Package 'VplotR'

Title: Set of tools to make V-plots and compute footprint profiles
Description: The pattern of digestion and protection from DNA nucleases such as DNAse I, micrococcal nuclease, and Tn5 transposase can be used to infer the location of associated proteins. This package contains useful functions to analyze patterns of paired-end sequencing fragment density. VplotR facilitates the generation of V-plots and footprint profiles over single or aggregated genomic loci of interest.
Authors: Jacques Serizay [aut, cre]
Maintainer: Jacques Serizay <[email protected]>
License: GPL (>= 3)
Version: 1.15.0
Built: 2024-09-28 04:45:14 UTC
Source: https://github.com/bioc/VplotR

Help Index

ABF1_sacCer3

Description

Genomic loci with a REB1 binding motifs according to http://jaspar.genereg.net/api/v1/matrix/MA0265.1.jaspar. PWM and scanning done with TFBSTools.

Usage

data(ABF1_sacCer3)

Format

An object of class "GRanges".

References

Rossi, Lai & Pugh 2018 Genome Research

Examples

data(ABF1_sacCer3)
ABF1_sacCer3

A function to re-align a GRanges object to TSSs

Description

This function re-aligns ranges (typically regulatory elements) to a set of coordinates, either the TSS column or the TSS.fwd and TSS.rev columns. If none are found, the function assumes the ranges are promoters and that the end or the ranges are the TSSs.

Usage

alignToTSS(granges, upstream = 0, downstream = 1)

Arguments

granges

A stranded GRanges object with a TSS column or TSS.rev and TSS.fwd columns
upstream

How many bases upstream of the TSS should the GRanges object by extended by? [Default: 0]
downstream

How many bases downstream of the TSS should the GRanges object by extended by? [Default: 1]

Value

GRanges aligned to the TSS column or to TSS.rev and TSS.fwd columns, and extended by upstream/downstream bp.

Examples

data(ce11_proms)
ce11_proms
alignToTSS(ce11_proms)

ATAC_ce11_Serizay2020

Description

A sample of ATAC-seq fragments from individual worm tissues (Serizay et al. 2020, "Tissue-specific profiling reveals distinctive regulatory architectures for ubiquitous, germline and somatic genes", BiorXiv)

Usage

data(ATAC_ce11_Serizay2020)

Format

An object of class "list".

Examples

data(ATAC_ce11_Serizay2020)
ATAC_ce11_Serizay2020

bam_test

Description

A .bam file sample

Usage

data(bam_test)

Format

An object of class "GRanges".

Examples

data(bam_test)
bam_test

ce11_all_REs

Description

Regulatory elements annotated in C. elegans (ce11) according to Serizay et al. 2020, "Tissue-specific profiling reveals distinctive regulatory architectures for ubiquitous, germline and somatic genes", BiorXiv.

Usage

data(ce11_all_REs)

Format

GRanges

Source

BiorXiv

References

Serizay et al. 2020, "Tissue-specific profiling reveals distinctive regulatory architectures for ubiquitous, germline and somatic genes", BiorXiv. (DOI)

Examples

data(ce11_all_REs)
table(ce11_all_REs$regulatory_class)
table(ce11_all_REs$which.tissues)

ce11_proms

Description

Promoters annotated in C. elegans (ce11) according to Serizay et al. 2020, "Tissue-specific profiling reveals distinctive regulatory architectures for ubiquitous, germline and somatic genes", BiorXiv.

Usage

data(ce11_proms)

Format

An object of class "GRanges".

Source

BiorXiv

References

Serizay et al. 2020, "Tissue-specific profiling reveals distinctive regulatory architectures for ubiquitous, germline and somatic genes", BiorXiv. (DOI)

Examples

data(ce11_proms)
table(ce11_proms$which.tissues)

A function to compute Vplot matrix

Description

This function computes the underlying matrix shown as a heatmap in Vplots. For each pair of coordinates (x: distance from fragment midpoint to center of GRanges of interest; y: fragment size), the function computes how many fragments there are.

