---
title: "volcano3D"
author: "Katriona Goldmann, Myles Lewis"
output:
html_document:
toc: true
toc_float:
collapsed: false
toc_depth: 3
number_sections: false
vignette: >
%\VignetteIndexEntry{volcano3D}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
---
```{r setup, include = FALSE, echo = FALSE}
knitr::opts_chunk$set(echo = TRUE,
warning = FALSE,
message = FALSE,
fig.height = 7,
fig.width=7,
fig.align = "center")
library(knitr)
library(kableExtra)
```
# volcano3D
```{r, echo=FALSE}
library(ggplot2)
library(ggpubr)
library(plotly)
library(usethis)
```
The volcano3D package provides a tool for analysis of three-class high
dimensional data. It enables exploration of genes differentially expressed
between three groups. Its main purpose is for the visualisation of
differentially expressed genes in a three-dimensional volcano plot or three-way
polar plot. These plots can be converted to interactive visualisations using
plotly. The 3-way polar plots and 3d volcano plots can be applied to any data in
which multiple attributes have been measured and their relative levels are being
compared across three classes.
This vignette covers the basic features of the package using a small example
data set. To explore more extensive examples and view the interactive radial
and volcano plots, see the [extended vignette](https://katrionagoldmann.github.io/volcano3D/articles/Extended_Vignette.html)
which explores a case study from the PEAC
rheumatoid arthritis trial (Pathobiology of Early Arthritis Cohort).
The methodology has been published in
[Lewis, Myles J., et al. _Molecular portraits of early rheumatoid arthritis
identify clinical and treatment response phenotypes_. Cell reports 28.9 (2019):
2455-2470.
(DOI: 10.1016/j.celrep.2019.07.091)](https://doi.org/10.1016/j.celrep.2019.07.091)
with an interactive, searchable web tool available at
[https://peac.hpc.qmul.ac.uk](https://peac.hpc.qmul.ac.uk). This was creating as an
[R Shiny app](https://www.rstudio.com/products/shiny/) and deployed to the web using a server.
There are also supplementary vignettes with further information on:
- [using the volcano3D package to create and deploy a shiny app](https://katrionagoldmann.github.io/volcano3D/articles/shiny_builder.html)
[](https://lifecycle.r-lib.org/articles/stages.html)
[](https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html)
[](https://cran.r-project.org/package=volcano3D)
[](https://cranlogs.r-pkg.org/badges/grand-total/volcano3D)
`r paste0("[![", Sys.Date(),"]","(",paste0("https://img.shields.io/badge/last%20commit-", gsub('-', '--', Sys.Date()),"-turquoise.svg"), ")]","(",'https://github.com/KatrionaGoldmann/volcano3D/blob/master/NEWS.md',")")`
[](https://github.com/KatrionaGoldmann/volcano3D/actions/workflows/r.yml/badge.svg)
[](https://GitHub.com/KatrionaGoldmann/volcano3D/issues/)
## Getting Started
### Prerequisites
* [ggplot2](https://CRAN.R-project.org/package=ggplot2)
* [ggpubr](https://CRAN.R-project.org/package=ggpubr)
* [plotly](https://CRAN.R-project.org/package=plotly)
### Install from CRAN
[](https://cran.r-project.org/package=volcano3D)
```{r, eval = FALSE}
install.packages("volcano3D")
```
### Install from Github
[](https://GitHub.com/KatrionaGoldmann/volcano3D/tags/)
```{r, eval = FALSE}
library(devtools)
install_github("KatrionaGoldmann/volcano3D")
```
### Load the package
```{r}
library(volcano3D)
```
# Examples
This vignette uses a subset of the 500 genes from the PEAC dataset to explore
the package functions. This can be loaded using:
```{r}
data("example_data")
```
Which contains:
* `syn_example_rld` - the log transformed expression data
* `syn_example_meta` which contains sample information and divides the samples
into 3 classes.
