Co-inertia analysis: an alternative method for studying species–environment relationships

• 1 Methods used for the study of species–environment relationships can be grouped into: (i) simple indirect and direct gradient analysis and multivariate direct gradient analysis (e.g. canonical correspondence analysis), all of which search for non-symmetric patterns between environmental data sets and species data sets; and (ii) analysis of juxtaposed tables, canonical correlation analysis, and intertable ordination, which examine species–environment relationships by considering each data set equally. Different analytical techniques are appropriate for fulfilling different objectives.
• 2 We propose a method, co-inertia analysis, that can synthesize various approaches encountered in the ecological literature. Co-inertia analysis is based on the mathematically coherent Euclidean model and can be universally reproduced (i.e. independently of software) because of its numerical stability. The method performs simultaneous analysis of two tables. The optimizing criterion in co-inertia analysis is that the resulting sample scores (environmental scores and faunistic scores) are the most covariant. Such analysis is particularly suitable for the simultaneous detection of faunistic and environmental features in studies of ecosystem structure.
• 3 The method was demonstrated using faunistic and environmental data from Friday (Freshwater Biology 18, 87-104, 1987). In this example, non-symmetric analyses is inappropriate because of the large number of variables (species and environmental variables) compared with the small number of samples.
• 4 Co-inertia analysis is an extension of the analysis of cross tables previously attempted by others. It serves as a general method to relate any kinds of data set, using any kinds of standard analysis (e.g. principal components analysis, correspondence analysis, multiple correspondence analysis) or between-class and within-class analyses.

     

A new analysis method for the comparison of 2 temporal patterns,with indices of association

A new method for evaluating the degree of association between the behavioural patterns of 2 individuals is described and illustrated. It is proposed discrimination should be made between the unit for sampling (sample interval) and the unit for analysis. The length of the sample interval affects analysis accuracy, while the length of the analysis unit affects the analysis scale. A new “moving analysis unit (MAU) method” is proposed to divide an observation period into analysis units. The MAU method, with various lengths of analysis units, allows for various aspects of interaction between 2 individuals. As a similarity coefficient between the 2 temporal patterns, Iwao's ω (Iwao 1977) is proposed. Investigation of nonparametric confidence intervals of ω is performed with the bootstrap (Efron 1979), which is also applied for obtaining the null hypothesis distribution of ω. The MAU method, ω and the bootstrap are applied to the analysis of both insect and human communication.     

PathAct: a novel method for pathway analysis using gene expression profiles

We developed PathAct, a novel method for pathway analysis to investigate the biological and clinical implications of the gene expression profiles. The advantage of PathAct in comparison with the conventional pathway analysis methods is that it can estimate pathway activity levels for individual patient quantitatively in the form of a pathway-by-sample matrix. This matrix can be used for further analysis such as hierarchical clustering and other analysis methods. To evaluate the feasibility of PathAct, comparison with frequently used gene-enrichment analysis methods was conducted using two public microarray datasets. The dataset #1 was that of breast cancer patients, and we investigated pathways associated with triple-negative breast cancer by PathAct, compared with those obtained by gene set enrichment analysis (GSEA). The dataset #2 was another breast cancer dataset with disease-free survival (DFS) of each patient. Contribution by each pathway to prognosis was investigated by our method as well as the Database for Annotation, Visualization and Integrated Discovery (DAVID) analysis. In the dataset #1, four out of the six pathways that satisfied p < 0.05 and FDR < 0.30 by GSEA were also included in those obtained by the PathAct method. For the dataset #2, two pathways (“Cell Cycle” and “DNA replication”) out of four pathways by PathAct were commonly identified by DAVID analysis. Thus, we confirmed a good degree of agreement among PathAct and conventional methods. Moreover, several applications of further statistical analyses such as hierarchical cluster analysis by pathway activity, correlation analysis and survival analysis between pathways were conducted.     