Usage

computeVmat(
  bam_granges,
  granges,
  cores = 1,
  xlims = c(-250, 250),
  ylims = c(50, 300)
)

Arguments

bam_granges

GRanges, paired-end fragments
granges

GRanges, regions to map the fragments onto
cores

Integer, nb of threads to parallelize fragments subsetting
xlims

The x limits of the computed Vmat
ylims

The y limits of the computed Vmat

Value

A table object

Examples

data(bam_test)
data(ce11_all_REs)
Vmat <- computeVmat(bam_test, ce11_all_REs)
dim(Vmat)
Vmat[seq(1,5), seq(1,10)]

CTCF_hg38

Description

high-score CTCF binding motifs, obtained from JASPAR

Usage

data(CTCF_hg38)

Format

An object of class "GRanges".

Examples

data(CTCF_hg38)
CTCF_hg38

A function to duplicate bi-directional GRanges

Description

This function splits bi-directional ranges into + and - stranded ranges. It duplicates the ranges which are '*'.

Usage

deconvolveBidirectionalPromoters(granges)

Arguments

granges

A stranded GRanges object

Value

GRanges with only '+' and '-' strands. GRanges with '*' strand have been duplicated and split into forward and reverse strands.

Examples

data(ce11_all_REs)
library(GenomicRanges)
proms <- ce11_all_REs[grepl('prom', ce11_all_REs$regulatory_class)]
proms
table(strand(proms))
proms <- deconvolveBidirectionalPromoters(proms)
proms
table(strand(proms))

A function to compute sizes distribution for paired-end fragments

Description

This function takes fragments and compute the distribution of their sizes over a set or multiple sets of GRanges.

Usage

getFragmentsDistribution(
  fragments,
  granges_list = NULL,
  extend_granges = c(-500, 500),
  limits = c(0, 600),
  roll = 3,
  cores = 1
)

Arguments

fragments

GRanges object containing paired-end fragments. See importPEBamFiles for more details on how to create such object.
granges_list

GRanges, can be a list of different sets of GRanges.
extend_granges

numeric vector of length 2, how the GRanges should be extended.
limits

numeric vector of length 2, only consider fragments within this window of sizes.
roll

Integer, apply a moving average of this size
cores

Integer, number of threads used to compute fragment size distribution

Value

A list of tbl, one for each .bam file.

Examples

data(bam_test)
data(ce11_proms)
df <- getFragmentsDistribution(
    bam_test, 
    ce11_proms,
    extend_granges = c(-500, 500)
)
head(df)
which.max(df$y)

A function to import paired end bam files as GRanges

Description

This function takes bam file paths and read them into GRanges objects. Note: Can be quite lengthy for .bam files with 5+ millions fragments.

Usage

importPEBamFiles(
  files,
  genome = NULL,
  where = NULL,
  max_insert_size = 1000,
  shift_ATAC_fragments = FALSE,
  cores = 10,
  verbose = TRUE
)

Arguments

files

character vector, each element of the vector is the path of an individual .bam file.
genome

character, genome ID (e.g. "sacCer3", "ce11", "dm6", "mm10" or "hg38").
where

GRanges, only import the fragments mapping to the input GRanges (can fasten the import process a lot).
max_insert_size

Integer, filter out fragments larger than this size.
shift_ATAC_fragments

Boolean, if the fragments come from ATAC-seq, one might want to shift the extremities by +5 / -4 bp.
cores

Integer, number of cores to use when indexing bam files
verbose

Boolean

Value

A GRanges object containing fragments from the input .bam file.

Examples

bamfile <- system.file("extdata", "ex1.bam", package = "Rsamtools")
fragments <- importPEBamFiles(
    bamfile, 
    shift_ATAC_fragments = TRUE
)
fragments

MNase_sacCer3_Henikoff2011

Description

A sample of MNase-seq fragments from yeast (Henikoff et al. 2011, "Epigenome characterization at single base-pair resolution", PNAS)

Usage

data(MNase_sacCer3_Henikoff2011)

Format

An object of class "GRanges".

Examples

data(MNase_sacCer3_Henikoff2011)
MNase_sacCer3_Henikoff2011

MNase_sacCer3_Henikoff2011_subset

Description

A sample of fragments from multiple MNase-seq experiments performed in yeast (Henikoff et al. 2011, "Epigenome characterization at single base-pair resolution", PNAS), mapping over chrXV:186,400-187,400.

Usage

data(MNase_sacCer3_Henikoff2011_subset)

Format

An object of class "GRanges".

Examples

data(MNase_sacCer3_Henikoff2011_subset)
MNase_sacCer3_Henikoff2011_subset

A function to normalized a Vmat

Description

This function normalizes a Vmat. Several different approaches have been implemented to normalize the Vmats.

