EnhancedVolcano will attempt to fit as many variable names in the plot window as possible, thus avoiding 'clogging' up the plot with labels that could not otherwise have been read.Volcano plots represent a useful way to visualise the results of differential expression analyses. Here, we present a highly-configurable function that produces publication-ready volcano plots [@EnhancedVolcano]. EnhancedVolcano will attempt to fit as many variable names in the plot window as possible, thus avoiding 'clogging' up the plot with labels that could not otherwise have been read.
EnhancedVolcano(
toptable,
lab,
x,
y,
selectLab = NULL,
xlim = c(min(toptable[[x]], na.rm = TRUE) - 1.5, max(toptable[[x]], na.rm = TRUE) +
1.5),
ylim = c(0, max(-log10(toptable[[y]]), na.rm = TRUE) + 5),
xlab = bquote(~Log[2] ~ "fold change"),
ylab = bquote(~-Log[10] ~ italic(P)),
axisLabSize = 18,
title = "Volcano plot",
subtitle = bquote(italic(EnhancedVolcano)),
caption = paste0("total = ", nrow(toptable), " variables"),
titleLabSize = 18,
subtitleLabSize = 14,
captionLabSize = 14,
pCutoff = 1e-05,
FCcutoff = 1,
cutoffLineType = "longdash",
cutoffLineCol = "black",
cutoffLineWidth = 0.4,
pointSize = 2,
labSize = 5,
labCol = "black",
labFace = "plain",
boxedLabels = FALSE,
parseLabels = FALSE,
shape = 19,
shapeCustom = NULL,
col = c("grey30", "forestgreen", "royalblue", "red2"),
colCustom = NULL,
colAlpha = 1/2,
colGradient = NULL,
colGradientBreaks = c(pCutoff, 1),
colGradientLabels = c("0", "1.0"),
colGradientLimits = c(0, 1),
legendLabels = c("NS", expression(Log[2] ~ FC), "p-value", expression(p - value ~ and
~ log[2] ~ FC)),
legendPosition = "top",
legendLabSize = 14,
legendIconSize = 5,
legendDropLevels = TRUE,
encircle = NULL,
encircleCol = "black",
encircleFill = "pink",
encircleAlpha = 3/4,
encircleSize = 2.5,
shade = NULL,
shadeFill = "grey",
shadeAlpha = 1/2,
shadeSize = 0.01,
shadeBins = 2,
drawConnectors = FALSE,
widthConnectors = 0.5,
typeConnectors = "closed",
endsConnectors = "first",
lengthConnectors = unit(0.01, "npc"),
colConnectors = "grey10",
maxoverlapsConnectors = 15,
directionConnectors = "both",
arrowheads = TRUE,
hline = NULL,
hlineType = "longdash",
hlineCol = "black",
hlineWidth = 0.4,
vline = NULL,
vlineType = "longdash",
vlineCol = "black",
vlineWidth = 0.4,
gridlines.major = TRUE,
gridlines.minor = TRUE,
border = "partial",
borderWidth = 0.8,
borderColour = "black",
raster = FALSE
)A data-frame of test statistics (if not, a data frame, an attempt will be made to convert it to one). Requires at least the following: column for variable names (can be rownames); a column for log2 fold changes; a column for nominal or adjusted p-value.
A column name in toptable containing variable names.
Can be rownames(toptable).
A column name in toptable containing log2 fold changes.
A column name in toptable containing nominal or adjusted
p-values.
A vector containing a subset of lab.
Limits of the x-axis.
Limits of the y-axis.
Label for x-axis.
Label for y-axis.
Size of x- and y-axis labels.
Plot title.
Plot subtitle.
Plot caption.
Size of plot title.
Size of plot subtitle.
Size of plot caption.
Cut-off for statistical significance. A horizontal line will be drawn at -log10(pCutoff).
Cut-off for absolute log2 fold-change. Vertical lines will be drawn at the negative and positive values of log2FCcutoff.
Line type for FCcutoff and pCutoff
('blank', 'solid', 'dashed', 'dotted', 'dotdash', 'longdash', 'twodash').
Line colour for FCcutoff and pCutoff.
Line width for FCcutoff and pCutoff.
Size of plotted points for each variable. Can be a single value or a vector of sizes.
Size of labels for each variable.
Colour of labels for each variable.
Font face of labels for each variable.
Logical, indicating whether or not to draw labels in boxes.
Logical, indicating whether or not to parse expressions in labels
Shape of the plotted points. Either a single value for
all points, or 4 values corresponding to the default 4 legend labels
specified by legendLabels.
Named vector / key-value pairs that will over-ride the
default shape scheme. The order must match that of toptable.
Names / keys relate to groups / categories; values relate to shape encodings.
