Analysis of T-RFLP data using analysis of variance and ordination methods: a comparative study

The analysis of T-RFLP data has developed considerably over the last decade, but there remains a lack of consensus about which statistical analyses offer the best means for finding trends in these data. In this study, we empirically tested and theoretically compared ten diverse T-RFLP datasets derived from soil microbial communities using the more common ordination methods in the literature: principal component analysis (PCA), nonmetric multidimensional scaling (NMS) with Sørensen, Jaccard and Euclidean distance measures, correspondence analysis (CA), detrended correspondence analysis (DCA) and a technique new to T-RFLP data analysis, the Additive Main Effects and Multiplicative Interaction (AMMI) model. Our objectives were i) to determine the distribution of variation in T-RFLP datasets using analysis of variance (ANOVA), ii) to determine the more robust and informative multivariate ordination methods for analyzing T-RFLP data, and iii) to compare the methods based on theoretical considerations. For the 10 datasets examined in this study, ANOVA revealed that the variation from Environment main effects was always small, variation from T-RFs main effects was large, and variation from T-RF × Environment (T × E) interactions was intermediate. Larger variation due to T × E indicated larger differences in microbial communities between environments/treatments and thus demonstrated the utility of ANOVA to provide an objective assessment of community dissimilarity. The comparison of statistical methods typically yielded similar empirical results. AMMI, T-RF-centered PCA, and DCA were the most robust methods in terms of producing ordinations that consistently reached a consensus with other methods. In datasets with high sample heterogeneity, NMS analyses with Sørensen and Jaccard distance were the most sensitive for recovery of complex gradients. The theoretical comparison showed that some methods hold distinct advantages for T-RFLP analysis, such as estimations of variation captured, realistic or minimal assumptions about the data, reduced weight placed on rare T-RFs, and uniqueness of solutions. Our results lead us to recommend that method selection be guided by T-RFLP dataset complexity and the outlined theoretical criteria. Finally, we recommend using binary or relativized peak height data with soil-based T-RFLP data for ordination-based exploratory microbial analyses.     

Higher-level snake phylogeny inferred from mitochondrial DNA sequences of 12S rRNA and 16S rRNA genes

Portions of two mitochondrial genes (12S and 16S ribosomal RNA) were sequenced to determine the phylogenetic relationships among the major clades of snakes. Thirty-six species, representing nearly all extant families, were examined and compared with sequences of a tuatara and three families of lizards. Snakes were found to constitute a monophyletic group (confidence probability [CP] = 96%), with the scolecophidians (blind snakes) as the most basal lineages (CP = 99%). This finding supports the hypothesis that snakes underwent a subterranean period early in their evolution. Caenophidians (advanced snakes), excluding Acrochordus, were found to be monophyletic (CP = 99%). Among the caenophidians, viperids were monophyletic (CP = 98%) and formed the sister group to the elapids plus colubrids (CP = 94%). Within the viperids, two monophyletic groups were identified: true vipers (CP = 98%) and pit vipers plus Azemiops (CP = 99%). The elapids plus Atractaspis formed a monophyletic clade (CP = 99%). Within the paraphyletic Colubridae, the largely Holarctic Colubrinae was found to be a monophyletic assemblage (CP = 98%), and the Xenodontinae was found to be polyphyletic (CP = 91%). Monophyly of the henophidians (primitive snakes) was neither supported nor rejected because of the weak resolution of relationships among those taxa, except for the clustering of Calabaria with a uropeltid, Rhinophis (CP = 94%).      

Detrended correspondence analysis: An improved ordination technique

Summary Detrended correspondence analysis (DCA) is an improvement upon the reciprocal averaging (RA) ordination technique. RA has two main faults: the second axis is often an arch or horseshoe distortion of the first axis, and distances in the ordination space do not have a consistent meaning in terms of compositional change (in particular, distances at the ends of the first RA axis are compressed relative to the middle). DCA corrects these two faults. Tests with simulated and field data show DCA superior to RA and to nonmetric multidimensional sealing in giving clear, interpretable results. DCA has several advantages. (a) Its performance is the best of the ordination techniques tested, and both species and sample ordinations are produced simultaneously. (b) The axes are scaled in standard deviation units with a definite meaning, (c) As implemented in a FORTRAN program called DECORANA, computing time rises only linearly with the amount of data analyzed, and only positive entries in the data matrix are stored in memory, so very large data sets present no difficulty. However, DCA has limitations, making it best to remove extreme outliers and discontinuities prior to analysis. DCA consistently gives the most interpretable ordination results, but as always the interpretation of results remains a matter of ecological insight and is improved by field experience and by integration of supplementary environmental data for the vegetation sample sites.This research was supported by the Institute of Terrestrial Ecology, Bangor, Wales, and by a grant from the National Science Foundation to R.H. Whittaker. We thank R.H. Whittaker for encouragement and comments, S.B. Singer for assistance with the Cornell computer, and H.J.B. Birks, S.R. Sabo, T.C.E. Wells, and R.H. Whittaker for data sets used for ordination tests.     