Samples in this cohort fall into three histological 'pathotype' groups:
```{r}
kable(table(syn_example_meta$Pathotype), col.names = c("Pathotype", "Count"))
```
These will be used as the differential expression classes for the three-way
analysis.
## Creating Polar Coordinates
The function `polar_coords()` is used to map attributes to polar coordinates. If
you have RNA-Seq count data this step can be skipped and you can use functions
`deseq_polar()` or `voom_polar()` instead (see [Gene Expression pipeline]).
`polar_coords` accepts raw data and performs all the calculations needed to
generate coordinates, colours etc for plotting either a 3d volcano plot or
radial 3-way plot. In brief, the function calculates the mean of each attribute/
variable for each group and maps the mean level per group onto polar coordinates
along 3 axes in the x-y plane. The z axis is plotted as -log~10~(p-value) of the
group statistical test (e.g. likelihood ratio test, one-way ANOVA or
Kruskal-Wallis test).
A table of p-values can be supplied by the user (see table below for formatting
requirements). If a table of p-values is absent, p-values are automatically
calculated by `polar_coords()`. By default one-way ANOVA is used for the group
comparison and t-tests are used for pairwise tests.
`polar_coords()` has the following inputs:
```{r, echo=FALSE}
mytable = data.frame(
outcome = c("outcome\
\n\n(required)",
"Vector containing three-level factor indicating which of the three classes each sample belongs to."),
data = c("data\
\n\n(required)",
"A dataframe or matrix containing data to be compared between the three classes (e.g. gene expression data). Note that variables are in columns, so gene expression data will need to be transposed. This \
is used to calculate z-score and fold change, so for gene expression count data it should be \
normalised such as log transformed or variance stabilised count transformation."),
pvals = c("pvals\
\n\n(optional)",
"the pvals matrix which contains the statistical\
significance of probes or attributes between classes. This contains: \
\n * the first column is a group test such as one-way ANOVA or Kruskal-Wallis test.
\n * columns 2-4 contain p-values one for each comparison in the sequence A vs B, A vs C, B vs C, where A, B, C are the three levels in sequence in the outcome factor.
For gene expression RNA-Seq count data, conduit functions
using 'limma voom' or 'DESeq' pipelines to extract p-values for \
analysis are provided in functions `deseq_polar()` and `voom_polar()`.\
If p-values are not provided by the user, they can be calculated via the `polar_coords()` function.
"),
padj = c("padj\
\n\n(optional)",
"Matrix containing the adjusted p-values matching the pvals matrix."),
pcutoff = c("pcutoff", "Cut-off for p-value significance"),
scheme = c("scheme", "Vector of colours starting with non-significant attributes"),
labs = c("labs", 'Optional character vector for labelling classes. Default `NULL`
leads to abbreviated labels based on levels in `outcome` using
`abbreviate()`. A vector of length 3 with custom abbreviated names for the
outcome levels can be supplied. Otherwise a vector length 7 is expected, of
the form "ns", "B+", "B+C+", "C+", "A+C+", "A+", "A+B+", where "ns" means
non-significant and A, B, C refer to levels 1, 2, 3 in `outcome`, and must
be in the correct order.')
)
kable(t(mytable), row.names = FALSE, col.names = c("Variable", "Details")) %>%
kable_styling(font_size=11)
```
This can be applied to the example data as below:
```{r}
data("example_data")
syn_polar <- polar_coords(outcome = syn_example_meta$Pathotype,
data = t(syn_example_rld))
```
This creates a 'volc3d' class object for downstream plotting.
## Gene expression pipeline
RNA-Sequencing gene expression count data can be compared for differentially
expressed genes between 3 classes using 2 pipeline functions to allow
statistical analysis by Bioconductor packages 'DESeq2' and 'limma voom' to
quickly generate a polar plotting object of class 'volc3d' which can be plotted
either as a 2d polar plot with 3 axes or as a 3d cylindrical plot with a 3d
volcano plot.
