Type:	Package
Title:	3D Volcano Plots and Polar Plots for Three-Class Data
Version:	2.0.9
Maintainer:	Katriona Goldmann <katrionagoldmann@gmail.com>
URL:	https://katrionagoldmann.github.io/volcano3D/index.html, https://github.com/KatrionaGoldmann/volcano3D
BugReports:	https://github.com/KatrionaGoldmann/volcano3D/issues
Description:	Generates interactive plots for analysing and visualising three-class high dimensional data. It is particularly suited to visualising differences in continuous attributes such as gene/protein/biomarker expression levels between three groups. Differential gene/biomarker expression analysis between two classes is typically shown as a volcano plot. However, with three groups this type of visualisation is particularly difficult to interpret. This package generates 3D volcano plots and 3-way polar plots for easier interpretation of three-class data.
Language:	en-gb
License:	GPL-2
Encoding:	UTF-8
LazyData:	true
Depends:	R (≥ 3.5)
VignetteBuilder:	knitr
RoxygenNote:	7.2.3
NeedsCompilation:	no
Packaged:	2023-05-17 10:02:31 UTC; kgoldmann
Imports:	plotly, ggplot2, ggpubr, htmlwidgets, magrittr, methods, Rfast, rlang, matrixTests
Suggests:	DESeq2, edgeR, limma, qvalue, SummarizedExperiment, knitr, rmarkdown, kableExtra, usethis, easylabel
Author:	Katriona Goldmann [aut, cre], Myles Lewis [aut]
Repository:	CRAN
Date/Publication:	2023-05-17 11:00:02 UTC

Add mode bar button to rotate the plot

Description

Add mode bar button to rotate the plot

Usage

add_animation(
  p,
  rotate_icon_path = NULL,
  stop_icon_path = NULL,
  rotate_colour = "#c7c7c7",
  stop_colour = "#ff6347",
  scale = "scale(0.4) translate(-4, -4)",
  speed = 320,
  shiny_event_names = c()
)

Arguments

Value

Returns a rotating cylindrical 3D plotly plot featuring variables on a tri-axis radial graph with the -log10(multi-group test p-value) on the z-axis

References

Lewis, Myles J., et al. (2019). Molecular portraits of early rheumatoid arthritis identify clinical and treatment response phenotypes. Cell reports, 28:9

See Also

volcano3D

Examples

data(example_data)
syn_polar <- polar_coords(outcome = syn_example_meta$Pathotype,
                          data = t(syn_example_rld))

p <- volcano3D(syn_polar, 
    label_rows = c("COBL", "TREX2"))
add_animation(p)

Boxplot to compare groups

Description

Plots the expression of a specific row in expression to compare the three groups in a boxplot using either ggplot or plotly.

Usage

boxplot_trio(
  polar,
  value,
  box_colours = c("green3", "blue", "red"),
  test = "polar_pvalue",
  levels_order = NULL,
  my_comparisons = NULL,
  text_size = 10,
  stat_colour = "black",
  stat_size = 3,
  step_increase = 0.05,
  plot_method = "ggplot",
  ...
)

Arguments

Value

Returns a boxplot featuring the differential expression between groups in comparison with annotated pvalues.

References

Lewis, Myles J., et al. (2019). Molecular portraits of early rheumatoid arthritis identify clinical and treatment response phenotypes. Cell reports, 28:9

Examples

data(example_data)
syn_polar <- polar_coords(outcome = syn_example_meta$Pathotype,
                          data = t(syn_example_rld))

boxplot_trio(syn_polar, value = "COBL", plot_method="plotly")
boxplot_trio(syn_polar, value = "COBL")

Calculate one-way test and pairwise tests

Description

Internal function for calculating 3-class group test (either one-way ANOVA or Kruskal-Wallis test) and pairwise tests (either t-test or Wilcoxon test) on multi-column data against an outcome parameter with 3 levels.