Usage

normalizeVmat(
  Vmat,
  bam_granges,
  granges,
  normFun = c("zscore"),
  s = 0.99,
  roll = 1,
  verbose = TRUE
)

Arguments

Vmat

A Vmat, usually output of computeVmat
bam_granges

GRanges, the paired-end fragments
granges

GRanges, the regions to map the fragments onto
normFun

character. A Vmat should be scaled either by:

  • 'libdepth+nloci', e.g. the library depth and the number of loci used to compute the Vmat;

  • zscore, if relative patterns of fragment density are more important than density per se;

  • Alternatively, the Vmat can be scaled to a chosen quantile ('quantile') or to the max Vmat value ('max').

s

A float indicating which quantile to use if 'quantile' normalization is chosen
roll

integer, to use as the window to smooth the Vmat rows by rolling mean.
verbose

Boolean

Value

A normalized Vmat object

Examples

data(bam_test)
data(ce11_all_REs)
Vmat <- computeVmat(bam_test, ce11_all_REs)
Vmat <- normalizeVmat(
    Vmat, 
    bam_test, 
    ce11_all_REs,
    normFun = c('libdepth+nloci')
)

A function to compute nucleosome enrichment over a set of GRanges

Description

A function to compute nucleosome enrichment over a set of GRanges

Usage

nucleosomeEnrichment(x, ...)

Arguments

x

a GRanges or Vmat
...

additional parameters

Value

list

Examples

data(bam_test)
data(ce11_proms)
n <- nucleosomeEnrichment(bam_test, ce11_proms)
n$fisher_test
n$plot

A function to compute nucleosome enrichment over a set of GRanges

Description

A function to compute nucleosome enrichment over a set of GRanges

Usage

## S3 method for class 'GRanges'
nucleosomeEnrichment(x, granges, plus1_nuc_only = FALSE, verbose = TRUE, ...)

Arguments

x

GRanges, paired-end fragments
granges

GRanges, loci to map the fragments onto
plus1_nuc_only

Boolean, should compute nucleosome enrichment only for +1 nucleosome?
verbose

Boolean
...

additional parameters

Value

list

Examples

data(bam_test)
data(ce11_proms)
n <- nucleosomeEnrichment(bam_test, ce11_proms)
n$fisher_test
n$plot

A function to compute nucleosome enrichment over a Vmat

Description

A function to compute nucleosome enrichment over a Vmat

Usage

## S3 method for class 'Vmat'
nucleosomeEnrichment(x, background, plus1_nuc_only = FALSE, ...)

Arguments

x

a computed Vmat. Should be un-normalized.
background

a background Vmat. Should be un-normalized.
plus1_nuc_only

Boolean, should compute nucleosome enrichment only for +1 nucleosome?
...

additional parameters

Value

list

Examples

data(bam_test)
data(ce11_proms)
V <- plotVmat(
    bam_test,
    ce11_proms,
    normFun = '',
    return_Vmat = TRUE
)
V_bg <- plotVmat(
    bam_test,
    sampleGRanges(ce11_proms),
    normFun = '',
    return_Vmat = TRUE
)
n <- nucleosomeEnrichment(V, V_bg)
n$fisher_test
n$plot

A function to plot footprint of paired-end data at given loci

Description

This function takes paired-end fragments, extract the "cuts" (i.e. extremities) and plot the footprint profile over a set of GRanges.

Usage

plotFootprint(
  frags,
  targets,
  split_strand = FALSE,
  plot_central = TRUE,
  xlim = c(-75, 75),
  bin = 1,
  verbose = 1
)

Arguments

frags

GRanges, the paired-end fragments
targets

GRanges, the loci to map the fragments onto
split_strand

Boolean, should the + and - strand be splitted?
plot_central

plot grey rectangle over the loci
xlim

numeric vector of length 2, the x limits of the computed Vmat
bin

Integer, bin used to smooth the gootprint profile
verbose

Integer

Value

A footprint ggplot

Examples

data(bam_test)
data(ce11_proms)
plotFootprint(bam_test, ce11_proms)

A function to generate a Vplot along chromosome coordinates

Description

The paired-end fragments overlapping a locus of interest (e.g., binding sites, provided in the 'loci' argument) are shown in red while the remaining fragments mapping to the genomic window are displayed in black. Marginal curves are also plotted on the side of the distribution plot. They highlight the smoothed distribution of the position of paired-end fragment midpoints (top) or of the paired-end fragment length (right)

