Package 'wiggleplotr' reference manual

Title:	Make read coverage plots from BigWig files
Description:	Tools to visualise read coverage from sequencing experiments together with genomic annotations (genes, transcripts, peaks). Introns of long transcripts can be rescaled to a fixed length for better visualisation of exonic read coverage.
Authors:	Kaur Alasoo [aut, cre]
Maintainer:	Kaur Alasoo <[email protected]>
License:	Apache License 2.0
Version:	1.37.0
Built:	2026-05-29 09:51:11 UTC
Source:	https://github.com/bioc/wiggleplotr

Extract read coverage data from the bigWig files

Description

Does not work on Windows, because rtracklayer cannot read BigWig files on Windows.

Usage

extractCoverageData(
  exons,
  cdss = NULL,
  transcript_annotations = NULL,
  track_data,
  rescale_introns = TRUE,
  new_intron_length = 50,
  flanking_length = c(50, 50),
  plot_fraction = 0.1,
  mean_only = TRUE,
  region_coords = NULL
)
extractCoverageData(
  exons,
  cdss = NULL,
  transcript_annotations = NULL,
  track_data,
  rescale_introns = TRUE,
  new_intron_length = 50,
  flanking_length = c(50, 50),
  plot_fraction = 0.1,
  mean_only = TRUE,
  region_coords = NULL
)

Arguments

exons

list of GRanges objects, each object containing exons for one transcript. The list must have names that correspond to transcript_id column in transcript_annotations data.frame.

cdss

list of GRanges objects, each object containing the coding regions (CDS) of a single transcript. The list must have names that correspond to transcript_id column in trancsript_annotations data.frame. If cdss is not specified then exons list will be used for both arguments. (default: NULL).

transcript_annotations

Data frame with at least three columns: transcript_id, gene_name, strand. Used to construct transcript labels. (default: NULL)

track_data

data.frame with the metadata for the bigWig read coverage files. Must contain the following columns:

sample_id - unique id for each sample.
track_id - if multiple samples (bigWig files) have the same track_id they will be overlayed on the same plot, track_id is also used as the facet label on the right.
bigWig - path to the bigWig file.
scaling_factor - normalisation factor for each sample, useful if different samples sequenced to different depth and bigWig files not normalised for that.
colour_group - additional column to group samples into, is used as the colour of the coverage track.

rescale_introns

Specifies if the introns should be scaled to fixed length or not. (default: TRUE)

new_intron_length

length (bp) of introns after scaling. (default: 50)

flanking_length

Lengths of the flanking regions upstream and downstream of the gene. (default: c(50,50))

plot_fraction

Size of the random sub-sample of points used to plot coverage (between 0 and 1). Smaller values make plotting significantly faster. (default: 0.1)

mean_only

Plot only mean coverage within each combination of track_id and colour_group values. Useful for example for plotting mean coverage stratified by genotype (which is specified in the colour_group column) (default: TRUE).

region_coords

Start and end coordinates of the region to plot, overrides flanking_length parameter. The 'both' option tends to give better results for wide regions. (default: area).

Value

List containing all of the necessary data for the plotCoverageData function ()

Examples

require("dplyr")
require("GenomicRanges")
sample_data = dplyr::data_frame(sample_id = c("aipt_A", "aipt_C", "bima_A", "bima_C"), 
    condition = factor(c("Naive", "LPS", "Naive", "LPS"), levels = c("Naive", "LPS")), 
    scaling_factor = 1) %>%
    dplyr::mutate(bigWig = system.file("extdata",  paste0(sample_id, ".str2.bw"), package = "wiggleplotr"))

track_data = dplyr::mutate(sample_data, track_id = condition, colour_group = condition)

selected_transcripts = c("ENST00000438495", "ENST00000392477") #Plot only two transcripts of the gens
## Not run: 
extractCoverageData(ncoa7_exons[selected_transcripts], ncoa7_cdss[selected_transcripts], ncoa7_metadata, track_data)

## End(Not run)

require("dplyr")
require("GenomicRanges")
sample_data = dplyr::data_frame(sample_id = c("aipt_A", "aipt_C", "bima_A", "bima_C"), 
    condition = factor(c("Naive", "LPS", "Naive", "LPS"), levels = c("Naive", "LPS")), 
    scaling_factor = 1) %>%
    dplyr::mutate(bigWig = system.file("extdata",  paste0(sample_id, ".str2.bw"), package = "wiggleplotr"))

track_data = dplyr::mutate(sample_data, track_id = condition, colour_group = condition)

selected_transcripts = c("ENST00000438495", "ENST00000392477") #Plot only two transcripts of the gens
## Not run: 
extractCoverageData(ncoa7_exons[selected_transcripts], ncoa7_cdss[selected_transcripts], ncoa7_metadata, track_data)

## End(Not run)

Returns a three-colour palette suitable for visualising read coverage stratified by genotype

Description

Returns a three-colour palette suitable for visualising read coverage stratified by genotype

Usage

getGenotypePalette(old = FALSE)
getGenotypePalette(old = FALSE)

Arguments

old

Return old colour palette (now deprecated).

