The volcano3D package provides a tool for analysis of threeclass 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 threedimensional volcano plot or threeway polar plot. These plots can be converted to interactive visualisations using plotly. The 3way 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): 24552470. (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:
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"))
Pathotype  Count 

Lymphoid  45 
Myeloid  20 
Fibroid  16 
These will be used as the differential expression classes for the threeway analysis.
First, the differential expression can be mapped to polar coordinates using the polar_coords function, which has inputs:
Variable  Details 

outcome (required) 
Vector containing threelevel factor indicating which of the three classes each sample belongs to. 
data (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 zscore and fold change, so for gene expression count data it should be normalised such as log transformed or variance stabilised count transformation. 
pvals (optional) 
the pvals matrix which contains the statistical significance of probes or attributes between classes. This contains:

padj (optional) 
Matrix containing the adjusted pvalues matching the pvals matrix. 
pcutoff  Cutoff for pvalue significance 
scheme  Vector of colours starting with nonsignificant attributes 
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 nonsignificant and A, B, C
refer to levels 1, 2, 3 in outcome , and must be in the
correct order.

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.
RNASequencing 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.
This requires 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 3class 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 3way polar plot.
Note the design formula must be of the form
~ 0 + outcome + ...
. The 3class 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.
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")