Usage

plotProfile(
  fragments,
  window = loc,
  loci = NULL,
  annots = NULL,
  min = 50,
  max = 200,
  alpha = 0.5,
  size = 1,
  with_densities = TRUE,
  verbose = TRUE
)

Arguments

fragments

GRanges
window

character, chromosome location
loci

GRanges, optional genomic locus. Fragments overlapping this locus will be in red.
annots

GRanges, optional gene annotations
min

integer, minimum fragment size
max

integer, maximum fragment size
alpha

float, transparency value
size

float, dot size
with_densities

Boolean, should the densities be plotted?
verbose

Boolean

Value

A ggplot

Examples

data(bam_test)
data(ce11_proms)
V <- plotProfile(
    bam_test,
    'chrI:10000-12000',
    loci = ce11_proms,
    min = 80, 
    max = 200
)

A function to generate a Vplot

Description

See individual methods for further detail

Usage

plotVmat(x, ...)

Arguments

x

GRanges or list or Vmat
...

additional parameters

Value

A Vmat ggplot

Examples

data(bam_test)
data(ce11_proms)
V <- plotVmat(
    bam_test,
    ce11_proms,
    normFun = 'libdepth+nloci'
)

A function to plot a computed Vmat

Description

The default plotVmat method generates a ggplot representing a heatmap of fragment density.

Usage

## Default S3 method:
plotVmat(
  x,
  hm = 90,
  colors = COLORSCALE_VMAT,
  breaks = NULL,
  xlim = c(-250, 250),
  ylim = c(50, 300),
  main = "",
  xlab = "Distance from center of elements",
  ylab = "Fragment length",
  key = "Score",
  ...
)

Arguments

x

A computed Vmat (ideally, should be normalized)
hm

Integer, should be between 0 and 100. Used to automatically scale the range of colors (best to keep between 90 and 100)
colors

a vector of colors
breaks

a vector of breaks. length(breaks) == length(colors) + 1
xlim

vector of two integers, x limits
ylim

vector of two integers, y limits
main

character, title of the plot
xlab

character, x-axis label
ylab

character, y-axis label
key

character, legend label
...

additional parameters

Value

A Vmat ggplot

Examples

data(bam_test)
data(ce11_proms)
V <- plotVmat(
    bam_test,
    ce11_proms,
    normFun = 'libdepth+nloci', 
    return_Vmat = TRUE
)
plotVmat(V)

A function to compute (and plot) a Vmat

Description

The plotVmat.GRanges() method computes and normalizes a Vmat before passing it to plotVmat.Vmat() method.

Usage

## S3 method for class 'GRanges'
plotVmat(
  x,
  granges,
  xlims = c(-250, 250),
  ylims = c(50, 300),
  normFun = "",
  s = 0.95,
  roll = 3,
  cores = 1,
  return_Vmat = FALSE,
  verbose = 1,
  ...
)

Arguments

x

GRanges, paired-end fragments
granges

GRanges, loci to map the fragments onto
xlims

x limits of the computed Vmat
ylims

y limits of the computed Vmat
normFun

character. A Vmat should be scaled either by:

  • 'libdepth+nloci', e.g. the library depth and the number of loci used to compute the Vmat;

  • zscore, if relative patterns of fragment density are more important than density per se;

  • Alternatively, the Vmat can be scaled to a chosen quantile ('quantile') or to the max Vmat value ('max').

s

A float indicating which quantile to use if 'quantile' normalization is chosen
roll

integer, to use as the window to smooth the Vmat rows by rolling mean.
cores

Integer, number of threads to parallelize fragments subsetting
return_Vmat

Boolean, should the function return the computed Vmat rather than the plot?
verbose

Boolean
...

additional parameters

Value

A Vmat ggplot

Examples

data(bam_test)
data(ce11_proms)
V <- plotVmat(
    bam_test,
    ce11_proms,
    normFun = 'libdepth+nloci', 
    roll = 5
)

A function to compute (and plot) several Vmats.

Description

The plotVmat.GRanges() method computes and normalizes multiple Vmats before passing them to plotVmat.VmatList() method.

