Julia codes implementing the non-stationary surrogate algorithm described in: M. Chavez, B. Cazel... more Julia codes implementing the non-stationary surrogate algorithm described in: M. Chavez, B. Cazelles (2019). Detecting dynamic spatial correlation patterns with generalized wavelet coherence and non-stationary surrogate data. Scientific Reports, 9: 7389 Briefly, in contrast with classical methods, the surrogate data used here are realisations of a non-stationary stochastic process, preserving both the amplitude and time-frequency distributions of original data. To reproduce the results in the figure, please run the routine instructionsJulia.jl <strong>The instructions have been tested on Julia v 1.0</strong>. For older versions (e.g. v0.4), some instructions don't work.
Human microbiome research is helped by the characterization of microbial networks, as these may r... more Human microbiome research is helped by the characterization of microbial networks, as these may reveal key microbes that can be targeted for beneficial health effects. Prevailing methods of microbial network characterization are based on measures of association, often applied to limited sampling points in time. Here, we demonstrate the potential of wavelet clustering, a technique that clusters time series based on similarities in their spectral characteristics. We illustrate this technique with synthetic time series and apply wavelet clustering to densely sampled human gut microbiome time series. We compare our results with hierarchical clustering based on temporal correlations in abundance, within and across individuals, and show that the cluster trees obtained by using either method are significantly different in terms of elements clustered together, branching structure and total branch length. By capitalizing on the dynamic nature of the human microbiome, wavelet clustering revea...
Julia codes implementing the non-stationary surrogate algorithm described in: M. Chavez, B. Cazel... more Julia codes implementing the non-stationary surrogate algorithm described in: M. Chavez, B. Cazelles (2019). Detecting dynamic spatial correlation patterns with generalized wavelet coherence and non-stationary surrogate data. Scientific Reports, 9: 7389 Briefly, in contrast with classical methods, the surrogate data used here are realisations of a non-stationary stochastic process, preserving both the amplitude and time-frequency distributions of original data. To reproduce the results in the figure, please run the routine instructionsJulia.jl <strong>The instructions have been tested on Julia v 1.0</strong>. For older versions (e.g. v0.4), some instructions don't work.
Human microbiome research is helped by the characterization of microbial networks, as these may r... more Human microbiome research is helped by the characterization of microbial networks, as these may reveal key microbes that can be targeted for beneficial health effects. Prevailing methods of microbial network characterization are based on measures of association, often applied to limited sampling points in time. Here, we demonstrate the potential of wavelet clustering, a technique that clusters time series based on similarities in their spectral characteristics. We illustrate this technique with synthetic time series and apply wavelet clustering to densely sampled human gut microbiome time series. We compare our results with hierarchical clustering based on temporal correlations in abundance, within and across individuals, and show that the cluster trees obtained by using either method are significantly different in terms of elements clustered together, branching structure and total branch length. By capitalizing on the dynamic nature of the human microbiome, wavelet clustering revea...
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Papers by Bernard Cazelles