Usage

calc_pvals(
  outcome,
  data,
  pcutoff = 0.05,
  padj.method = "BH",
  group_test = c("anova", "kruskal.test"),
  pairwise_test = c("t.test", "wilcoxon"),
  exact = FALSE,
  filter_pairwise = TRUE
)

Arguments

Value

Returns a list with first element representing a data frame of unadjusted p-values and the second element adjusted p-values. Each dataframe contains 4 columns: the first column is the 3-way comparison (LRT or ANOVA). Columns 2-4 are pairwise comparisons between groups A vs B, A vs C and B vs C, where A, B, C are the 3 levels in the outcome factor.

Calculate p-values for 2x3-way analysis

Description

Calculate p-values for 2x3-way analysis

Usage

calc_stats_2x3(
  data,
  outcome,
  group,
  padj.method,
  test = c("t.test", "wilcoxon"),
  exact = FALSE
)

Arguments

Value

A list containing a data frame with summary statistics for the comparisons between the outcome, for each group level.

2 x 3 factor DESeq2 analysis

Description

Experimental function for performing 2x3 factor DESeq2 analyses. Output can be passed to deseq_2x3_polar() and subsequently plotted. Example usage would include comparing gene expression against a binary outcome e.g. response vs non-response, across 3 drugs: the design would be ~ response and group would refer to the medication column in the metadata.

Usage

deseq_2x3(object, design, group, ...)

Arguments

Value

Returns a list of 3 DESeq2 results objects which can be passed onto deseq_2x3_polar()

Examples

# Basic DESeq2 set up
library(DESeq2)

counts <- matrix(rnbinom(n=3000, mu=100, size=1/0.5), ncol=30)
rownames(counts) <- paste0("gene", 1:100)
cond <- rep(factor(rep(1:3, each=5), labels = c('A', 'B', 'C')), 2)
resp <- factor(rep(1:2, each=15), labels = c('non.responder', 'responder'))
metadata <- data.frame(drug = cond, response = resp)

# Full dataset object construction
dds <- DESeqDataSetFromMatrix(counts, metadata, ~response)

# Perform 3x DESeq2 analyses comparing binary response for each drug
res <- deseq_2x3(dds, ~response, "drug")

# Generate polar object
obj <- deseq_2x3_polar(res)

# 2d plot
radial_plotly(obj)

# 3d plot
volcano3D(obj)

Convert 2x3 design DESeq2 objects to a volcano3d object

Description

This function is used instead of polar_coords if you have raw RNA-Seq count data. It takes results from a 2x3 design DESeq2 analysis using deseq_2x3(). Alternatively it will accept a list of 3 DESeqDataSet or DESeqResults objects each with the same type of binary comparison across 3 groups. Statistical results are extracted and converted to a 'volc3d' object, which can be directly plotted. Example usage would include comparing gene expression against a binary outcome e.g. response vs non-response, across 3 drugs.

Usage

deseq_2x3_polar(
  object,
  pcutoff = 0.05,
  fc_cutoff = NULL,
  padj.method = "BH",
  process = c("positive", "negative", "two.sided"),
  scheme = c("grey60", "red", "gold2", "green3", "cyan", "blue", "purple", "black"),
  labs = NULL
)

Arguments

Details

This function generates a 'volc3d' class object for visualising a 2x3 way analysis for RNA-Seq data. For usual workflow it is typically preceded by a call to deseq_2x3() which runs the 3x DESeq2 analyses required.

Scaled polar coordinates are based on the DESeq2 statistic for each group comparison. Unscaled polar coordinates are generated using the log2 fold change for each group comparison.

The z axis for 3d volcano plots does not have as clear a corollary in 2x3 analysis as for the standard 3-way analysis (which uses the likelihood ratio test for the 3 groups). For 2x3 polar analysis the smallest p-value from the 3 group pairwise comparisons for each gene is used to generate a z coordinate as -log10(p-value).

