volcano3D

The volcano3D package enables exploration of probes differentially expressed between three groups. Its main purpose is for the visualisation of differentially expressed genes in a three-dimensional volcano plot. These plots can be converted to interactive visualisations using plotly.

This vignette covers the basic features of the package using a subset example. To explore more extensive examples and tutorials 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 web tool available at https://peac.hpc.qmul.ac.uk.

This tool acts as a searchable interface to examine relationships between individual synovial and blood gene transcript levels and histological, clinical, and radiographic parameters, and clinical response at 6 months. An interactive interface allows the gene module analysis to be explored for relationships between modules and clinical parameters. The PEAC interactive web tool was creating as an R Shiny app and deployed to the web using a server.

There are also supplementary vignettes with further information on:

• setting up the input pvalues data frame
• using the volcano3D package to create and deploy a shiny app

Getting Started

Prerequisites

• ggplot2
• ggpubr
• ggrepel
• plotly

Install from CRAN

install.packages("volcano3D")

Install from Github

library(devtools)
install_github("KatrionaGoldmann/volcano3D")

Load the package

library(volcano3D)

Dictionary

Variables used in this vignette:

Examples

This vignette uses a subset data set to explore the functions. First we will set up a polar object, using the polar_coords function, which has inputs:

Creating Polar Coordinates

Using the example_data from PEAC we can create a polar object for differentially expressed genes. Here syn_example_rld is the log transformed expression data; syn_example_p is the pvalues data frame containing differential expression statistics about each gene; and syn_example_meta contains information about each sample.

data("example_data")

syn_polar <- polar_coords(sampledata = syn_example_meta,
                          contrast = "Pathotype",
                          pvalues = syn_example_p,
                          expression = syn_example_rld,
                          p_col_suffix = "pvalue",
                          padj_col_suffix = "padj",
                          fc_col_suffix = "log2FoldChange",
                          multi_group_prefix = "LRT",
                          non_sig_name = "Not Significant",
                          significance_cutoff = 0.01,
                          label_column = NULL,
                          fc_cutoff = 0.1)

The pvalues slot should now have at least two statistics for each comparison - pvalue and adjusted pvalue with an optional logarithmic fold change statistic also:

head(syn_polar@pvalues)

The sig column in syn_polar@polar allows us to determine relative differences in expression between groups (in this case pathotypes). The '+' indicates which pathotypes are significantly 'up' compared to others. For example:

• genes labelled 'Lymphoid+' are significantly up in Lymphoid vs Myeloid and Lymphoid vs Fibroid.

• genes up in two pathotypes such as 'Lymphoid+Myeloid+' are up in both Lymphoid and Myeloid, therefore Lymphoid vs Fibroid and Myeloid vs Fibroid are statistically significant.

• genes which show no significant difference between pathotypes are classed according to non_sig_name

This gives us:

setNames(data.frame(table(syn_polar@polar$sig)), c("Significance", "Frequency"))

Volcano Plots

If there is a fold change column previously provided, we can now investigate the comparisons between pathotypes using the volcano_trio function. This creates three ggplot outputs.

syn_plots <- volcano_trio(polar = syn_polar,
                          sig_names = c("not significant","significant",
                                        "not significant","significant"),
                          colours = rep(c("grey60",  "slateblue1"), 2),
                          text_size = 9,
                          marker_size=1,
                          shared_legend_size = 0.9,
                          label_rows = c("SLAMF6", "BOC", "FMOD"),
                          fc_line = FALSE, 
                          share_axes = FALSE)

syn_plots$All

Radial Plots

The differential expression can now be visualised on an interactive radar plot using radial_plotly. The labelRows variable allows any markers of interest to be labelled.

radial_plotly(polar = syn_polar, label_rows = c("SLAMF6", "GREM2", "FMOD")) 

By hovering over certain point you can also determine genes for future interrogation.

Similarly we can create a static ggplot image using radial_ggplot:

radial_ggplot(polar = syn_polar,
              label_rows = c("SLAMF6", "FMOD", "GREM2"),
              marker_size = 2.3,
              legend_size = 10) +
  theme(legend.position = "right")

Boxplots

We can then interrogate any one specific variable as a boxplot, to investigate these differences.

plot1 <- boxplot_trio(syn_polar,
                      value = "SLAMF6",
                      text_size = 7,
                      test = "polar_padj",
                      my_comparisons=list(c("Lymphoid", "Myeloid"),
                                          c("Lymphoid", "Fibroid")))

plot2 <- boxplot_trio(syn_polar,
                      value = "SLAMF6",
                      box_colours = c("violet", "gold2"),
                      levels_order = c("Lymphoid", "Fibroid"),
                      text_size = 7,
                      test = "polar_padj")

plot3 <- boxplot_trio(syn_polar,
                      value = "FMOD",
                      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)

Three Dimensional Volcano Plots

The final thing we can look at is the 3D volcano plot which projects differential gene expression onto cylindrical coordinates.

p <- volcano3D(syn_polar,
               label_rows = c("SLAMF6", "GREM2", "FMOD"),
               label_size = 10,
               xy_aspectratio = 1,
               z_aspectratio = 0.9, 
               plot_height = 700)
p

Again this produces an interactive plot, unfortunately WebGL is not supported in vignette html but to see this in action you can visit the extended vignette . If you have the orca command-line utility installed, this can be used to save static images. To install follow the instructions here.

orca(p, "./volcano_3d_synovium.svg", format = "svg")

Citation

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:

Lewis, Myles J., et al. Molecular portraits of early rheumatoid arthritis identify clinical and treatment response phenotypes. Cell reports 28.9 (2019): 2455-2470. or using:

citation("volcano3D")

## 
## To cite package 'volcano3D' in publications use:
## 
##   Katriona Goldmann and Myles Lewis (2020). volcano3D: Interactive
##   Plotting of Three-Way Differential Expression Analysis. R package
##   version 1.0.0. https://github.com/KatrionaGoldmann/volcano3D
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {volcano3D: Interactive Plotting of Three-Way Differential Expression
## Analysis},
##     author = {Katriona Goldmann and Myles Lewis},
##     year = {2020},
##     note = {R package version 1.0.0},
##     url = {https://github.com/KatrionaGoldmann/volcano3D},
##   }