knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE, fig.height = 7, fig.width=7, fig.align = "center") library(knitr) library(kableExtra)
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 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) with an interactive, searchable web tool available at https://peac.hpc.qmul.ac.uk. This was creating as an R Shiny app and deployed to the web using a server.
There are also supplementary vignettes with further information on:
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',")")
install.packages("volcano3D")
library(devtools) install_github("KatrionaGoldmann/volcano3D")
library(volcano3D)
This vignette uses a subset of the 500 genes from the PEAC dataset to explore the package functions. This can be loaded using:
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:
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.
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:
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:
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.
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.
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.
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)
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.
library(limma) library(edgeR) syn_tpm <- syn_txi$counts # raw counts resl <- voom_polar(~ 0 + Pathotype + Batch + Gender, syn_metadata, syn_tpm) volcano3D(resl)
The differential expression can now be visualised on an interactive radial plot
using radial_plotly
.
radial_plotly(syn_polar)
Unfortunately CRAN does not support interactive plotly in the vignette, but these can be viewed on the extended vignette. 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()
.
radial_plotly(syn_polar) %>% toWebGL()
A very similar looking static ggplot image can be created using radial_ggplot
:
radial_ggplot(syn_polar, marker_size = 2.3, legend_size = 10) + theme(legend.position = "right")
Any one specific variable (such as a gene) can be interrogated using a boxplot to investigate differences between groups:
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)
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.
p <- volcano3D(syn_polar) p
knitr::include_graphics("volcano3D.png")
A fully interactive demonstration of this plot can be viewed at: https://peac.hpc.qmul.ac.uk/
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:
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.
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:
p %>% plotly::config(toImageButtonOptions = list(format = "svg"))
The full plotly objects can be saved to HTML by converting them to widgets and saving with the htmlwidgets package:
htmlwidgets::saveWidget(as_widget(p), "volcano3D.html")
volcano3D was developed by the bioinformatics team at the Experimental Medicine & Rheumatology department and Centre for Translational Bioinformatics at Queen Mary University London.
If you use this package please cite as:
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.
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