The colour scheme is not as straightforward as for the standard polar plot and volcano3D plot since genes (or attributes) can be significantly up or downregulated in the response comparison for each of the 3 groups. process = "positive" means that genes are labelled with colours if a gene is significantly upregulated in the response for that group. This uses the primary colours (RGB) so that if a gene is upregulated in both red and blue groups it becomes purple etc with secondary colours. If the gene is upregulated in all 3 groups it is labelled black. Non-significant genes are in grey.

With process = "negative" genes are coloured when they are significantly downregulated. With process = "two.sided" the colour scheme means that both significantly up- and down-regulated genes are coloured with downregulated genes labelled with inverted colours (i.e. cyan is the inverse of red etc). However, significant upregulation in a group takes precedence.

Value

Returns an S4 'volc3d' object containing:

• 'df' A list of 2 dataframes. Each dataframe contains both x,y,z coordinates as well as polar coordinates r, angle. The first dataframe has coordinates based on the DESeq2 statistic. The 2nd dataframe is unscaled and represents log2 fold change for gene expression. The type argument in volcano3D, radial_plotly and radial_ggplot corresponds to these dataframes.

• 'outcome' An empty factor whose levels are the three-group contrast factor for comparisons

• 'data' Empty dataframe for compatibility

• 'pvals' A dataframe containing p-values. Columns 1-3 are pairwise comparisons between the outcome factor for groups A, B, C respectively.

• 'padj' A dataframe containing p-values adjusted for multiple testing

• 'pcutoff Numeric value for cut-off for p-value significance

• 'scheme' Character vector with colour scheme for plotting

• 'labs' Character vector with labels for colour groups

See Also

deseq_2x3()

Convert DESeq2 objects to a volcano3d object

Description

This function is used instead of polar_coords if you have raw RNA-Seq count data. It takes 2 DESeqDataSet objects, extracts statistical results and converts the results to a 'volc3d' object, which can be directly plotted.

Usage

deseq_polar(
  object,
  objectLRT,
  contrast = NULL,
  data = NULL,
  pcutoff = 0.05,
  padj.method = "BH",
  filter_pairwise = TRUE,
  ...
)

Arguments

Value

Calls polar_coords to return an S4 'volc3d' object

See Also

polar_coords, voom_polar, DESeq in the DESeq2 package

Examples

  library(DESeq2)

  counts <- matrix(rnbinom(n=1500, mu=100, size=1/0.5), ncol=15)
  cond <- factor(rep(1:3, each=5), labels = c('A', 'B', 'C'))

  # object construction
  dds <- DESeqDataSetFromMatrix(counts, DataFrame(cond), ~ cond)

  # standard analysis
  dds <- DESeq(dds)

  # Likelihood ratio test
  ddsLRT <- DESeq(dds, test="LRT", reduced= ~ 1)

  polar <- deseq_polar(dds, ddsLRT, "cond")
  volcano3D(polar)
  radial_ggplot(polar)

Forest plot individual gene from 2x3 factor analysis

Description

Forest plot individual gene from 2x3 factor analysis using either base graphics, plotly or ggplot2.

Usage

forest_plot(
  object,
  genes,
  scheme = c("red", "green3", "blue"),
  labs = NULL,
  error_type = c("ci", "se"),
  error_width = 0.05,
  gap = 1,
  transpose = FALSE,
  mar = if (transpose) c(5, 7, 5, 4) else c(5, 5, 5, 3),
  ...
)

forest_plotly(
  object,
  genes,
  scheme = c("red", "green3", "blue"),
  labs = NULL,
  error_type = c("ci", "se"),
  error_width = 4,
  gap = 1,
  transpose = FALSE,
  ...
)

forest_ggplot(
  object,
  genes,
  scheme = c("red", "green3", "blue"),
  labs = NULL,
  error_type = c("ci", "se"),
  error_width = 0.3,
  facet = TRUE,
  gap = 1,
  transpose = FALSE,
  ...
)

Arguments

Value

Returns a plot using either base graphics (forest_plot), plotly (forest_plotly) or ggplot2 (forest_ggplot). forest_plot also invisibly returns the dataframe used for plotting.