Applying pattern recognition methods to analyze the molecular properties of a homologous series of nitrogen mustard agents

The purpose of this research was to analyze the pharmacological properties of a homologous series of nitrogen mustard (N-mustard) agents formed after inserting 1 to 9 methylene groups (-CH₂-) between 2-N(CH₂CH₂Cl)₂ groups. These compounds were shown to have significant correlations and associations in their properties after analysis by pattern recognition methods including hierarchical classification, cluster analysis, nonmetric multi-dimensional scaling (MDS), detrended correspondence analysis, K-means cluster analysis, discriminant analysis, and self-organizing tree algorithm (SOTA) analysis. Detrended correspondence analysis showed a linear-like association of the 9 homologs, and hierarchical classification showed that each homolog had great similarity to at least one other member of the series—as did cluster analysis using paired-group distance measure. Nonmetric multi-dimensional scaling was able to discriminate homologs 2 and 3 (by number of methylene groups) from homologs 4, 5, and 6 as a group, and from homologs 7, 8, and 9 as a group. Discriminant analysis, K-means cluster analysis, and hierarchical classification distinguished the high molecular weight homologs from low molecular weight homologs. As the number of methylene groups increased the aqueous solubility decreased, dermal permeation coefficient increased, Log P increased, molar volume increased, parachor increased, and index of refraction decreased. Application of pattern recognition methods discerned useful interrelationships within the homologous series that will determine specific and beneficial clinical applications for each homolog and methods of administration.     

Determination of the numerical density of perforated synapses in rat neocortex

Summary The numerical density and frequency of perforated synapses in the molecular layer of rat parietal cortex have been determined using 4 procedures in an attempt to overcome problems associated with the size and complex three-dimensional shape of perforated synapses. The following procedures were compared: A, single-section analysis; B, adjacent-section analysis; C, semi-serial-section analysis; and D, complete serial-section analysis. All procedures made use of an unbiased counting rule.Estimates of the numerical density of perforated synapses ranged from 0.06 to 0.27×10⁹ mm^-3, and that of all synapses (non-perforated and perforated) from 1.88 to 2.50×10⁹ mm^-3. The frequency of perforated synapses varied from 4.5 to 18.0%. Procedures B (adjacent-section analysis) and D (complete serial-section analysis), neither of which utilize assumptions regarding the shape of synapses, produced comparable results (numerical density of perforated synapses 0.19–0.27×10⁹ mm^-3, and of all synapses 2.24–2.45×10⁹ mm^-3; frequency of perforated synapses 8.6–10.9%). The frequency of perforated synapses appeared to be underestimated by procedure A (single section analysis; 4.5%) and overestimated by C (semi-serial section analysis; 18%).It is concluded that adjacent-section analysis is the most efficient and effective procedure for determining the numerical density and frequency of complex particles, such as perforated synapses. There is, however, no significant difference in the performance of this procedure compared with that of single-section analysis, for determining the numerical density of synapses in general. Nevertheless, inherent problems of bias within the single-section procedure make the adjacent section method the procedure of choice.     

Philosophical Aspects of Measurements, Equations and Inferences in Plant Growth Studies

Growth analysis is based on equations that are ‘identities’because they are algebraically self-evident, whereas the moredeterministic models of plant growth are based on ‘conditionalequations’ that represent quantitative hypotheses. Growthanalytical studies consequently focus on the values of the componentsand not on the validity of the equations, whereas ‘validation’is a prime concern for other growth models. Implications ofmeasurement theory, of dependent and independent variables andof compensating components arise in the use of both types ofequation for quantifying growth. There is now available a rangeof approaches, from traditional growth analysis, through variousdevelopments of growth analysis including light conversion analysis,to complex mechanistic models of growth. Growth analysis, yield component analysis, light conversion analysis, mathematical models, measurement theory, derived quantities, independent variables, equations of growth     

Effectiveness of fine root fingerprinting as a tool to identify plants of the Alps: Results of a preliminary study

