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Copy file name to clipboardExpand all lines: README.md
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3) Optional displaying credible intervals (CrIs) to visualize uncertainty.
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4) Preserving the familiar, intuitive volcano structure.
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We are not the first to think about the concept of Bayesian volcano plots [Sousa et al. 2020](https://doi.org/10.1016/j.aca.2019.11.006) introduced them as a single
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use case (their b-values correspond to our pi-values)
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but to our knowledge we are the first to provide an R-package
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for easy calculation of pi-values and visualization.
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## Installation
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result<- prepare_volcano_df(
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posterior=posterior,
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annotation_df=annotation_df,
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annotation_df=annotation_df
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)
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plot_volcano(result,
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color="group",
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```
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# References
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Julie de Sousa, Ondřej Vencálek, Karel Hron, Jan Václavík, David Friedecký, Tomáš Adam,
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Bayesian multiple hypotheses testing in compositional analysis of untargeted metabolomic data,
Copy file name to clipboardExpand all lines: vignettes/BayesVolcano.Rmd
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Bayesian volcano plots bring together the uncertainty aware power of Bayesian
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models and the familiar visualization of volcano plots.
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We are not the first to think about the concept of Bayesian volcano plots [Sousa et al. 2020](https://doi.org/10.1016/j.aca.2019.11.006) introduced them as a single
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use case (their b-values correspond to our pi-values)
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but to our knowledge we are the first to provide an R-package
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for easy calculation of pi-values and visualization.
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# Workflow
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## Preparation of plotting dataframe
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xlab = "My informative parameter")
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```
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# References
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Julie de Sousa, Ondřej Vencálek, Karel Hron, Jan Václavík, David Friedecký, Tomáš Adam,
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Bayesian multiple hypotheses testing in compositional analysis of untargeted metabolomic data,
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