Changing the Reference Measure in the Simplex and its Weighting Effects

Egozcue, J. J.; Pawlowsky‐Glahn, Vera

doi:10.17713/ajs.v45i4.126

Cited by 34 publications

(31 citation statements)

References 12 publications

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“…Balances enable highly interpretable results of ILR coordinates by using selected sets of taxa in the log ratios. ILR balances were generated using the following equation: where and indicate the set of taxa involved the numerator and denominator of that balance (b i ), respectively, and provides the weight integration (30). The first part of the listed balance equation, , acts as a scaling factor that normalizes each balance to unit length, regardless of weighting scheme.…”

Section: Methodsmentioning

confidence: 99%

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crAssphage abundance and genomic selective pressure correlate with altered bacterial abundance in the fecal microbiota of South African mother-infant dyads

Bp¹,

Wendoh

Chopera

et al. 2019

Preprint

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word count: 241 24 Main text word count: 4,667 25 26 Abstract 29crAssphages are a class of bacteriophages that are highly abundant in the human 30 gastrointestinal tract. Accordingly, crAssphage genomes have been identified in most 31human fecal viral metagenome studies. However, we currently have an incomplete 32 understanding of factors impacting the transmission frequencies of these phages 33 between mothers and infants, and the evolutionary pressures associated with such 34 transmissions. Here, we use metagenome sequencing of stool-associated virus-like 35 particles to identify the prevalence of crAssphage across ten South African mother-36 infant dyads that are discordant for HIV infection. We report the identification of a 37 complete 97kb crAssphage genome, parts of which are detected at variable levels 38 across each mother-infant dyad. We observed average nucleotide sequence identities 39 of >99% for crAssphages from related mother-infant pairs but ~97% identities between 40 crAssphages from unrelated mothers and infants: a finding strongly suggestive of 41 vertical mother to infant transmission. We further analyzed patterns of nucleotide 42 diversity across the crAssphage sequences described here, identifying particularly 43 elevated positive selection in RNA polymerase and phage tail protein encoding genes, 44 which we validated against a crAssphage genome from previous studies. Using 16S 45 rRNA gene sequencing, we found that the relative abundances of Bacteroides 46 thetaiotaomicron and Parabacteroides merdae (Order: Bacteroidales) were differentially 47 Importance 53 crAssphages are an ubiquitous member of the human gut microbiome and modulate 54 interactions with key bacterial associates within the order Bacteroidales. However, the 55 role of this interaction in the genomic evolution of crAssphage remains unclear. Across 56 a longitudinally sampled cohort of ten South African mother-infant dyads, we use 57 metagenome sequencing of the fecal virome and 16S rRNA gene sequencing of the 58 fecal bacterial microbiota to elucidate the ecological and evolutionary dynamics of these 59interactions. Here, we demonstrate elevated levels of crAssphage average nucleotide 60 identity between related mother-infant dyads as compared to unrelated individuals, 61suggesting vertical transmission. We report strong positive selection in crAssphage 62 RNA polymerase and phage tail protein genes. Finally, we demonstrate that 63 crAssphage abundance is linearly correlated (P < 0.014) with the abundance of two 64 bacterial taxa, Bacteroides thetaiotaomicron and Parabacteroides merdae. These 65 results suggest that phage-bacterial interactions may help shape ecological and 66 evolutionary dynamics in the gut. 67 68

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Section: Methodsmentioning

confidence: 99%

“…The first part of the listed balance equation, , acts as a scaling factor that normalizes each balance to unit length, regardless of weighting scheme. The weighted geometric mean (30) of the taxa associated with a given balance is represented as and can be formalized as: …”

Section: Methodsmentioning

confidence: 99%

crAssphage abundance and genomic selective pressure correlate with altered bacterial abundance in the fecal microbiota of South African mother-infant dyads

Bp¹,

Wendoh

Chopera

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…More appealing alternatives do not need to post-process the output and allow to perform the analysis taking into account the constraints. The seminal works by Egozcue et al (2006), van der Boogaart et al (2010), van der Boogaart et al (2014) and Egozcue and Pawlowsky-Glahn (2015) introduced the notion of Bayes space, that is a Hilbert space of probability density functions. They borrowed from compositional data analysis and Aitchison's geometry (see Aitchison (1986)) and interpreted probability density functions as infinite-dimensional compositional vectors.…”