To identify plants of the Alps through analysis of their roots is currently extremely difficult when using traditional identification methods such as dichotomous keys and/or illustrated atlases. Besides genetic analysis, other analytical methods, such as chromatographic analysis, could also be useful for root identification. Chromatographic fingerprints of root extracts of six species (Betula pendula, Picea abies, Fagus sylvatica, Larix decidua, Fraxinus excelsior and Corylus avellana) were analyzed in order to understand whether these species have a chromatographic fingerprint that identifies them, and hence to ascertain whether they can be identified by applying the method of analysis presented below. One hundred and sixty-two root samples were collected in various areas of the Alps and subjected to high-performance liquid chromatography (HPLC) analysis. Multivariate analysis techniques (e.g. cluster analysis) were employed for statistical analysis of chromatographic fingerprints. This study revealed that the chromatographic fingerprints of birch, spruce and larch samples were similar and that the method can therefore clearly identify the respective species. Instead, chromatographic fingerprint samples of beech, hazel and ash presented greater variability. Research proposals based on the results obtained in this study were also developed in order to implement and facilitate studies regarding plant roots.     

Quantitative and chemical fingerprint analysis for quality control of Rhizoma Coptidischinensis based on UPLC-PAD combined with chemometrics methods

To control the quality of Rhizoma Coptidis, a method based on ultra performance liquid chromatography with photodiode array detector (UPLC-PAD) was developed for quantitative analysis of five active alkaloids and chemical fingerprint analysis. In quantitative analysis, the five alkaloids showed good regression (R > 0.999 2) within test ranges and the recovery of the method was in the range of 98.4- 100.8%. The limit of detections and quantifications for five alkaloids in PAD were less than 0.07 and 0.22 μg/ml, respectively. In order to compare the UPLC fingerprints between Rhizoma Coptidis from different origins, the chemometrics procedures, including similarity analysis (SA), hierarchical clustering analysis (HCA), principal component analysis (PCA) were applied to classify the Rhizoma Coptidis samples according to their cultivated origins. Consistent results were obtained to show that Rhizoma Coptidis samples could be successfully grouped in accordance with the province of origin. Furthermore, five marker constituents were screened out to be the main chemical marker, which could be applied to accurate discrimination and quality control for Rhizoma Coptidis by quantitative analysis. This study revealed that UPLC-PAD method was simple, sensitive and reliable for quantitative and chemical fingerprint analysis, moreover, for the quality evaluation and control of Rhizoma Coptidis.     

Plant Growth Analysis: Additive and Multiplicative Components of Growth

Three techniques used to investigate whole-plant growth areplant growth analysis, yield component analysis and demographicanalysis. Each subdivides growth into morphological or physiologicalcomponents. This paper derives several relationships which definethe contributions made by components to the performance of thewhole plant. For example, the additive contributions by differentplant parts to overall unit leaf rate may be determined. Also,for multiplicative components, the relative growth rate of yieldis the sum of the relative growth rates of yield components.The relationships developed here serve to link different approachesto growth analysis, and they are illustrated using data fromgrowth studies of bean and sunflower. Plant growth analysis, yield component analysis, demographic analysis, Phaseolus vulgaris L., Helianthus annus L.     

Plant Growth Analysis: Growth and Yield Component Responses to Population Density in Forage Maize

Forage maize (Zea mays L.) was grown in monocultures at populationdensities ranging from 4·9 to 11·1 plants m^–2.Data for plant growth analysis were obtained from six harvestscarried out from 21 to 115 d after planting. Conventional plantgrowth analysis indicated that improvements in forage productivityper unit land area by high population density resulted directlyfrom increased plant presence. Reduction in dry weight per shootat high population density was associated with reduced unitleaf rate. Leaf area ratio was little affected, which may implythat competition for soil nutrients or oxygen was the chiefcause of plant interference. Yield component analysis demonstratedthe increasing importance of population density treatments asa source of variation as growth progressed. Direct relationshipsbetween variation in yield per plant and variation in two yieldcomponents, stem diameter and the inverse of leaf area ratio,were demonstrated. Both conventional plant growth analysis andyield component analysis indicated complex physiological andmorphological adjustments to species population density. Plant growth analysis, yield component analysis, Zea mays L     

