robCompositions: An R‐package for Robust Statistical Analysis of Compositional Data

Templ, Matthias; Hron, Karel; Filzmoser, Peter

doi:10.1002/9781119976462.ch25

Cited by 286 publications

(224 citation statements)

References 17 publications

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“…All ternary plots were created in a custom-modified version of the R package robCompositions (Templ et al, 2011) and are shown in the same orientation: the suction axis runs from the midpoint of the bottom edge of the triangle to the top vertex, the body ram axis runs from the midpoint of the left edge of the triangle to the lower right vertex, and the jaw ram axis runs from the midpoint of right edge of the triangle to the lower left vertex. Robust PCA for compositional data were calculated using a centered logratio transformation as implemented in the pcaCoDa function in the robCompositions R package (Filzmoser et al, 2009;Templ et al, 2011). Note that as body ram, jaw ram and suction contributions to prey capture all add to a constant value, our dataset is two-dimensional and so only two principal component axes were obtained.…”

Section: Data Visualization and Statistical Analysismentioning

confidence: 99%

Body ram, not suction, is the primary axis of suction feeding diversity in spiny-rayed fishes

Longo

McGee

Oufiero

et al. 2015

Journal of Experimental Biology

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Suction-feeding fishes exhibit diverse prey-capture strategies that vary in their relative use of suction and predator approach (ram), which is often referred to as the ram-suction continuum. Previous research has found that ram varies more than suction distance among species, such that ram accounts for most differences in prey-capture behaviors. To determine whether these findings hold at broad evolutionary scales, we collected high-speed videos of 40 species of spiny-rayed fishes (Acanthomorpha) feeding on live prey. For each strike, we calculated the contributions of suction, body ram (swimming) and jaw ram (mouth movement relative to the body) to closing the distance between predator and prey. We confirm that the contribution of suction distance is limited even in this phylogenetically and ecologically broad sample of species, with the extreme suction area of prey-capture space conspicuously unoccupied. Instead of a continuum from suction to ram, we find that variation in body ram is the major factor underlying the diversity of prey-capture strategies among suction-feeding fishes. Independent measurement of the contribution of jaw ram revealed that it is an important component of diversity among spiny-rayed fishes, with a number of ecomorphologies relying heavily on jaw ram, including pivot feeding in syngnathiforms, extreme jaw protruders and benthic sit-and-wait ambush predators. A combination of morphological and behavioral innovations has allowed fish to invade the extreme jaw ram area of prey-capture space. We caution that while two-species comparisons may support a ram-suction trade-off, these patterns do not speak to broader patterns across spiny-rayed fishes.

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Section: Data Visualization and Statistical Analysismentioning

confidence: 99%

Body ram, not suction, is the primary axis of suction feeding diversity in spiny-rayed fishes

Longo

McGee

Oufiero

et al. 2015

Journal of Experimental Biology

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“…This approach produces compositional data subject to constantsum constraints and inherently biased toward negative correlation among components (Aitchison 1986). Therefore, we followed the approach of Margres et al (2015a) and Wray et al (2015) and used centered log ratio (clr) and isometric log ratio (ilr) transformations (Egozcue et al 2003), when appropriate, to transform the data using the robCompositions package (Templ et al 2011) in R prior to statistical analysis (Filzmoser et al 2009). We used the multiplicative replacement strategy (Martin-Fernandez et al 2003) implemented in the R package zCompositions assuming a detection threshold of 0.01% (the smallest measured value) to resolve the issue of zeros.…”

Section: Mass Spectrometry Analysismentioning

confidence: 99%

Expression Differentiation Is Constrained to Low-Expression Proteins over Ecological Timescales