Value

Vector of three colours.

Examples

getGenotypePalette()
getGenotypePalette()

Make a Manahattan plot of p-values

Description

The Manhattan plots is compatible with wiggpleplotr read coverage and transcript strucutre plots. Can be appended to those using the cowplot::plot_grid() function.

Usage

makeManhattanPlot(
  pvalues_df,
  region_coords,
  color_R2 = FALSE,
  data_track = TRUE
)
makeManhattanPlot(
  pvalues_df,
  region_coords,
  color_R2 = FALSE,
  data_track = TRUE
)

Arguments

pvalues_df

Data frame of association p-values (required columns: track_id, p_nominal, pos)

region_coords

Start and end coordinates of the region to plot.

color_R2

Color the points according to R2 from the lead variant. Require R2 column in the pvalues_df data frame.

data_track

If TRUE, then remove all information from x-axis. Makes it easy to append to read coverage or transcript strcture plots using cowplot::plot_grid().

Value

gglot2 object

Examples

data = dplyr::data_frame(track_id = "GWAS", pos = sample(c(1:1000), 200), p_nominal = runif(200, min = 0.0000001, 1))
makeManhattanPlot(data, c(1,1000), data_track = FALSE)
data = dplyr::data_frame(track_id = "GWAS", pos = sample(c(1:1000), 200), p_nominal = runif(200, min = 0.0000001, 1))
makeManhattanPlot(data, c(1,1000), data_track = FALSE)

Coding sequences from 9 protein coding transcripts of NCOA7

Description

A dataset containing start and end coordinates of coding sequences (CDS) from nine protein coding transcripts of NCOA7.

Usage

ncoa7_cdss
ncoa7_cdss

Format

A GRangesList object with 9 elements:

element: CDS start and end coordinates for a single transcript (GRanges object)

...

Source

http://www.ensembl.org/

Exons from 9 protein coding transcripts of NCOA7

Description

A dataset containing start and end coordinates of exons from nine protein coding transcripts of NCOA7.

Usage

ncoa7_exons
ncoa7_exons

Format

A GRangesList object with 9 elements:

element: Exon start and end coordinates for a single transcript (GRanges object)

...

Source

http://www.ensembl.org/

Gene metadata for NCOA7

Description

A a list of transcripts for NCOA7.

Usage

ncoa7_metadata
ncoa7_metadata

Format

A data.frame object with 4 columns:

transcript_id: Ensembl transcript id.
gene_id: Ensembl gene id.
gene_name: Human readable gene name.
strand: Strand of the transcript (either +1 or -1).

...

Source

http://www.ensembl.org/

Paste two factors together and preserved their joint order.

Description

Paste two factors together and preserved their joint order.

Usage

pasteFactors(factor1, factor2)
pasteFactors(factor1, factor2)

Arguments

factor1

First factor

factor2

Second factor

Value

Factors factor1 and factor2 pasted together.

Plot read coverage across a genomic region

Description

Also supports rescaling introns to constant length. Extracts read coverage from bigWig files with extractCoverageData and plots it with plotCoverageData. Custom visualisations can be created by modifying the plotCoverageData function. Does not work on Windows, because rtracklayer cannot read BigWig files on Windows.

Usage

plotCoverage(
  exons,
  cdss = NULL,
  transcript_annotations = NULL,
  track_data,
  rescale_introns = TRUE,
  new_intron_length = 50,
  flanking_length = c(50, 50),
  plot_fraction = 0.1,
  heights = c(0.75, 0.25),
  alpha = 1,
  fill_palette = c("#a1dab4", "#41b6c4", "#225ea8"),
  mean_only = TRUE,
  connect_exons = TRUE,
  transcript_label = TRUE,
  return_subplots_list = FALSE,
  region_coords = NULL,
  coverage_type = "area",
  show_legend = FALSE
)
plotCoverage(
  exons,
  cdss = NULL,
  transcript_annotations = NULL,
  track_data,
  rescale_introns = TRUE,
  new_intron_length = 50,
  flanking_length = c(50, 50),
  plot_fraction = 0.1,
  heights = c(0.75, 0.25),
  alpha = 1,
  fill_palette = c("#a1dab4", "#41b6c4", "#225ea8"),
  mean_only = TRUE,
  connect_exons = TRUE,
  transcript_label = TRUE,
  return_subplots_list = FALSE,
  region_coords = NULL,
  coverage_type = "area",
  show_legend = FALSE
)