Two functions `deseq_polar()` and `voom_polar()` are available. They both take
RNA-Seq count data objects as input and extract correct statistical results and
then internally call `polar_coords()` to create a 'volc3d' class object which
can be plotted straightaway.
### Method using DESeq2
This takes 2 `DESeqDataSet` objects and converts the results to a 'volc3d' class
object for plotting. `object` is an object of class 'DESeqDataSet' with the full
design formula. Note the function `DESeq` needs to have been previously run on
this object. `objectLRT` is an object of class 'DESeqDataSet' with the reduced
design formula. The function `DESeq` needs to have been run on this object with
`DESeq` argument `test="LRT"`.
Note that in the DESeq2 design formula, the 3-class variable of interest should
be first.
```{r eval=FALSE}
library(DESeq2)
# setup initial dataset from Tximport
dds <- DESeqDataSetFromTximport(txi = syn_txi,
colData = syn_metadata,
design = ~ Pathotype + Batch + Gender)
# initial analysis run
dds_DE <- DESeq(dds)
# likelihood ratio test on 'Pathotype'
dds_LRT <- DESeq(dds, test = "LRT", reduced = ~ Batch + Gender, parallel = TRUE)
# create 'volc3d' class object for plotting
res <- deseq_polar(dds_DE, dds_LRT, "Pathotype")
# plot 3d volcano plot
volcano3D(res)
```
### Method using limma voom
The method for limma voom is faster and takes a design formula, metadata and raw
count data. The Bioconductor packages 'limma' and 'edgeR' are used to analyse
the data using the 'voom' method. The results are converted to a 'volc3d' object
ready for plotting a 3d volcano plot or 3-way polar plot.
Note the design formula must be of the form `~ 0 + outcome + ...`. The 3-class
outcome variable must be the first variable after the '0', and this variable
must be a factor with exactly 3 levels.
```{r eval=FALSE}
library(limma)
library(edgeR)
syn_tpm <- syn_txi$counts # raw counts
resl <- voom_polar(~ 0 + Pathotype + Batch + Gender, syn_metadata, syn_tpm)
volcano3D(resl)
```
## Radial Plots
The differential expression can now be visualised on an interactive radial plot
using `radial_plotly`.
```{r, eval=FALSE}
radial_plotly(syn_polar)
```
Unfortunately CRAN does not support interactive plotly in the vignette, but
these can be viewed on the [extended vignette](https://katrionagoldmann.github.io/volcano3D/articles/Extended_Vignette.html).
When interactive, it is possible to identify genes for future interrogation by
hovering over certain markers.
`radial_plotly` produces an SVG based plotly object by default. With 10,000s of
points SVG can be slow, so for large number of points we recommend switching to
webGL by piping the plotly object to `toWebGL()`.
```{r, eval=FALSE}
radial_plotly(syn_polar) %>% toWebGL()
```
A very similar looking static ggplot image can be created using `radial_ggplot`:
```{r, fig.height=4.5, fig.width=7}
radial_ggplot(syn_polar,
marker_size = 2.3,
legend_size = 10) +
theme(legend.position = "right")
```
## Boxplots
Any one specific variable (such as a gene) can be interrogated using a boxplot to investigate
differences between groups:
```{r, fig.height = 3.2, fig.width = 7}
plot1 <- boxplot_trio(syn_polar,
value = "COBL",
text_size = 7,
test = "polar_padj",
my_comparisons=list(c("Lymphoid", "Myeloid"),
c("Lymphoid", "Fibroid")))
plot2 <- boxplot_trio(syn_polar,
value = "COBL",
box_colours = c("violet", "gold2"),
levels_order = c("Lymphoid", "Fibroid"),
text_size = 7,
test = "polar_padj"
)
plot3 <- boxplot_trio(syn_polar,
value = "TREX2",
text_size = 7,
stat_size=2.5,
test = "polar_multi_padj",
levels_order = c("Lymphoid", "Myeloid", "Fibroid"),
box_colours = c("blue", "red", "green3"))
ggarrange(plot1, plot2, plot3, ncol=3)
```
## 3D Volcano Plot
Lastly, the 3D volcano plot can be used to project 3-way differential
comparisons such as differential gene expression between 3 classes onto
cylindrical polar coordinates.