See Also

deseq_2x3_polar()

An S4 class to define the polar grid coordinates system.

Description

An S4 class to define the polar grid coordinates system.

Slots

polar_grid

The coordinates for the cylindrical grid segments with x,y,z coordinates

axes

The axes features for 'plotly'

axis_labs

The axis labels

r

The grid radius

z

The grid height

text_coords

data frame for axis label cartesian coordinates (x, y, z)

n_r_breaks

The number of ticks on the r axis

n_z_breaks

The number of ticks on the z axis

r_breaks

The r axis ticks as a numeric

z_breaks

The z axis ticks as a numeric

Coordinates for Three Way Polar Plot

Description

This function creates a 'volc3d' object of S4 class for downstream plots containing the p-values from a three-way group comparison, expression data sample data and polar coordinates. For RNA-Seq count data, two functions deseq_polar or voom_polar can be used instead.

Usage

polar_coords(
  outcome,
  data,
  pvals = NULL,
  padj = pvals,
  pcutoff = 0.05,
  fc_cutoff = NULL,
  scheme = c("grey60", "red", "gold2", "green3", "cyan", "blue", "purple"),
  labs = NULL,
  ...
)

Arguments

Value

Returns an S4 'volc3d' object containing:

• 'df' A list of 2 dataframes. Each dataframe contains both x,y,z coordinates as well as polar coordinates r, angle. The first dataframe has coordinates on scaled data. The 2nd dataframe has unscaled data (e.g. log2 fold change for gene expression). The type argument in volcano3D, radial_plotly and radial_ggplot corresponds to these dataframes.

• 'outcome' The three-group contrast factor used for comparisons

• 'data' Dataframe or matrix containing the expression data

• 'pvals' A dataframe containing p-values. First column is the 3-way comparison (LRT or ANOVA). Columns 2-4 are pairwise comparisons between groups A vs B, A vs C and B vs C, where A, B, C are the 3 levels in the outcome factor.

• 'padj' A dataframe containing p-values adjusted for multiple testing

• 'pcutoff Numeric value for cut-off for p-value significance

• 'scheme' Character vector with colour scheme for plotting

• 'labs' Character vector with labels for colour groups

See Also

deseq_polar, voom_polar, calc_pvals

Examples

data(example_data)
syn_polar <- polar_coords(outcome = syn_example_meta$Pathotype,
                          data = t(syn_example_rld))

Coordinates for three way polar plot from 2x3 factor analysis

Description

This function creates a 'volc3d' object of S4 class for downstream plots containing the p-values from a 2x3 factor analysis, expression data sample data and polar coordinates. For RNA-Seq count data, two functions deseq_2x3 followed by deseq_2x3_polar() can be used instead.

Usage

polar_coords_2x3(
  data,
  metadata = NULL,
  outcome,
  group,
  pvals = NULL,
  padj = pvals,
  pcutoff = 0.05,
  fc_cutoff = NULL,
  padj.method = "BH",
  process = c("positive", "negative", "two.sided"),
  scheme = c("grey60", "red", "gold2", "green3", "cyan", "blue", "purple", "black"),
  labs = NULL,
  ...
)

Arguments

Details

This function is designed for manually generating a 'volc3d' class object for visualising a 2x3 way analysis comparing a large number of attributes such as genes. For RNA-Seq data we suggest using deseq_2x3() and deseq_2x3_polar() functions in sequence instead.

Scaled polar coordinates are generated using the t-score for each group comparison. Unscaled polar coordinates are generated as difference between means for each group comparison. If p-values are not supplied they are calculated by calc_stats_2x3() using either t-tests or wilcoxon tests.