Margres¹,

Wray²,

Seavy³

et al. 2015

Genetics

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Protein expression level is one of the strongest predictors of protein sequence evolutionary rate, with high-expression protein sequences evolving at slower rates than low-expression protein sequences largely because of constraints on protein folding and function. Expression evolutionary rates also have been shown to be negatively correlated with expression level across human and mouse orthologs over relatively long divergence times (i.e., 100 million years). Long-term evolutionary patterns, however, often cannot be extrapolated to microevolutionary processes (and vice versa), and whether this relationship holds for traits evolving under directional selection within a single species over ecological timescales (i.e., ,5000 years) is unknown and not necessarily expected. Expression is a metabolically costly process, and the expression level of a particular protein is predicted to be a tradeoff between the benefit of its function and the costs of its expression. Selection should drive the expression level of all proteins close to values that maximize fitness, particularly for high-expression proteins because of the increased energetic cost of production. Therefore, stabilizing selection may reduce the amount of standing expression variation for high-expression proteins, and in combination with physiological constraints that may place an upper bound on the range of beneficial expression variation, these constraints could severely limit the availability of beneficial expression variants. To determine whether rapid-expression evolution was restricted to low-expression proteins owing to these constraints on highly expressed proteins over ecological timescales, we compared venom protein expression levels across mainland and island populations for three species of pit vipers. We detected significant differentiation in protein expression levels in two of the three species and found that rapid-expression differentiation was restricted to low-expression proteins. Our results suggest that various constraints on high-expression proteins reduce the availability of beneficial expression variants relative to lowexpression proteins, enabling low-expression proteins to evolve and potentially lead to more rapid adaptation.KEYWORDS protein expression; selective constraints; evolutionary rates; adaptation T HE expression level of a protein is one of the strongest predictors of protein sequence evolutionary rate; sequences of highly expressed proteins evolve more slowly than low-expression proteins (Duret and Mouchiroud 1999;Pal et al. 2001;Gout et al. 2010;Yang et al. 2012;Nabholz et al. 2013;Park et al. 2013). This relationship may be a function of specific selective constraints on sequences to avoid protein misfolding (Drummond et al. 2005;Geiler-Samerotte et al. 2011), protein misinteractions (Yang et al. 2012, a decrease in protein function (Cherry 2010;Gout et al. 2010), and/or messenger RNA (mRNA) misfolding (Park et al. 2013). Analyses of microarray data have shown that expression evolutionary rate is also negatively...

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“…Numerical analyses were performed in the R statistical computing environment: the compositions package (van den Boogaart et al, 2014) to transform compositional data into isometric log-ratios; the dplyr package for general data manipulation; the ggplot2 package (Wickham and Chang, 2015) for data visualization; the mvoutlier package (Filzmoser and Gschwandtner, 2015) to identify outliers; the nlme package (Pinheiro et al, 2015) to fit non-linear multilevel models; the robCompositions package (Templ et al, 2015) to impute missing values robustly; the vegan package (Oksanen et al, 2015) for principal component analysis. The R code and data are available as Supplementary Material 1.…”

Section: Numerical Analysismentioning

confidence: 99%

Site-Specific Multilevel Modeling of Potato Response to Nitrogen Fertilization

Parent

Leblanc

Parent

et al. 2017

Front. Environ. Sci.

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Technologies of precision agriculture, digital soil maps, and meteorological stations provide a minimum data set to guide precision farming operations. However, determining optimal nutrient requirements for potato (Solanum tuberosum L.) crops at subfield scale remains a challenge given specific climatic, edaphic, and managerial conditions. Multilevel modeling can generalize yield response to fertilizer additions using data easily accessible to growers. Our objective was to elaborate a multilevel N fertilizer response model for potato crops using the Mitscherlich equation and a core data set of 93 N fertilizer trials conducted in Québec, Canada. Daily climatic data were collected at 10 × 10 km resolution. Soils were characterized by organic matter content, pH, and texture in the arable layer, and by texture and tools of pedometrics across a gleization-podzolization continuum in subsoil layers. There were five categories of preceding crops and five cultivar maturity orders. The three Mitscherlich parameters (Asymptote, Rate, and Environment) were most often site-specific. Sensitivity analysis showed that optimum N dosage increased with non-leguminous high-residue preceding crops, coarser soils, podzolization, drier climatic condition, and late cultivar maturity. The inferential model could guide site-specific N fertilization using an accessible minimum data set to support fertilization decisions. As decision-support system, the model could also provide a range of optimum N doses across a large spectrum of site-specific conditions including climate change.

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robCompositions: An R‐package for Robust Statistical Analysis of Compositional Data

Cited by 286 publications

References 17 publications

Body ram, not suction, is the primary axis of suction feeding diversity in spiny-rayed fishes

Body ram, not suction, is the primary axis of suction feeding diversity in spiny-rayed fishes

Expression Differentiation Is Constrained to Low-Expression Proteins over Ecological Timescales

Site-Specific Multilevel Modeling of Potato Response to Nitrogen Fertilization

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