```{r, eval=FALSE, fig.height=5}
p <- volcano3D(syn_polar)
p
```
```{r volcano3D, echo = FALSE, message=FALSE, fig.align='center', out.width='80%', out.extra='style="border: 0;"'}
knitr::include_graphics("volcano3D.png")
```
A fully interactive demonstration of this plot can be viewed at:
https://peac.hpc.qmul.ac.uk/
## Spinning Animation
It is also possible to animate spinning/rotation of the plot in 3d using the
`add_animation` function which adds a custom plotly modeBar button:
```{r, eval=FALSE}
add_animation(p)
```
A working demo of this function can be viewed at https://peac.hpc.qmul.ac.uk/ by
clicking on the 'play' button in the plotly modeBar to spin the plot.
## Saving Plotly Plots
### Static Images
There are a few ways to save plotly plots as static images. Firstly plotly
offers a download button ( ) in
the figure mode bar (appears top right). By default this saves images as png,
however it is possible to convert to svg, jpeg or webp using:
```{r, eval=FALSE}
p %>% plotly::config(toImageButtonOptions = list(format = "svg"))
```
### Interactive HTML
The full plotly objects can be saved to HTML by converting them to widgets and
saving with the htmlwidgets package:
```{r, eval=FALSE}
htmlwidgets::saveWidget(as_widget(p), "volcano3D.html")
```
---
# Citation
volcano3D was developed by the bioinformatics team at the
[Experimental Medicine & Rheumatology department](https://www.qmul.ac.uk/whri/emr/)
and [Centre for Translational Bioinformatics](https://www.qmul.ac.uk/c4tb/) at
Queen Mary University London.
If you use this package please cite as:
```{r}
citation("volcano3D")
```
or using:
> Lewis, Myles J., et al. _Molecular portraits of early rheumatoid arthritis
identify clinical and treatment response phenotypes_. Cell reports 28.9 (2019):
2455-2470.
```{r, echo=FALSE}
library(ggplot2)
library(ggpubr)
library(plotly)
library(usethis)
```
The volcano3D package provides a tool for analysis of three-class high
dimensional data. It enables exploration of genes differentially expressed
between three groups. Its main purpose is for the visualisation of
differentially expressed genes in a three-dimensional volcano plot or three-way
polar plot. These plots can be converted to interactive visualisations using
plotly. The 3-way polar plots and 3d volcano plots can be applied to any data in
which multiple attributes have been measured and their relative levels are being
compared across three classes.
This vignette covers the basic features of the package using a small example
data set. To explore more extensive examples and view the interactive radial
and volcano plots, see the [extended vignette](https://katrionagoldmann.github.io/volcano3D/articles/Extended_Vignette.html)
which explores a case study from the PEAC
rheumatoid arthritis trial (Pathobiology of Early Arthritis Cohort).
The methodology has been published in
[Lewis, Myles J., et al. _Molecular portraits of early rheumatoid arthritis
identify clinical and treatment response phenotypes_. Cell reports 28.9 (2019):
2455-2470.
(DOI: 10.1016/j.celrep.2019.07.091)](https://doi.org/10.1016/j.celrep.2019.07.091)
with an interactive, searchable web tool available at
[https://peac.hpc.qmul.ac.uk](https://peac.hpc.qmul.ac.uk). This was creating as an
[R Shiny app](https://www.rstudio.com/products/shiny/) and deployed to the web using a server.