The z axis for 3d volcano plots does not have as clear a corollary in 2x3 analysis as for the standard 3-way analysis (which uses the likelihood ratio test for the 3 groups). For 2x3 polar analysis the smallest p-value from the 3 group pairwise comparisons for each gene is used to generate a z coordinate as -log10(p-value).

The colour scheme is not as straightforward as for the standard polar plot and volcano3D plot since genes (or attributes) can be significantly up or downregulated in the response comparison for each of the 3 groups. process = "positive" means that genes are labelled with colours if a gene is significantly upregulated in the response for that group. This uses the primary colours (RGB) so that if a gene is upregulated in both red and blue group it becomes purple etc with secondary colours. If the gene is upregulated in all 3 groups it is labelled black. Non-significant genes are in grey.

With process = "negative" genes are coloured when they are significantly downregulated. With process = "two.sided" the colour scheme means that both significantly up- and down-regulated genes are coloured with downregulated genes labelled with inverted colours (i.e. cyan is the inverse of red etc). However, significant upregulation in a group takes precedence.

Value

Returns an S4 'volc3d' object containing:

• 'df' A list of 2 dataframes. Each dataframe contains both x,y,z coordinates as well as polar coordinates r, angle. The first dataframe has coordinates on scaled data. The 2nd dataframe has unscaled data (e.g. log2 fold change for gene expression). The type argument in volcano3D, radial_plotly and radial_ggplot corresponds to these dataframes.

• 'outcome' The three-group contrast factor used for comparisons, linked to the group column

• 'data' Dataframe or matrix containing the expression data

• 'pvals' A dataframe containing p-values in 3 columns representing the binary comparison for the outcome for each of the 3 groups.

• 'padj' A dataframe containing p-values adjusted for multiple testing

• 'pcutoff Numeric value for cut-off for p-value significance

• 'scheme' Character vector with colour scheme for plotting

• 'labs' Character vector with labels for colour groups

See Also

deseq_2x3, deseq_2x3_polar, calc_stats_2x3

Grid required for 3D volcano plot and 2D radial plots

Description

Generates a cylindrical grid of the appropriate dimensions for a 3D volcano plot

Usage

polar_grid(
  r_vector = NULL,
  z_vector = NULL,
  r_axis_ticks = NULL,
  z_axis_ticks = NULL,
  axis_angle = 5/6,
  n_spokes = 12,
  axes_from_origin = TRUE,
  ...
)

Arguments

Value

Returns an S4 grid object containing:

• 'polar_grid' The coordinates for a radial grid

• 'axes' The axes features for 'plotly'

• 'axis_labels' The axis labels

• 'r' The grid radius

• 'z' The grid height

• 'text_coords' The coordinates for text labels

• 'n_r_breaks' The number of ticks on the r axis

• 'n_r_breaks' The number of ticks on the z axis

References

Lewis, Myles J., et al. (2019). Molecular portraits of early rheumatoid arthritis identify clinical and treatment response phenotypes. Cell reports, 28:9

Examples

data(example_data)
syn_polar <- polar_coords(outcome = syn_example_meta$Pathotype,
                          data = t(syn_example_rld))

grid <- polar_grid(r_vector=syn_polar@df[[1]]$r,
           z_vector=NULL,
           r_axis_ticks = NULL,
           z_axis_ticks = c(0, 8, 16, 32),
           n_spokes = 4)

'Ggplot' for Three Way Polar Plot

Description

This function creates a 3-way polar plot using 'ggplot' for a three-class comparison.

