kuleuven.ac.be/maran/.     

Novel methods for assessing fish blood biochemical data

The purpose of this study was the identification and quantification of biochemical parameters over a 1‐year cycle and to provide a detailed picture of seasonal changes in plasma metabolites and enzymes. Using the novel methods of machine learning techniques, the authors created and generated for the first time comprehensible classification models for exploring the importance of blood chemistry parameters, strength, mutual interactions or dependencies, and reliability of particular parameters within the seasonal groups.     

Evaluation of statistical methods for avoidance data of schooling fish

Concentrations of heavy metals blends avoided by schools of fathead minnows and alkaline pH levels avoided by schools of bluegill sunfish, fathead minnows, golden shiners, and rainbow trout were determined in a boundary layer avoidance chamber. Parameters measured were residence time, activity, and sequential fish location counts. Data were evaluated using linear, quadratic, and polynomial regression, log₁₀ transformations, analysis of variance, covariance analysis, Duncan's multiple range test, and Hochberg's GT2 test. The best methods of analysis are quadratic regression and covariance analysis.     

A new statistical approach for assessing similarity of species composition with incidence and abundance data

The classic Jaccard and Sørensen indices of compositional similarity (and other indices that depend upon the same variables) are notoriously sensitive to sample size, especially for assemblages with numerous rare species. Further, because these indices are based solely on presence–absence data, accurate estimators for them are unattainable. We provide a probabilistic derivation for the classic, incidence‐based forms of these indices and extend this approach to formulate new Jaccard‐type or Sørensen‐type indices based on species abundance data. We then propose estimators for these indices that include the effect of unseen shared species, based on either (replicated) incidence‐ or abundance‐based sample data. In sampling simulations, these new estimators prove to be considerably less biased than classic indices when a substantial proportion of species are missing from samples. Based on species‐rich empirical datasets, we show how incorporating the effect of unseen shared species not only increases accuracy but also can change the interpretation of results.     

基因芯片筛选差异表达基因方法比较

摘要: 使用计算机模拟数据和真实的芯片数据, 对8种筛选差异表达基因的方法进行了比较分析, 旨在比较不同方法对基因芯片数据的筛选效果。模拟数据分析表明, 所使用的8种方法对均匀分布的差异表达基因有很好的识别、检出作用。算法方面, SAM和Wilcoxon秩和检验方法较好; 数据分布方面, 正态分布的识别效果较好, 卡方分布和指数分布的识别效果较差。杨树cDNA芯片分析表明, SAM、Samroc和回归模型方法相近, 而Wilcoxon秩和检验方法与它们有较大差异。     

OPTIMAS-DW: A comprehensive transcriptomics,metabolomics, ionomics,proteomics and phenomics data resource for maize

Background

The wild barley Hordeum chilense fulfills some requirements for being a useful tool to investigate the endosperm yellow pigment content (YPC) in the Triticeae including its diploid constitution, the availability of genetic resources (addition and deletion stocks and a high density genetic map) and, especially, its high seed YPC not silenced in tritordeums (amphiploids derived from H. chilense and wheat). Thus, the aim of this work was to test the utility of the H. chilense genome for investigating the YPC in the Triticeae.

Results

Twelve genes related to endosperm carotenoid content and/or YPC in grasses (Dxr, Hdr [synonym ispH], Ggpps1, Psy2, Psy3, Pds, Zds, e-Lcy, b-Lcy, Hyd3, Ccd1 and Ppo1) were identified, and mapped in H. chilense using rice genes to identify orthologs from barley, wheat, sorghum and maize. Macrocolinearity studies revealed that gene positions were in agreement in H. vulgare and H. chilense. Additionally, three main regions associated with YPC were identified in chromosomes 2H^ch, 3H^ch and 7H^ch in H. chilense, the former being the most significant one.

Conclusions

The results obtained are consistent with previous findings in wheat and suggest that Ggpps1, Zds and Hyd3 on chromosome 2H^ch may be considered candidate genes in wheat for further studies in YPC improvement. Considering the syntenic location of carotenoid genes in H. chilense, we have concluded that the H^ch genome may constitute a valuable tool for YPC studies in the Triticeae.