MAGMA: Generalized Gene-Set Analysis of GWAS Data

By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical power for most methods is strongly affected by linkage disequilibrium between markers, multi-marker associations are often hard to detect, and the reliance on permutation to compute p-values tends to make the analysis computationally very expensive. To address these issues we have developed MAGMA, a novel tool for gene and gene-set analysis. The gene analysis is based on a multiple regression model, to provide better statistical performance. The gene-set analysis is built as a separate layer around the gene analysis for additional flexibility. This gene-set analysis also uses a regression structure to allow generalization to analysis of continuous properties of genes and simultaneous analysis of multiple gene sets and other gene properties. Simulations and an analysis of Crohn’s Disease data are used to evaluate the performance of MAGMA and to compare it to a number of other gene and gene-set analysis tools. The results show that MAGMA has significantly more power than other tools for both the gene and the gene-set analysis, identifying more genes and gene sets associated with Crohn’s Disease while maintaining a correct type 1 error rate. Moreover, the MAGMA analysis of the Crohn’s Disease data was found to be considerably faster as well.     

Statistical analysis of real-time PCR data

Background  

Even though real-time PCR has been broadly applied in biomedical sciences, data processing procedures for the analysis of quantitative real-time PCR are still lacking; specifically in the realm of appropriate statistical treatment. Confidence interval and statistical significance considerations are not explicit in many of the current data analysis approaches. Based on the standard curve method and other useful data analysis methods, we present and compare four statistical approaches and models for the analysis of real-time PCR data.     

A novel DNA methylation-based model that effectively predicts prognosis in hepatocellular carcinoma

Purpose: To build a novel predictive model for hepatocellular carcinoma (HCC) patients based on DNA methylation data.Methods: Four independent DNA methylation datasets for HCC were used to screen for common differentially methylated genes (CDMGs). Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used to explore the biological roles of CDMGs in HCC. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis were performed to identify survival-related CDMGs (SR-CDMGs) and to build a predictive model. The importance of this model was assessed using Cox regression analysis, propensity score-matched (PSM) analysis and stratification analysis. A validation group from the Cancer Genome Atlas (TCGA) was constructed to further validate the model.Results: Four SR-CDMGs were identified and used to build the predictive model. The risk score of this model was calculated as follows: risk score = (0.01489826 × methylation level of WDR69) + (0.15868618 × methylation level of HOXB4) + (0.16674959 × methylation level of CDKL2) + (0.16689301 × methylation level of HOXA10). Kaplan–Meier analysis demonstrated that patients in the low-risk group had a significantly longer overall survival (OS; log-rank P-value =0.00071). The Cox model multivariate analysis and PSM analysis identified the risk score as an independent prognostic factor (P<0.05). Stratified analysis results further confirmed this model performed well. By analyzing the validation group, the results of receiver operating characteristic (ROC) curve analysis and survival analysis further validated this model.Conclusion: Our DNA methylation-based prognosis predictive model is effective and reliable in predicting prognosis for patients with HCC.     

Reflections on univariate and multivariate analysis of metabolomics data

Metabolomics experiments usually result in a large quantity of data. Univariate and multivariate analysis techniques are routinely used to extract relevant information from the data with the aim of providing biological knowledge on the problem studied. Despite the fact that statistical tools like the t test, analysis of variance, principal component analysis, and partial least squares discriminant analysis constitute the backbone of the statistical part of the vast majority of metabolomics papers, it seems that many basic but rather fundamental questions are still often asked, like: Why do the results of univariate and multivariate analyses differ? Why apply univariate methods if you have already applied a multivariate method? Why if I do not see something univariately I see something multivariately? In the present paper we address some aspects of univariate and multivariate analysis, with the scope of clarifying in simple terms the main differences between the two approaches. Applications of the t test, analysis of variance, principal component analysis and partial least squares discriminant analysis will be shown on both real and simulated metabolomics data examples to provide an overview on fundamental aspects of univariate and multivariate methods.     