Section: Copula Familymentioning

confidence: 99%

Nonparametric Forecasting of Multivariate Probability Density Functions

Guégan

Iacopini²

2018

SSRN Journal

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The study of dependence between random variables is the core of theoretical and applied statistics. Static and dynamic copula models are useful for describing the dependence structure, which is fully encrypted in the copula probability density function. However, these models are not always able to describe the temporal change of the dependence patterns, which is a key characteristic of financial data.We propose a novel nonparametric framework for modelling a time series of copula probability density functions, which allows to forecast the entire function without the need of post-processing procedures to grant positiveness and unit integral. We exploit a suitable isometry that allows to transfer the analysis in a subset of the space of square integrable functions, where we build on nonparametric functional data analysis techniques to perform the analysis.The framework does not assume the densities to belong to any parametric family and it can be successfully applied also to general multivariate probability density functions with bounded or unbounded support. Finally, a noteworthy field of application pertains the study of time varying networks represented through vine copula models.We apply the proposed methodology for estimating and forecasting the time varying dependence structure between the S&P500 and NASDAQ indices. *

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“…The original PhILR furthermore allows to use per-taxon weights p in order to 523 down-weigh the impact of low abundant taxa/OTUs [30,89]. In our adaptation, this 524 weighting scheme is accordingly changed to weights p per edge of the RT.…”

mentioning

confidence: 99%

“…We later describe an appropriate choice 530 of weights in Section Edge Weights. These weights are applied to the relative masses x 531 to obtain the shifted composition y = x/p, using element-wise division [89].…”

mentioning

confidence: 99%

Scalable methods for analyzing and visualizing phylogenetic placement of metagenomic samples

Czech

Stamatakis

2018

Preprint

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The exponential decrease in molecular sequencing cost generates unprecedented amounts of data. Hence, scalable methods to analyze these data are required. Phylogenetic (or Evolutionary) Placement methods identify the evolutionary provenance of anonymous sequences with respect to a given reference phylogeny. This increasingly popular method is deployed for scrutinizing metagenomic samples from environments such as water, soil, or the human gut.Here, we present novel and, more importantly, highly scalable methods for analyzing phylogenetic placements of metagenomic samples. More specifically, we introduce methods for (a) visualizing differences between samples and their correlation with associated meta-data on the reference phylogeny, (b) clustering similar samples using a variant of the k-means method, and (c) finding phylogenetic factors using an adaptation of the Phylofactorization method. These methods enable to interpret metagenomic data in a phylogenetic context, to find patterns in the data, and to identify branches of the phylogeny that are driving these patterns.To demonstrate the scalability and utility of our methods, as well as to provide exemplary interpretations of our methods, we applied them to 3 publicly available datasets comprising 9782 samples with a total of approximately 168 million sequences. The results indicate that new biological insights can be attained via our methods. Introduction 1The availability of high-throughput DNA sequencing technologies has revolutionized 2 biology by transforming it into an ever more data-driven and compute-intense 3 discipline [1]. In particular, Next Generation Sequencing (NGS) [2], as well as later 4 generations [3][4][5][6], have given rise to novel methods for studying microbial 5 environments [7][8][9][10]. These technologies are often used in metagenomic studies to 6 sequence organisms in water [11][12][13] or soil [14,15] samples, in the human 7 microbiome [16][17][18], and a plethora of other environments. These studies yield a large 8 set of short anonymous DNA sequences, so-called reads, for each sample. Reads that are 9 obtained from specific parts of the genome are called meta-barcoding reads; most often, 10 reads are amplified before sequencing and later de-replicated again, resulting in 11 so-called amplicons. A typical task in metagenomic studies is to identify and classify 12 1/72 these sequences with respect to known reference sequences, either in a taxonomic or a 13 phylogenetic context. 14 Conventional methods like BLAST [19] are based on sequence similarity or identity. 15 Such methods are fast, but only attain satisfying accuracy levels if the query sequences 16 (e.g., the environmental reads or amplicons) are sufficiently similar to the reference 17 sequences. Furthermore, BLAST might yield suboptimal results [20], and the best 18 BLAST hit does often not represent the most closely related species [21]. 19 Alternatively, so-called phylogenetic (or evolutionary) placement methods [22][23][24] 20 identify query sequences based on a...

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Changing the Reference Measure in the Simplex and its Weighting Effects

Cited by 34 publications

References 12 publications

crAssphage abundance and genomic selective pressure correlate with altered bacterial abundance in the fecal microbiota of South African mother-infant dyads

crAssphage abundance and genomic selective pressure correlate with altered bacterial abundance in the fecal microbiota of South African mother-infant dyads

Nonparametric Forecasting of Multivariate Probability Density Functions

Scalable methods for analyzing and visualizing phylogenetic placement of metagenomic samples

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