Usage

## S3 method for class 'list'
plotVmat(
  x,
  cores = 1,
  cores_subsetting = 1,
  nrow = NULL,
  ncol = NULL,
  xlims = c(-250, 250),
  ylims = c(50, 300),
  normFun = "libdepth+nloci",
  s = 0.95,
  roll = 3,
  return_Vmat = FALSE,
  verbose = 1,
  ...
)

Arguments

x

list Each element of the list should be a list containing paired-end fragments and GRanges of interest.
cores

Integer, number of cores to parallelize the plots
cores_subsetting

Integer, number of threads to parallelize fragments subsetting
nrow

Integer, how many rows in facet?
ncol

Integer, how many cols in facet?
xlims

x limits of the computed Vmat
ylims

y limits of the computed Vmat
normFun

character. A Vmat should be scaled either by:

  • 'libdepth+nloci', e.g. the library depth and the number of loci used to compute the Vmat;

  • zscore, if relative patterns of fragment density are more important than density per se;

  • Alternatively, the Vmat can be scaled to a chosen quantile ('quantile') or to the max Vmat value ('max').

s

A float indicating which quantile to use if 'quantile' normalization is chosen
roll

integer, to use as the window to smooth the Vmat rows by rolling mean.
return_Vmat

Boolean, should the function return the computed Vmat rather than the plot?
verbose

Boolean
...

additional parameters

Value

A list of Vmat ggplots

Examples

data(bam_test)
data(ce11_proms)
list_params <- list(
    'germline' = list(
        bam_test,
        ce11_proms[ce11_proms$which.tissues == 'Germline']
    ),
    'muscle' = list(
        bam_test,
        ce11_proms[ce11_proms$which.tissues == 'Muscle']
    )
)
V <- plotVmat(
    list_params,
    normFun = 'libdepth+nloci', 
    roll = 5
)

A function to plot a computed Vmat

Description

The plotVmat.Vmat() method forwards the Vmat to plotVmat.default().

Usage

## S3 method for class 'Vmat'
plotVmat(x, ...)

Arguments

x

A computed Vmat (ideally, should be normalized)
...

additional parameters

Value

A Vmat ggplot

Examples

data(bam_test)
data(ce11_proms)
V <- plotVmat(
    bam_test,
    ce11_proms,
    normFun = 'libdepth+nloci', 
    return_Vmat = TRUE
)
plotVmat(V)

A function to plot a computed VmatList

Description

The plotVmat.VmatList() method forwards the Vmat to plotVmat.default().

Usage

## S3 method for class 'VmatList'
plotVmat(x, nrow = NULL, ncol = NULL, dir = "v", ...)

Arguments

x

A VmatList (output of plotVmat.list())
nrow

Integer, how many rows in facet?
ncol

Integer, how many cols in facet?
dir

str, direction of facets?
...

additional parameters

Value

A Vmat ggplot

Examples

data(bam_test)
data(ce11_proms)
list_params <- list(
    'germline' = list(
        bam_test,
        ce11_proms[ce11_proms$which.tissues == 'Germline']
    ),
    'muscle' = list(
        bam_test,
        ce11_proms[ce11_proms$which.tissues == 'Muscle']
    )
)
V <- plotVmat(
    list_params,
    normFun = 'libdepth+nloci', 
    roll = 5
)

REB1_sacCer3

Description

Genomic loci with a REB1 binding motifs according to http://jaspar.genereg.net/api/v1/matrix/MA0363.1.jaspar. PWM and scanning done with TFBSTools.

Usage

data(REB1_sacCer3)

Format

An object of class "GRanges".

References

Rossi, Lai & Pugh 2018 Genome Research

Examples

data(REB1_sacCer3)
REB1_sacCer3

A function to sample GRanges from GRanges

Description

This function takes a given GRanges and returns another GRanges object. The new GRanges has the same number of ranges and the same chromosome, width and strand distributions than the original GRanges.

Usage

sampleGRanges(
  x,
  n = NULL,
  width = NULL,
  exclude = FALSE,
  avoid_overlap = FALSE
)

Arguments

x

GRanges object
n

Integer, number of sampled GRanges
width

Integer, width of sampled GRanges
exclude

Boolean, should the original GRanges be excluded?
avoid_overlap

Boolean, should the sampled GRanges not be overlapping?