There are also supplementary vignettes with further information on:
- [using the volcano3D package to create and deploy a shiny app](https://katrionagoldmann.github.io/volcano3D/articles/shiny_builder.html)
[](https://lifecycle.r-lib.org/articles/stages.html)
[](https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html)
[](https://cran.r-project.org/package=volcano3D)
[](https://cranlogs.r-pkg.org/badges/grand-total/volcano3D)
`r paste0("[![", Sys.Date(),"]","(",paste0("https://img.shields.io/badge/last%20commit-", gsub('-', '--', Sys.Date()),"-turquoise.svg"), ")]","(",'https://github.com/KatrionaGoldmann/volcano3D/blob/master/NEWS.md',")")`
[](https://github.com/KatrionaGoldmann/volcano3D/actions/workflows/r.yml/badge.svg)
[](https://GitHub.com/KatrionaGoldmann/volcano3D/issues/)
## Getting Started
### Prerequisites
* [ggplot2](https://CRAN.R-project.org/package=ggplot2)
* [ggpubr](https://CRAN.R-project.org/package=ggpubr)
* [plotly](https://CRAN.R-project.org/package=plotly)
### Install from CRAN
[](https://cran.r-project.org/package=volcano3D)
```{r, eval = FALSE}
install.packages("volcano3D")
```
### Install from Github
[](https://GitHub.com/KatrionaGoldmann/volcano3D/tags/)
```{r, eval = FALSE}
library(devtools)
install_github("KatrionaGoldmann/volcano3D")
```
### Load the package
```{r}
library(volcano3D)
```
# Examples
This vignette uses a subset of the 500 genes from the PEAC dataset to explore
the package functions. This can be loaded using:
```{r}
data("example_data")
```
Which contains:
* `syn_example_rld` - the log transformed expression data
* `syn_example_meta` which contains sample information and divides the samples
into 3 classes.
Samples in this cohort fall into three histological 'pathotype' groups:
```{r}
kable(table(syn_example_meta$Pathotype), col.names = c("Pathotype", "Count"))
```
These will be used as the differential expression classes for the three-way
analysis.
## Creating Polar Coordinates
The function `polar_coords()` is used to map attributes to polar coordinates. If
you have RNA-Seq count data this step can be skipped and you can use functions
`deseq_polar()` or `voom_polar()` instead (see [Gene Expression pipeline]).
`polar_coords` accepts raw data and performs all the calculations needed to
generate coordinates, colours etc for plotting either a 3d volcano plot or
radial 3-way plot. In brief, the function calculates the mean of each attribute/
variable for each group and maps the mean level per group onto polar coordinates
along 3 axes in the x-y plane. The z axis is plotted as -log~10~(p-value) of the
group statistical test (e.g. likelihood ratio test, one-way ANOVA or
Kruskal-Wallis test).
A table of p-values can be supplied by the user (see table below for formatting
requirements). If a table of p-values is absent, p-values are automatically
calculated by `polar_coords()`. By default one-way ANOVA is used for the group
comparison and t-tests are used for pairwise tests.
`polar_coords()` has the following inputs:
```{r, echo=FALSE}
mytable = data.frame(
outcome = c("outcome\
\n\n(required)",
"Vector containing three-level factor indicating which of the three classes each sample belongs to."),
data = c("data\
\n\n(required)",
"A dataframe or matrix containing data to be compared between the three classes (e.g. gene expression data). Note that variables are in columns, so gene expression data will need to be transposed. This \
is used to calculate z-score and fold change, so for gene expression count data it should be \
normalised such as log transformed or variance stabilised count transformation."),
pvals = c("pvals\
\n\n(optional)",
"the pvals matrix which contains the statistical\
significance of probes or attributes between classes. This contains: \
\n * the first column is a group test such as one-way ANOVA or Kruskal-Wallis test.
\n * columns 2-4 contain p-values one for each comparison in the sequence A vs B, A vs C, B vs C, where A, B, C are the three levels in sequence in the outcome factor.
For gene expression RNA-Seq count data, conduit functions
using 'limma voom' or 'DESeq' pipelines to extract p-values for \
analysis are provided in functions `deseq_polar()` and `voom_polar()`.\
If p-values are not provided by the user, they can be calculated via the `polar_coords()` function.