Colour shading for plotted points, corresponding to
the default 4 legend labels specified by legendLabels.
Named vector / key-value pairs that will over-ride the
default colour scheme. The order must match that of toptable.
Names / keys relate to groups / categories; values relate to colour.
Alpha for purposes of controlling colour transparency of variable points.
If activated, over-rides the default discrete colour scheme
and replaces it with a continous scheme that shades based on nominal or
adjusted p-value specified by y. For example, c('red2', 'blue2').
Break-points for the two colours specified by colGradient.
Labels for the break-points specified by colGradientBreaks.
Limits of the colour scheme specified by colGradient, i.e., max and min possible p-values.
Plot legend text labels.
Position of legend ('top', 'bottom', 'left', 'right').
Size of plot legend text.
Size of plot legend icons / symbols.
Logical, drop unused factor levels from legend.
A vector of variable names to encircle.
Colour of the encircled line.
Colour fill of the encircled region.
Alpha for purposes of controlling colour transparency of encircled region.
Line width of the encircled line.
A vector of variable names to shade.
Colour of shaded regions.
Alpha for purposes of controlling colour transparency of shaded region.
Size of the shade contour lines.
Number of bins for the density of the shade.
Logical, indicating whether or not to connect plot labels to their corresponding points by line connectors.
Line width of connectors.
Have the arrow head open ('open') or filled ('closed')?
Which end of connectors to draw arrow head? ('last', 'first', 'both').
Length of the connectors.
Line colour of connectors.
Equivalent of max.overlaps in ggrepel. Set to 'Inf' to always display all labels when drawConnectors = TRUE.
direction in which to draw connectors. 'both', 'x', or 'y'.
Logical, indicating whether or not to draw arrow heads or or just have straight lines.
Draw one or more horizontal lines passing through this/these values on y-axis. For single values, only a single numerical value is necessary. For multiple lines, pass these as a vector, e.g., c(60,90).
Line type for hline ('blank', 'solid', 'dashed', 'dotted',
'dotdash', 'longdash', 'twodash').
Colour of hline.
Width of hline.
Draw one or more vertical lines passing through this/these values on x-axis. For single values, only a single numerical value is necessary. For multiple lines, pass these as a vector, e.g., c(60,90).
Line type for vline ('blank', 'solid', 'dashed', 'dotted',
'dotdash', 'longdash', 'twodash').
Colour of vline.
Width of vline.
Logical, indicating whether or not to draw major gridlines.
Logical, indicating whether or not to draw minor gridlines.
Add a border for just the x and y axes ('partial') or the entire plot grid ('full')?
Width of the border on the x and y axes.
Colour of the border on the x and y axes.
Logical, indicating whether to rasterize the geom_point layer.
A ggplot2 object.
Volcano plots represent a useful way to visualise the results of differential expression analyses. Here, we present a highly-configurable function that produces publication-ready volcano plots [@EnhancedVolcano]. EnhancedVolcano will attempt to fit as many variable names in the plot window as possible, thus avoiding 'clogging' up the plot with labels that could not otherwise have been read.
# NOT RUN {
library('pasilla')
pasCts <- system.file('extdata', 'pasilla_gene_counts.tsv',
package='pasilla', mustWork=TRUE)
pasAnno <- system.file('extdata', 'pasilla_sample_annotation.csv',
package='pasilla', mustWork=TRUE)
cts <- as.matrix(read.csv(pasCts,sep='\t',row.names='gene_id'))
coldata <- read.csv(pasAnno, row.names=1)
coldata <- coldata[,c('condition','type')]
rownames(coldata) <- sub('fb', '', rownames(coldata))
cts <- cts[, rownames(coldata)]
library('DESeq2')
dds <- DESeqDataSetFromMatrix(countData = cts,
colData = coldata,
design = ~ condition)
featureData <- data.frame(gene=rownames(cts))
mcols(dds) <- DataFrame(mcols(dds), featureData)
dds <- DESeq(dds)
res <- results(dds)
EnhancedVolcano(res,
lab = rownames(res),
x = 'log2FoldChange',
y = 'pvalue',
pCutoff = 10e-4,
FCcutoff = 1.333,
xlim = c(-5.5, 5.5),
ylim = c(0, -log10(10e-12)),
pointSize = 1.5,
labSize = 2.5,
title = 'DESeq2 results',
subtitle = 'Differential expression',
caption = 'FC cutoff, 1.333; p-value cutoff, 10e-4',
legendPosition = "right",
legendLabSize = 14,
col = c('grey30', 'forestgreen', 'royalblue', 'red2'),
colAlpha = 0.9,
drawConnectors = TRUE,
hline = c(10e-8),
widthConnectors = 0.5)
# }
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