     

数据点选择与参数估计方法对杉木人工林自疏边界线的影响

自疏边界线是指植物种群发生密度依赖死亡时种群最大收获量的上边界线。已有研究由于在拟合自疏边界线的过程中对数据点的选择和参数估计的方法存在诸多的差异,进而导致产生对自疏法则的争议。该研究采用26年生杉木(Cunninghamia lanceolata)人工林的定位观测数据,对视觉法、死亡率法、等距区间法和相对密度法等4种数据点选择方法以及最小二乘法、降维分析法、分位数回归法和随机边界方程等4种参数拟合方法进行对比分析,以探寻客观选择自疏拟合数据和正确拟合方法的途径。比较4种不同的数据选择方法得出:视觉法具有较强的主观性;对于没有发生非密度依赖死亡的林分,死亡率法可以准确地确定林分自疏的起始点;等距区间法可以减少非密度依赖死亡的影响,得到的数据点能充分反映林分的自疏过程;相对密度法可以保证临界密度阈值以上的数据点拟合林分自疏边界线的有效性,并能排除非密度依赖死亡的影响。比较分析4种不同的拟合方法发现:最小二乘法和降维分析法拟合的林分自疏边界线均从实测数据"中心"穿过,与林分自疏边界线为林分收获量上边界线的涵义不相符合,无法真实反映林分的自疏进程;分位数回归和随机边界方程的拟合结果均与实测数据一致,能够较为准确地反映林分自疏的真实过程,但二者的统计推断要求都比较严格。分位数值的正确选取和残差足够小且趋于0,是分位数回归法和随机边界方程能否正确反映林分自疏动态的前提。     

Can metabolomics be used for assessing nutritive-dependent human diseases?

Metabolomic approaches are taking place within emerging technologies that share the focus to unravel unexpected effects in classical biological experiments, for example mutant/wild type comparisons. This review highlights the possibility of metabolomics going beyond this stage and comparing the onset and the pathogenesis of complex diseases, such as type 2 diabetes mellitus, in large scale projects that might reach out to compare different populations and different nutritional diets and interventions. The steps of validation that are needed to pursue such projects are described, and the range of analytical and computational approaches that can be utilized are discussed. Using type 2 diabetes mellitus, it is emphasized that there is a high economic and scientific need to reliably diagnose and predict metabolic diseases in a cost effective manner. This revised version was published online in June 2006 with corrections to the Cover Date.     

Current status of macroinvertebrate methods used for assessing the quality of European marine waters: implementing the Water Framework Directive

Ephemeral ponds are doubly insular habitats in that they are discrete in time as well as in space. Predicting species richness based on pond size has been attempted using measures of both spatial extent and habitat duration, but habitat qualities alone only drive community composition under the species-sorting metacommunity paradigm. We tested the hypothesis that community composition in temporary ponds is driven by species sorting due to pond duration. In order to eliminate bias due to under-sampling, we sampled 34 pools distributed among 3 complexes in every ponding event over a period of six years, and identified every individual for the microcrustacean taxa. Our data were consistent with most of the predictions of the species-sorting hypothesis. There was a clear pattern of difference between ponds in species richness as well as higher species richness in years with higher rainfall. The set of crustacean species that we found in the pools was highly significantly nested across the region, but not necessarily within localized groups of ponds (complexes). We also found differences in community composition among complexes. Pond depth was the best predictor of species richness when data were summarized over the whole study, but in one year with unusually high rainfall, pond area and hydroperiod were significant but depth was not. We did find some species in all ponds. It is likely that given their short development time, ponds do not differ in habitat quality for these few species. These results taken together emphasize the variability inherent in ephemeral pond ecology, with detectable differences in crustacean communities and the factors influencing them between years as well as between ponds, and at scales of meters as well as kilometers. Although our data provide further evidence that species sorting on pond permanence is an important factor structuring temporary pond crustacean communities, our assumption that dispersal is not limiting still needs to be tested.

CLUSTAG: hierarchical clustering and graph methods for selecting tag SNPs

SUMMARY: Cluster and set-cover algorithms are developed to obtain a set of tag single nucleotide polymorphisms (SNPs) that can represent all the known SNPs in a chromosomal region, subject to the constraint that all SNPs must have a squared correlation R2>C with at least one tag SNP, where C is specified by the user. AVAILABILITY: http://hkumath.hku.hk/web/link/CLUSTAG/CLUSTAG.html CONTACT: mng@maths.hku.hk.     

A review of some statistical methods for covariance analysis of categorical data

Three general methods for covariance analysis of categorical data are reviewed and applied to an example from a clinical trial in rheumatoid arthritis. The three methods considered are randomization-model nonparametric procedures, maximum likelihood logistic regression, and weighted least squares analysis of correlated marginal functions. A fourth heuristic approach, the unweighted linear model analysis, is an approximate procedure but it is easy to implement. The assumptions and statistical issues for each method are discussed so as to emphasize philosophical differences between their rationales. Attention is given to computational differences, but it is shown that the methods lead to similar results for analogous problems. It is argued that the essential differences between the methods lie in their underlying assumptions and the generality of the conclusions which may be drawn.     