"),
padj = c("padj\
\n\n(optional)",
"Matrix containing the adjusted p-values matching the pvals matrix."),
pcutoff = c("pcutoff", "Cut-off for p-value significance"),
scheme = c("scheme", "Vector of colours starting with non-significant attributes"),
labs = c("labs", 'Optional character vector for labelling classes. Default `NULL`
leads to abbreviated labels based on levels in `outcome` using
`abbreviate()`. A vector of length 3 with custom abbreviated names for the
outcome levels can be supplied. Otherwise a vector length 7 is expected, of
the form "ns", "B+", "B+C+", "C+", "A+C+", "A+", "A+B+", where "ns" means
non-significant and A, B, C refer to levels 1, 2, 3 in `outcome`, and must
be in the correct order.')
)
kable(t(mytable), row.names = FALSE, col.names = c("Variable", "Details")) %>%
kable_styling(font_size=11)
```
This can be applied to the example data as below:
```{r}
data("example_data")
syn_polar <- polar_coords(outcome = syn_example_meta$Pathotype,
data = t(syn_example_rld))
```
This creates a 'volc3d' class object for downstream plotting.
## Gene expression pipeline
RNA-Sequencing gene expression count data can be compared for differentially
expressed genes between 3 classes using 2 pipeline functions to allow
statistical analysis by Bioconductor packages 'DESeq2' and 'limma voom' to
quickly generate a polar plotting object of class 'volc3d' which can be plotted
either as a 2d polar plot with 3 axes or as a 3d cylindrical plot with a 3d
volcano plot.
Two functions `deseq_polar()` and `voom_polar()` are available. They both take
RNA-Seq count data objects as input and extract correct statistical results and
then internally call `polar_coords()` to create a 'volc3d' class object which
can be plotted straightaway.
### Method using DESeq2
This takes 2 `DESeqDataSet` objects and converts the results to a 'volc3d' class
object for plotting. `object` is an object of class 'DESeqDataSet' with the full
design formula. Note the function `DESeq` needs to have been previously run on
this object. `objectLRT` is an object of class 'DESeqDataSet' with the reduced
design formula. The function `DESeq` needs to have been run on this object with
`DESeq` argument `test="LRT"`.
Note that in the DESeq2 design formula, the 3-class variable of interest should
be first.
```{r eval=FALSE}
library(DESeq2)
# setup initial dataset from Tximport
dds <- DESeqDataSetFromTximport(txi = syn_txi,
colData = syn_metadata,
design = ~ Pathotype + Batch + Gender)
# initial analysis run
dds_DE <- DESeq(dds)
# likelihood ratio test on 'Pathotype'
dds_LRT <- DESeq(dds, test = "LRT", reduced = ~ Batch + Gender, parallel = TRUE)
# create 'volc3d' class object for plotting
res <- deseq_polar(dds_DE, dds_LRT, "Pathotype")
# plot 3d volcano plot
volcano3D(res)
```
### Method using limma voom
The method for limma voom is faster and takes a design formula, metadata and raw
count data. The Bioconductor packages 'limma' and 'edgeR' are used to analyse
the data using the 'voom' method. The results are converted to a 'volc3d' object
ready for plotting a 3d volcano plot or 3-way polar plot.
Note the design formula must be of the form `~ 0 + outcome + ...`. The 3-class
outcome variable must be the first variable after the '0', and this variable
must be a factor with exactly 3 levels.
```{r eval=FALSE}
library(limma)
library(edgeR)
syn_tpm <- syn_txi$counts # raw counts
resl <- voom_polar(~ 0 + Pathotype + Batch + Gender, syn_metadata, syn_tpm)
volcano3D(resl)
```
## Radial Plots
The differential expression can now be visualised on an interactive radial plot
using `radial_plotly`.