Usage

radial_ggplot(
  polar,
  type = 1,
  colours = NULL,
  label_rows = NULL,
  arrow_length = 1,
  label_size = 5,
  colour_code_labels = FALSE,
  label_colour = "black",
  grid_colour = "grey80",
  grid_width = 0.7,
  axis_colour = "black",
  axis_width = 1,
  axis_title_size = 5,
  axis_label_size = 3,
  marker_alpha = 0.7,
  marker_size = 3,
  marker_outline_colour = "white",
  marker_outline_width = 0.5,
  legend_size = 20,
  ...
)

Arguments

Value

Returns a polar 'ggplot' object featuring variables on a tri-axis radial graph

References

Lewis, Myles J., et al. (2019). Molecular portraits of early rheumatoid arthritis identify clinical and treatment response phenotypes. Cell reports, 28:9

See Also

polar_coords

Examples

data(example_data)
syn_polar <- polar_coords(outcome = syn_example_meta$Pathotype,
                          data = t(syn_example_rld))

radial_ggplot(polar = syn_polar, label_rows = c("COBL"))

Three-way radial comparison Polar Plot (using plotly)

Description

This function creates an interactive plotly object which maps differential expression onto a polar coordinates.

Usage

radial_plotly(
  polar,
  type = 1,
  colours = polar@scheme,
  label_rows = NULL,
  arrow_length = 80,
  label_size = 14,
  colour_code_labels = FALSE,
  label_colour = "black",
  grid_colour = "grey80",
  grid_width = 1,
  marker_size = 7,
  marker_alpha = 0.8,
  marker_outline_colour = "white",
  marker_outline_width = 0.5,
  axis_title_size = 16,
  axis_label_size = 10,
  axis_colour = "black",
  axis_width = 2,
  ...
)

Arguments

Details

This function builds a layered plotly object. By default this produces an SVG output, but this can be slow with 1000s of points. For large number of points we recommend switching to webGL by piping to toWebGL() as shown in the examples.

Value

Returns a plotly plot featuring variables on a tri-axis radial graph

References

Lewis, Myles J., et al. (2019). Molecular portraits of early rheumatoid arthritis identify clinical and treatment response phenotypes. Cell reports, 28:9

See Also

polar_coords

Examples

data(example_data)
syn_polar <- polar_coords(outcome = syn_example_meta$Pathotype,
                          data = t(syn_example_rld))

radial_plotly(polar = syn_polar, label_rows = c("COBL"))

## Faster webGL version for large numbers of points
library(plotly)
radial_plotly(polar = syn_polar, label_rows = c("COBL")) %>%
  toWebGL()

Plots grid objects for inspection using plotly

Description

This function creates an interactive grids in polar and cylindrical coordinates

Usage

show_grid(grid, plot_height = 700, axis_angle = 0, z_axis_title_offset = 1.2)

Arguments

Value

Returns a list containing a polar and cylindrical coordinate system.

References

Lewis, Myles J., et al. (2019). Molecular portraits of early rheumatoid arthritis identify clinical and treatment response phenotypes. Cell reports, 28:9

Examples

data(example_data)
syn_polar <- polar_coords(outcome = syn_example_meta$Pathotype,
                          data = t(syn_example_rld))

grid <- polar_grid(r_vector=syn_polar@df[[1]]$r,
        z_vector=syn_polar@df[[1]]$z,
        r_axis_ticks = NULL,
        z_axis_ticks = NULL)
p <- show_grid(grid)
p$polar
p$cyl

Extract a subset population

Description

Subsets data according to the significance groups.

Usage

significance_subset(polar, significance = NULL, output = "pvals")

Arguments

Value

Returns an object (type defined by output) with rows susbet to those which satisfy the significance condition.

References

Lewis, Myles J., et al. (2019). Molecular portraits of early rheumatoid arthritis identify clinical and treatment response phenotypes. Cell reports, 28:9

Examples

data(example_data)
syn_polar <- polar_coords(outcome = syn_example_meta$Pathotype,
                          data = t(syn_example_rld))

subset <- significance_subset(syn_polar, "L+", "df")

PEAC synovial sample data

Description

A dataset containing sample data for 81 synovial biopsies from the PEAC cohort

Usage

syn_example_meta

Format

A data frame with 81 rows and 1 variables:

Pathotype

The synovial biopsy histological pathotype

Source

https://pubmed.ncbi.nlm.nih.gov/31461658/

PEAC synovial gene expression data

Description

A dataset containing the gene expression data for 81 synovial biopsies from the PEAC cohort

Usage

syn_example_rld

Format

A data frame with 500 rows representing the most significant genes/probes and 81 columns representing samples.