Gene mapping in the 20th and 21st centuries: statistical methods, data analysis, and experimental design

In the 20th century geneticists began to unravel some of the simpler aspects of the etiology of inherited diseases in humans. The theory of linkage analysis was developed and applied long before the advent of molecular biology, but only the technological advances of the second half of the 20th century made large-scale gene mapping with a dense genome-spanning set of markers a reality. More recently, the primary topic of interest has shifted from simple Mendelian diseases, for which genotypes of some gene are the cause of disease, to more complex diseases, for which genotypes of some set of genes together with environmental factors merely alter the probability that an individual gets the disease, although individual factors are typically insufficient to cause the disease outright. To this end, a great deal of dogma has evolved about the best way to skin this cat, although to date success has been minimal with any approach. We postulate that the main reason for this is a lack of attention to experimental design. Once the data have been ascertained, the most powerful statistical methods will not be able to salvage an inappropriately designed study (Andersen 1990). Each phenotype and/or population mandates its own individually tailored study design to maximize the chances of successful gene mapping. We suggest that careful consideration of the available data from real genotype-phenotype correlation studies (as opposed to oversimplified theoretically tractable models), and the practical feasibility of different ascertainment schemes dictate how one should proceed. In this review we review the theory and practice of gene mapping at the close of the 20th century, showing that most methods of linkage and linkage disequilibrium analysis are similar in a fundamental sense, with the differences being related more to study design and ascertainment than to technical details of the underlying statistical analysis. To this end, we propose a new focus in the field of statistical genetics that more explicitly highlights the primacy of study design as the means to increase power for gene mapping.     

POMAShiny: A user-friendly web-based workflow for metabolomics and proteomics data analysis

Metabolomics and proteomics, like other omics domains, usually face a data mining challenge in providing an understandable output to advance in biomarker discovery and precision medicine. Often, statistical analysis is one of the most difficult challenges and it is critical in the subsequent biological interpretation of the results. Because of this, combined with the computational programming skills needed for this type of analysis, several bioinformatic tools aimed at simplifying metabolomics and proteomics data analysis have emerged. However, sometimes the analysis is still limited to a few hidebound statistical methods and to data sets with limited flexibility. POMAShiny is a web-based tool that provides a structured, flexible and user-friendly workflow for the visualization, exploration and statistical analysis of metabolomics and proteomics data. This tool integrates several statistical methods, some of them widely used in other types of omics, and it is based on the POMA R/Bioconductor package, which increases the reproducibility and flexibility of analyses outside the web environment. POMAShiny and POMA are both freely available at https://github.com/nutrimetabolomics/POMAShiny and https://github.com/nutrimetabolomics/POMA, respectively.     

Supertree bootstrapping methods for assessing phylogenetic variation among genes in genome-scale data sets

Nonparamtric bootstrapping methods may be useful for assessing confidence in a supertree inference. We examined the performance of two supertree bootstrapping methods on four published data sets that each include sequence data from more than 100 genes. In "input tree bootstrapping," input gene trees are sampled with replacement and then combined in replicate supertree analyses; in "stratified bootstrapping," trees from each gene's separate (conventional) bootstrap tree set are sampled randomly with replacement and then combined. Generally, support values from both supertree bootstrap methods were similar or slightly lower than corresponding bootstrap values from a total evidence, or supermatrix, analysis. Yet, supertree bootstrap support also exceeded supermatrix bootstrap support for a number of clades. There was little overall difference in support scores between the input tree and stratified bootstrapping methods. Results from supertree bootstrapping methods, when compared to results from corresponding supermatrix bootstrapping, may provide insights into patterns of variation among genes in genome-scale data sets.     

Sampling methods for NMR-based metabolomics of Staphylococcus aureus

To select an appropriate sampling method for comparison of metabolite profiles between planktonic and biofilm Staphylococcus aureus using NMR techniques, we evaluated three methods: quenching-centrifugation (QC), filtration-quenching (FQ) and filtration-quenching-lyophilization (FQL). We found differences in metabolite loss, yield, reproducibility and metabolite profile. QC caused severe metabolite leakage and possible decomposition of nucleotides. FQ achieved high yields and reproducibility, although it had the disadvantages of long filtration and rinse times before quenching. FQL resulted in a loss of a few metabolites and a lower yield due to lyophilization. Although the biomarkers discovered by each method were nearly the same and seemed insensitive to technical variances, we conclude that FQ is the most appropriate sampling method because of its high yield and reproducibility.     

Toward supportive data collection tools for plant metabolomics

Over recent years, a number of initiatives have proposed standard reporting guidelines for functional genomics experiments. Associated with these are data models that may be used as the basis of the design of software tools that store and transmit experiment data in standard formats. Central to the success of such data handling tools is their usability. Successful data handling tools are expected to yield benefits in time saving and in quality assurance. Here, we describe the collection of datasets that conform to the recently proposed data model for plant metabolomics known as ArMet (architecture for metabolomics) and illustrate a number of approaches to robust data collection that have been developed in collaboration between software engineers and biologists. These examples also serve to validate ArMet from the data collection perspective by demonstrating that a range of software tools, supporting data recording and data upload to central databases, can be built using the data model as the basis of their design.