Value

A GRanges object of length n

Examples

data(ce11_proms)
sampleGRanges(ce11_proms, 100)

A function to sample GRanges within GRanges

Description

This function takes a given GRanges and returns another GRanges object. The new GRanges has the same number of ranges and the same chromosome, width and strand distributions than the original GRanges.

Usage

## S3 method for class 'GRanges'
sampleGRanges(
  x,
  n = NULL,
  width = NULL,
  exclude = FALSE,
  avoid_overlap = FALSE
)

Arguments

x

GRanges object
n

Integer, number of sampled GRanges
width

Integer, width of sampled GRanges
exclude

Boolean, should the original GRanges be excluded?
avoid_overlap

Boolean, should the sampled GRanges not be overlapping?

Value

A GRanges object of length n

Examples

data(ce11_proms)
sampleGRanges(ce11_proms, 100)

A function to shift GRanges fragments by 5/-4. This is useful when dealing with fragments coming from ATAC-seq.

Description

A function to shift GRanges fragments by 5/-4. This is useful when dealing with fragments coming from ATAC-seq.

Usage

shiftATACGranges(g, pos_shift = 4, neg_shift = 5)

Arguments

g

GRanges of ATAC-seq fragments
pos_shift

Integer. How many bases should fragments on direct strand be shifted by?
neg_shift

Integer. How many bases should fragments on negative strand be shifted by?

Value

A GRanges object containing fragments from the input .bam file.

Examples

data(bam_test)
shiftATACGranges(bam_test)

A function to shuffle a Vmat

Description

This function works on a Vmat (the output of computeVmat()). It shuffles the matrix to randomize the fragment densities.

Usage

shuffleVmat(Vmat)

Arguments

Vmat

A Vmat, usually output of computeVmat

Value

A shuffled Vmat object

Examples

data(bam_test)
data(ce11_all_REs)
Vmat <- computeVmat(bam_test, ce11_all_REs)
Vmat <- shuffleVmat(Vmat)

Personal ggplot2 theming function, adapted from roboto-condensed at https://github.com/hrbrmstr/hrbrthemes/

Description

Personal ggplot2 theming function, adapted from roboto-condensed at https://github.com/hrbrmstr/hrbrthemes/

Usage

theme_ggplot2(
  grid = TRUE,
  border = TRUE,
  base_family = "",
  base_size = 8,
  plot_title_family = base_family,
  plot_title_size = 12,
  plot_title_face = "plain",
  plot_title_margin = 5,
  subtitle_size = 11,
  subtitle_face = "plain",
  subtitle_margin = 5,
  strip_text_family = base_family,
  strip_text_size = 10,
  strip_text_face = "bold",
  caption_size = 9,
  caption_face = "plain",
  caption_margin = 3,
  axis_text_size = base_size,
  axis_title_family = base_family,
  axis_title_size = 9,
  axis_title_face = "plain",
  axis_title_just = "rt",
  panel_spacing = grid::unit(2, "lines"),
  grid_col = "#cccccc",
  plot_margin = margin(12, 12, 12, 12),
  axis_col = "#cccccc",
  axis = FALSE,
  ticks = FALSE
)

Arguments

grid

panel grid ('TRUE', 'FALSE', or a combination of 'X', 'x', 'Y', 'y')
border

border if 'TRUE' add border
base_family, base_size

base font family and size
plot_title_family, plot_title_face

plot title family, face
plot_title_size, plot_title_margin

plot title size and margin
subtitle_face, subtitle_size

plot subtitle family, face and size
subtitle_margin

plot subtitle margin bottom (single numeric value)
strip_text_family, strip_text_face, strip_text_size

facet label font family, face and size
caption_face, caption_size, caption_margin

plot caption family, face, size and margin
axis_text_size

font size of axis text
axis_title_family, axis_title_face, axis_title_size

axis title font family, face and size
axis_title_just

axis title font justificationk one of '[blmcrt]'
panel_spacing

panel spacing (use 'unit()')
grid_col

grid color
plot_margin

plot margin (specify with [ggplot2::margin])
axis_col

axis color
axis

add x or y axes? 'TRUE', 'FALSE', "'xy'"
ticks

ticks if 'TRUE' add ticks

Value

theme A ggplot theme

Examples

library(ggplot2)

ggplot(mtcars, aes(mpg, wt)) +
  geom_point() +
  labs(x="Fuel effiency (mpg)", y="Weight (tons)",
       title="Seminal ggplot2 scatterplot example") +
  theme_ggplot2()