Therapeutic mechanism of intracranial infection in patients with hydrocephalus after craniocerebral injury based on decompressive craniectomy

The objective of this study is to analyze the treatment mechanism of decompressive craniectomy for intracranial infection in patients with hydrocephalus after craniocerebral injury, and to provide a treatment plan for intracranial infection in patients with hydrocephalus after craniocerebral injury. In this study, literature screening and data acquisition were carried out firstly based on the research content, and then heterogeneity analysis, Meta-analysis, sensitivity analysis, and publication bias analysis were performed using statistical methods for the unilateral and bilateral decompressive craniectomy. Heterogeneity analysis, Meta-analysis and sensitivity analysis of indiscriminate unilateral decompressive craniectomy was performed; heterogeneity analysis, Meta-analysis, cumulative Meta-analysis, and sensitivity analysis for bilateral decompressive craniectomy were performed. In this study, the order of influence on patients with hydrocephalus after brain injury was as follows: bilateral decompressive craniectomy > unilateral and bilateral decompressive decompression > indiscriminate unilateral decompressive. Intracranial infection in patients with hydrocephalus after the craniocerebral injury should be comprehensively evaluated before the surgery and given clinical treatment in time.     

SIGNAL™/RTSD™ SOUND ANALYSIS SYSTEM

ABSTRACT

The prevalence of complex acoustic structures in mammalian vocalisations can make it difficult to quantify frequency characteristics. We describe two methods developed for the frequency analysis of a complex swift fox Vulpes velox vocalisation, the barking sequence: (1) autocorrelation function analysis and (2) instantaneous frequency analysis. The autocorrelation function analysis results in an energy density spectrum of the signal's averaged amplitude and frequency information. This analysis was used for locating possible formant structures and quantifying the energy distribution of single barks in the barking sequence. The instantaneous frequency analysis is applied to individual continuous frequency bands and generates frequency contours with a resolution of a couple of Hertz. It was used to quantify frequency modulation and calculate average frequencies of harmonic bands in individual barks and to estimate fundamental frequencies. This second method of analysis had to be evaluated with spectrographic analysis to gauge its reliability for each band analysed. The algorithms used should make both of these methods applicable to other complex vocalisations.     

Critical review of reporting of the data analysis step in metabolomics

Introduction

We present the first study to critically appraise the quality of reporting of the data analysis step in metabolomics studies since the publication of minimum reporting guidelines in 2007.

Objectives

The aim of this study was to assess the standard of reporting of the data analysis step in metabolomics biomarker discovery studies and to investigate whether the level of detail supplied allows basic understanding of the steps employed and/or reuse of the protocol. For the purposes of this review we define the data analysis step to include the data pretreatment step and the actual data analysis step, which covers algorithm selection, univariate analysis and multivariate analysis.

Method

We reviewed the literature to identify metabolomic studies of biomarker discovery that were published between January 2008 and December 2014. Studies were examined for completeness in reporting the various steps of the data pretreatment phase and data analysis phase and also for clarity of the workflow of these sections.

Results

We analysed 27 papers, published anytime in 2008 until the end of 2014 in the area or biomarker discovery in serum metabolomics. The results of this review showed that the data analysis step in metabolomics biomarker discovery studies is plagued by unclear and incomplete reporting. Major omissions and lack of logical flow render the data analysis’ workflows in these studies impossible to follow and therefore replicate or even imitate.

Conclusions

While we await the holy grail of computational reproducibility in data analysis to become standard, we propose that, at a minimum, the data analysis section of metabolomics studies should be readable and interpretable without omissions such that a data analysis workflow diagram could be extrapolated from the study and therefore the data analysis protocol could be reused by the reader. That inconsistent and patchy reporting obfuscates reproducibility is a given. However even basic understanding and reuses of protocols are hampered by the low level of detail supplied in the data analysis sections of the studies that we reviewed.

     

T-REX: software for the processing and analysis of T-RFLP data

Background  

Despite increasing popularity and improvements in terminal restriction fragment length polymorphism (T-RFLP) and other microbial community fingerprinting techniques, there are still numerous obstacles that hamper the analysis of these datasets. Many steps are required to process raw data into a format ready for analysis and interpretation. These steps can be time-intensive, error-prone, and can introduce unwanted variability into the analysis. Accordingly, we developed T-REX, free, online software for the processing and analysis of T-RFLP data.