Changes in the endurance shuttle walk test in COPD patients with chronic respiratory failure after pulmonary rehabilitation: the minimal important difference obtained with anchor- and distribution-based method

Background

Although the endurance shuttle walk test (ESWT) has proven to be responsive to change in exercise capacity after pulmonary rehabilitation (PR) for COPD, the minimally important difference (MID) has not yet been established. We aimed to establish the MID of the ESWT in patients with severe COPD and chronic hypercapnic respiratory failure following PR.

Methods

Data were derived from a randomized controlled trial, investigating the value of noninvasive positive pressure ventilation added to PR. Fifty-five patients with stable COPD, GOLD stage IV, with chronic respiratory failure were included (mean (SD) FEV₁ 31.1 (12.0) % pred, age 62 (9) y). MID estimates of the ESWT in seconds, percentage and meters change were calculated with anchor based and distribution based methods. Six minute walking distance (6MWD), peak work rate on bicycle ergometry (Wpeak) and Chronic Respiratory Questionnaire (CRQ) were used as anchors and Cohen’s effect size was used as distribution based method.

Results

The estimated MID of the ESWT with the different anchors ranged from 186–199 s, 76–82% and 154–164 m. Using the distribution based method the MID was 144 s, 61% and 137 m.

Conclusions

Estimates of the MID for the ESWT after PR showed only small differences using different anchors in patients with COPD and chronic respiratory failure. Therefore we recommend using a range of 186–199 s, 76–82% or 154–164 m as MID of the ESWT in COPD patients with chronic respiratory failure. Further research in larger populations should elucidate whether this cut-off value is also valid in other COPD populations and with other interventions.

Trial registration

ClinicalTrials.Gov (ID {"type":"clinical-trial","attrs":{"text":"NCT00135538","term_id":"NCT00135538"}}NCT00135538).

Electronic supplementary material

The online version of this article (doi:10.1186/s12931-015-0182-x) contains supplementary material, which is available to authorized users.     

Predictive and postdictive success of statistical analyses of yield trials

Summary The accuracy of a yield trial can be increased by improved experimental techniques, more replicates, or more efficient statistical analyses. The third option involves nominal fixed costs, and is therefore very attractive. The statistical analysis recommended here combines the Additive main effects and multiplicative interaction (AMMI) model with a predictive assessment of accuracy. AMMI begins with the usual analysis of variance (ANOVA) to compute genotype and environment additive effects. It then applies principal components analysis (PCA) to analyze non-additive interaction effects. Tests with a New York soybean yield trial show that the predictive accuracy of AMMI with only two replicates is equal to the predictive accuracy of means based on five replicates. The effectiveness of AMMI increases with the size of the yield trial and with the noisiness of the data. Statistical analysis of yield trials with the AMMI model has a number of promising implications for agronomy and plant breeding research programs.This research was supported by the Rhizobotany Project of the USDA-ARS     

Rapid initial clustering of large data sets

Summary Multivariate analysis of plant community data has three goals: summarization of redundancy, identification of outliers, and elueidation of relationships. The first two are handled conveniently by initial fast clustering, and the third by subsequent ordination and hierarchical clustering, and perhaps table arrangement.Initial clustering algorithms should achieve withincluster homogeneity and require minimal computer resources. However, algorithmic uniqueness and a hierarchy are not needed. Computing time should be proportional to the amount of data, with no higher dependencies on the number of samples. A method is presented here meeting these requirements, called composite clustering and implemented in a FORTRAN program called COMPCLUS. The computer time required for COMPCLUS clustering is on the order of the time required merely to read the data, regardless of the number of samples.Several large field data sets were analyzed effectively by using COMPCLUS to reduce redundancy and identify outliers, and then ordinating the resulting composite clusters by detrended correspondence analysis (DECORANA). Various clusterings of the same data set can be compared using a percent mutual matches (PMM) index, and a matrix of such values can be ordinated for simultaneous comparison of a number of clusterings.This paper benefited at many points from discussions with Mark O. Hill and Robert H. Whittaker. Mark Hill suggested condensed data storage. This work was done under a National Science Foundation grant to Robert Whittaker. I also appreciate technical assistance from Timothy F. Mason and Steven B. Singer.     

