Abstract:Pvclust is an add-on package for a statistical software R to assess the uncertainty in hierarchical cluster analysis. Pvclust can be used easily for general statistical problems, such as DNA microarray analysis, to perform the bootstrap analysis of clustering, which has been popular in phylogenetic analysis. Pvclust calculates probability values (p-values) for each cluster using bootstrap resampling techniques. Two types of p-values are available: approximately unbiased (AU) p-value and bootstrap probability (… Show more
“…Pvclust calculates probability values ( p -values) for each cluster using bootstrap resampling techniques. Two types of p -values are available: approximately unbiased p -value and bootstrap probability value (Suzuki and Shimodaira 2006). 10.1080/21501203.2018.1481152-F0005Figure 5.Validated dendrogram of 26 fungal isolates based on their Δτ, Lac activity and LiP activity, prepared using Infostat 2016 software linked to R software.…”
The aim of the present study was to assess the ability of different white-rot fungi to tolerate polychlorinated biphenyls (PCBs) using predictive mycology, by relating fungal growth inhibition to ligninolityc enzyme secretion. Fungal strains were grown in the presence of PCBs in solid media and their radial growth values were modelled through the Dantigny-logistic like function in order to estimate the time required by the fungal colonies to attain half their maximum diameter. The principal component analysis (PCA) revealed an inverse correlation between strain tolerance to PCBs and the laccase secretion over time, being laccase production closely associated with fungal growth capacity. Finally, a PCA was run to regroup and split between resistant and sensitive fungi. Simultaneously, a function associated with a model predicting the tolerance to PCBs was developed. Some of the assayed isolates showed a promising capacity to be applied in PCB bioremediation.Abbreviations: Polychlorinated biphenyls (PCBs), white-rot fungi (WRF)
“…Pvclust calculates probability values ( p -values) for each cluster using bootstrap resampling techniques. Two types of p -values are available: approximately unbiased p -value and bootstrap probability value (Suzuki and Shimodaira 2006). 10.1080/21501203.2018.1481152-F0005Figure 5.Validated dendrogram of 26 fungal isolates based on their Δτ, Lac activity and LiP activity, prepared using Infostat 2016 software linked to R software.…”
The aim of the present study was to assess the ability of different white-rot fungi to tolerate polychlorinated biphenyls (PCBs) using predictive mycology, by relating fungal growth inhibition to ligninolityc enzyme secretion. Fungal strains were grown in the presence of PCBs in solid media and their radial growth values were modelled through the Dantigny-logistic like function in order to estimate the time required by the fungal colonies to attain half their maximum diameter. The principal component analysis (PCA) revealed an inverse correlation between strain tolerance to PCBs and the laccase secretion over time, being laccase production closely associated with fungal growth capacity. Finally, a PCA was run to regroup and split between resistant and sensitive fungi. Simultaneously, a function associated with a model predicting the tolerance to PCBs was developed. Some of the assayed isolates showed a promising capacity to be applied in PCB bioremediation.Abbreviations: Polychlorinated biphenyls (PCBs), white-rot fungi (WRF)
“…Asymptotically unbiased probability values (AU) were then calculated for each cluster by AU ¼ F(Àa þ b). 17 We defined a stable cluster if a cluster had an AU40.95. We performed 10,000 bootstrap replicates for each sampling fraction to evaluate the cluster stability.…”
Section: Methodsmentioning
confidence: 99%
“…The pattern of missing values among the contaminants was assessed with hierarchical cluster analysis, using the fraction of missing data in common between any two variables as a similarity measure. All analyses were made using R 19 version 2.10.0, with functions from the Hmisc 20 and pvclust 17 packages.…”
We are daily exposed to many different environmental contaminants. Mixtures of these contaminants could act together to induce more pronounced effects than the sum of the individual contaminants. To evaluate the effects of such mixtures, it is of importance to assess the co-variance amongst the contaminants. Thirty-seven environmental contaminants representing different classes were measured in blood samples from 1016 individuals aged 70 years. Hierarchical cluster analysis and principal component analysis were used to assess the co-variation among the contaminants. Within each identified cluster, possible marker contaminants were sought for. We validated our findings using data from the National Health and Nutrition Examination Survey (NHANES) 2003--2004 study. Two large clusters could be identified, one representing low/medium chlorinated polychlorinated biphenyls (PCBs) (r6 chlorine atoms), as well as two pesticides and one representing medium/high chlorinated PCBs (Z6 chlorine atoms). PCBs 118 and 153 could be used as markers for the low/medium chlorinated cluster and PCBs 170 and 209 could be used as markers for the medium/high chlorinated cluster. This pattern was similar to data from the NHANES study. Apart from the PCBs, little co-variation was seen among the contaminants. Thus, a large number of chemicals have to be measured to adequately identify mixtures of environmental contaminants.
“…23 A multiple comparison procedure is designed to be conservative when testing for significant differences for more than one pair of groups. 24 The stability of the hierarchical clustering based on the distances among objects was assessed by a bootstrap analysis (10 000 resamplings) using the 'pvclust' package 25 in R. In a bootstrap analysis, the stability of the clustering is assessed upon randomization of the number of occurrences of each variable in the data set, while keeping the size of the data set equal.…”
Twin and family studies are typically used to elucidate the relative contribution of genetic and environmental variation to phenotypic variation. Here, we apply a quantitative genetic method based on hierarchical clustering, to blood plasma lipidomics data obtained in a healthy cohort consisting of 37 monozygotic and 28 dizygotic twin pairs, and 52 of their biological nontwin siblings. Such data are informative of the concentrations of a wide range of lipids in the studied blood samples. An important advantage of hierarchical clustering is that it can be applied to a high-dimensional 'omics' type data, whereas the use of many other quantitative genetic methods for analysis of such data is hampered by the large number of correlated variables. For this study we combined two lipidomics data sets, originating from two different measurement blocks, which we corrected for block effects by 'quantile equating'. In the analysis of the combined data, average similarities of lipidomics profiles were highest between monozygotic (MZ) cotwins, and became progressively lower between dizygotic (DZ) cotwins, among sex-matched nontwin siblings and among sex-matched unrelated participants, respectively. Our results suggest that (1) shared genetic background, shared environment, and similar age contribute to similarities in blood plasma lipidomics profiles among individuals; and (2) that the power of quantitative genetic analyses is enhanced by quantile equating and combination of data sets obtained in different measurement blocks.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