Source

https://pubmed.ncbi.nlm.nih.gov/31461658/

An S4 class to define the polar coordinates.

Description

An S4 class to define the polar coordinates.

Slots

df

List of coordinate data frames for scaled and unscaled expression

outcome

Outcome vector

data

Expression data

pvals

Matrix or dataframe with p-values

padj

Matrix adjusted p-values

pcutoff

Cut-off for p-value significance

scheme

Vector for colour scheme

labs

Character vector for labelling groups

Three-Dimensional Volcano Plot

Description

Plots the three-way comparisons of variables such as gene expression data in 3D space using plotly. x, y position represents polar position on 3 axes representing the amount each variable or gene tends to each of the 3 categories. The z axis represents -log10 P value for the one-way test comparing each variable across the 3 groups.

Usage

volcano3D(
  polar,
  type = 1,
  label_rows = c(),
  label_size = 14,
  arrow_length = 100,
  colour_code_labels = FALSE,
  label_colour = "black",
  grid_colour = "grey80",
  grid_width = 2,
  grid_options = NULL,
  axis_colour = "black",
  axis_width = 2,
  marker_size = 3,
  marker_outline_width = 0,
  marker_outline_colour = "white",
  z_axis_title_offset = 1.2,
  z_axis_title_size = 12,
  z_axis_angle = 0.5,
  radial_axis_title_size = 14,
  radial_axis_title_offset = 1.2,
  xy_aspectratio = 1,
  z_aspectratio = 0.8,
  camera_eye = list(x = 0.9, y = 0.9, z = 0.9),
  ...
)

Arguments

Value

Returns a cylindrical 3D plotly plot featuring variables on a tri-axis radial graph with the -log10(multi-group test p-value) on the z-axis

References

Lewis, Myles J., et al. (2019). Molecular portraits of early rheumatoid arthritis identify clinical and treatment response phenotypes. Cell reports, 28:9

See Also

polar_coords

Examples

data(example_data)
syn_polar <- polar_coords(outcome = syn_example_meta$Pathotype,
                          data = t(syn_example_rld))
volcano3D(syn_polar)

Convert RNA-Seq count data to a volcano3d object using 'limma voom'

Description

This function is used instead of polar_coords if you have raw RNA-Seq count data. The function takes a design formula, metadata and raw RNA-Seq count data and uses 'limma voom' to analyse the data. The results are converted to a 'volc3d' object ready for plotting a 3d volcano plot or polar plot.

Usage

voom_polar(
  formula,
  metadata,
  counts,
  pcutoff = 0.05,
  padj.method = "BH",
  filter_pairwise = TRUE,
  ...
)

Arguments

Details

Statistical results for the group and pairwise comparisons are calculated using the 'limma voom' pipeline and the results passed to polar_coords to generate a 'volc3d' object ready for plotting a 3d volcano plot or polar plot.

Value

Calls polar_coords to return an S4 'volc3d' object

See Also

polar_coords, deseq_polar, voom in the limma package

Examples

if (requireNamespace("limma", quietly = TRUE) & 
    requireNamespace("edgeR", quietly = TRUE)) {
  library(limma)
  library(edgeR)
  
  counts <- matrix(rnbinom(n=1500, mu=100, size=1/0.5), ncol=15)
  cond <- factor(rep(1:3, each=5), labels = c('A', 'B', 'C'))
  cond <- data.frame(cond)
  
  polar <- voom_polar(~0 + cond, cond, counts)
  
  volcano3D(polar)
  radial_ggplot(polar)
}