```{r, eval=FALSE}
radial_plotly(syn_polar)
```
Unfortunately CRAN does not support interactive plotly in the vignette, but
these can be viewed on the [extended vignette](https://katrionagoldmann.github.io/volcano3D/articles/Extended_Vignette.html).
When interactive, it is possible to identify genes for future interrogation by
hovering over certain markers.
`radial_plotly` produces an SVG based plotly object by default. With 10,000s of
points SVG can be slow, so for large number of points we recommend switching to
webGL by piping the plotly object to `toWebGL()`.
```{r, eval=FALSE}
radial_plotly(syn_polar) %>% toWebGL()
```
A very similar looking static ggplot image can be created using `radial_ggplot`:
```{r, fig.height=4.5, fig.width=7}
radial_ggplot(syn_polar,
marker_size = 2.3,
legend_size = 10) +
theme(legend.position = "right")
```
## Boxplots
Any one specific variable (such as a gene) can be interrogated using a boxplot to investigate
differences between groups:
```{r, fig.height = 3.2, fig.width = 7}
plot1 <- boxplot_trio(syn_polar,
value = "COBL",
text_size = 7,
test = "polar_padj",
my_comparisons=list(c("Lymphoid", "Myeloid"),
c("Lymphoid", "Fibroid")))
plot2 <- boxplot_trio(syn_polar,
value = "COBL",
box_colours = c("violet", "gold2"),
levels_order = c("Lymphoid", "Fibroid"),
text_size = 7,
test = "polar_padj"
)
plot3 <- boxplot_trio(syn_polar,
value = "TREX2",
text_size = 7,
stat_size=2.5,
test = "polar_multi_padj",
levels_order = c("Lymphoid", "Myeloid", "Fibroid"),
box_colours = c("blue", "red", "green3"))
ggarrange(plot1, plot2, plot3, ncol=3)
```
## 3D Volcano Plot
Lastly, the 3D volcano plot can be used to project 3-way differential
comparisons such as differential gene expression between 3 classes onto
cylindrical polar coordinates.
```{r, eval=FALSE, fig.height=5}
p <- volcano3D(syn_polar)
p
```
```{r volcano3D, echo = FALSE, message=FALSE, fig.align='center', out.width='80%', out.extra='style="border: 0;"'}
knitr::include_graphics("volcano3D.png")
```
A fully interactive demonstration of this plot can be viewed at:
https://peac.hpc.qmul.ac.uk/
## Spinning Animation
It is also possible to animate spinning/rotation of the plot in 3d using the
`add_animation` function which adds a custom plotly modeBar button:
```{r, eval=FALSE}
add_animation(p)
```
A working demo of this function can be viewed at https://peac.hpc.qmul.ac.uk/ by
clicking on the 'play' button in the plotly modeBar to spin the plot.
## Saving Plotly Plots
### Static Images
There are a few ways to save plotly plots as static images. Firstly plotly
offers a download button ( ) in
the figure mode bar (appears top right). By default this saves images as png,
however it is possible to convert to svg, jpeg or webp using:
```{r, eval=FALSE}
p %>% plotly::config(toImageButtonOptions = list(format = "svg"))
```
### Interactive HTML
The full plotly objects can be saved to HTML by converting them to widgets and
saving with the htmlwidgets package:
```{r, eval=FALSE}
htmlwidgets::saveWidget(as_widget(p), "volcano3D.html")
```
---
# Citation
volcano3D was developed by the bioinformatics team at the
[Experimental Medicine & Rheumatology department](https://www.qmul.ac.uk/whri/emr/)
and [Centre for Translational Bioinformatics](https://www.qmul.ac.uk/c4tb/) at
Queen Mary University London.
If you use this package please cite as:
```{r}
citation("volcano3D")
```
or using:
> Lewis, Myles J., et al. _Molecular portraits of early rheumatoid arthritis
identify clinical and treatment response phenotypes_. Cell reports 28.9 (2019):
2455-2470.