Imputing missing yield trial data

Summary Intraspecific mitochondrial DNA (mtDNA) diversity was determined in 23 Phaseolus vulgaris genotypes, and compared to previously observed variability of morphoagronomic characters and isozyme loci. Twenty of the lines were collected from Malawian landraces; the other three were pure-bred cultivars. The mtDNAs were digested with eight restriction endonucleases, revealing complex banding patterns. Southern hybridization using cosmid clones covering about 200-kb of the genome showed a considerable amount of uniformity of the mtDNA banding patterns. However, five restriction fragment length polymorphisms (RFLPs) were detected, dividing the bean lines into two groups corresponding to the previously known Mesoamerican and Andean gene pools of P. vulgaris. The cultivar Mecosta was separated from the rest of the lines by an additional RFLP. At least two out of the six RFLPs are believed to be due to base-pair mutation events. Our results provide the first evidence that the cytoplasms of the two major germ plasm pools of beans are distinct.     

Genotype × environment interaction analysis of North American shrub willow yield trials confirms superior performance of triploid hybrids

Development of dedicated bioenergy crop production systems will require accurate yield estimates, which will be important for determining many of the associated environmental and economic impacts of their production. Shrub willow (Salix spp) is being promoted in areas of the USA and Canada due to its adaption to cool climates and wide genetic diversity available for breeding improvement. Willow breeding in North America is in an early stage, and selection of elite genotypes for commercialization will require testing across broad geographic regions to gain an understanding of how shrub willow interacts with the environment. We analyzed a dataset of first‐rotation shrub willow yields of 16 genotypes across 10 trial environments in the USA and Canada for genotype‐by‐environment interactions using the additive main effects and multiplicative interactions (AMMI) model. Mean genotype yields ranged from 5.22 to 8.58 oven‐dry Mg ha^?1 yr^?1. Analysis of the main effect of genotype showed that one round of breeding improved yields by as much as 20% over check cultivars and that triploid hybrids, most notably Salix viminalis × S. miyabeana, exhibited superior yields. We also found important variability in genotypic response to environments, which suggests specific adaptability could be exploited among 16 genotypes for yield gains. Strong positive correlations were found between environment main effects and AMMI parameters and growing environment temperatures. These findings demonstrate yield improvements are possible in one generation and will be important for developing cultivar recommendations and for future breeding efforts.     

Thymoquinone is protective against 2,3,7,8-tetrachlorodibenzo-p-dioxin induced hepatotoxicity

We investigated changes in rat liver tissues following administration of thymoquinone (TQ) against 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) induced hepatotoxicity. Fifty rats were assigned randomly to five groups of 10 as follows: control, corn oil, TCDD, TQ and TCDD + TQ. Biochemical and histopathological analyses were conducted on liver tissue. We found that 30 day TCDD administration caused histopathological changes in liver including thickening of Glisson’s capsule, intracytoplasmic vacuolization in hepatocytes, sinusoidal dilation, vascular and sinusoidal congestion and inflammatory cell infiltration. TCDD administration increased malondialdehyde (MDA), total oxidant status (TOS), alanine aminotransferase (ALT), aspartate aminotransferase (AST) and alkaline phosphatase (ALP) levels in rat liver tissue and reduced glutathione (GSH), superoxide dismutase (SOD), catalase (CAT) and total antioxidant status (TAS) levels compared to all other groups. In the TQ treated group, GSH, SOD, CAT and TAS levels increased compared to all other groups. MDA, TOS, ALT, AST, ALP levels decreased compared to all other groups. Our histological findings were consistent with the biochemical findings. The oxidative and histologic effects of TCDD were eliminated by TQ treatment. TCDD administration caused oxidative stress in rat liver and TQ administered with TCDD prevented TCDD induced hepatotoxicity. TQ could be considered an alternative anti-TCDD toxicity agent.