Arguments

exons

list of GRanges objects, each object containing exons for one transcript. The list must have names that correspond to transcript_id column in transript_annotations data.frame.

cdss

list of GRanges objects, each object containing the coding regions (CDS) of a single transcript. The list must have names that correspond to transcript_id column in transript_annotations data.frame. If cdss is not specified then exons list will be used for both arguments. (default: NULL).

transcript_annotations

Data frame with at least three columns: transcript_id, gene_name, strand. Used to construct transcript labels. (default: NULL)

track_data

data.frame with the metadata for the bigWig read coverage files. Must contain the following columns:

sample_id - unique id for each sample.
track_id - if multiple samples (bigWig files) have the same track_id they will be overlayed on the same plot, track_id is also used as the facet label on the right.
bigWig - path to the bigWig file.
scaling_factor - normalisation factor for each sample, useful if different samples sequenced to different depth and bigWig files not normalised for that.
colour_group - additional column to group samples into, is used as the colour of the coverage track.

rescale_introns

Specifies if the introns should be scaled to fixed length or not. (default: TRUE)

new_intron_length

length (bp) of introns after scaling. (default: 50)

flanking_length

Lengths of the flanking regions upstream and downstream of the gene. (default: c(50,50))

plot_fraction

Size of the random sub-sample of points used to plot coverage (between 0 and 1). Smaller values make plotting significantly faster. (default: 0.1)

heights

Specifies the proportion of the height that is dedicated to coverage plots (first value) relative to transcript annotations (second value). (default: c(0.75,0.25))

alpha

Transparency (alpha) value for the read coverage tracks. Useful to set to something < 1 when overlaying multiple tracks (see track_id). (default: 1)

fill_palette

Vector of fill colours used for the coverage tracks. Length must be equal to the number of unique values in track_data$colour_group column.

mean_only

connect_exons

Print lines that connect exons together. Set to FALSE when plotting peaks (default: TRUE).

transcript_label

If TRUE then transcript labels are printed above each transcript. (default: TRUE).

return_subplots_list

Instead of a joint plot return a list of subplots that can be joined together manually.

region_coords

Start and end coordinates of the region to plot, overrides flanking_length parameter.

coverage_type

Specifies if the read coverage is represented by either 'line', 'area' or 'both'. The 'both' option tends to give better results for wide regions. (default: area).

show_legend

display legend for the colour_group next to the read coverage plot (default: FALSE).

Value

Either object from cow_plot::plot_grid() function or a list of subplots (if return_subplots_list == TRUE)

Examples

require("dplyr")
require("GenomicRanges")
sample_data = dplyr::data_frame(sample_id = c("aipt_A", "aipt_C", "bima_A", "bima_C"), 
    condition = factor(c("Naive", "LPS", "Naive", "LPS"), levels = c("Naive", "LPS")), 
    scaling_factor = 1) %>%
    dplyr::mutate(bigWig = system.file("extdata",  paste0(sample_id, ".str2.bw"), package = "wiggleplotr"))

track_data = dplyr::mutate(sample_data, track_id = condition, colour_group = condition)

selected_transcripts = c("ENST00000438495", "ENST00000392477") #Plot only two transcripts of the gens
## Not run: 
plotCoverage(ncoa7_exons[selected_transcripts], ncoa7_cdss[selected_transcripts], 
   ncoa7_metadata, track_data, 
   heights = c(2,1), fill_palette = getGenotypePalette())

## End(Not run)

require("dplyr")
require("GenomicRanges")
sample_data = dplyr::data_frame(sample_id = c("aipt_A", "aipt_C", "bima_A", "bima_C"), 
    condition = factor(c("Naive", "LPS", "Naive", "LPS"), levels = c("Naive", "LPS")), 
    scaling_factor = 1) %>%
    dplyr::mutate(bigWig = system.file("extdata",  paste0(sample_id, ".str2.bw"), package = "wiggleplotr"))

track_data = dplyr::mutate(sample_data, track_id = condition, colour_group = condition)

selected_transcripts = c("ENST00000438495", "ENST00000392477") #Plot only two transcripts of the gens
## Not run: 
plotCoverage(ncoa7_exons[selected_transcripts], ncoa7_cdss[selected_transcripts], 
   ncoa7_metadata, track_data, 
   heights = c(2,1), fill_palette = getGenotypePalette())

## End(Not run)

Plot read coverage across a genomic region

Description

Does not work on Windows, because rtracklayer cannot read BigWig files on Windows.

Usage

plotCoverageData(
  coverage_data_list,
  heights = c(0.75, 0.25),
  alpha = 1,
  fill_palette = c("#a1dab4", "#41b6c4", "#225ea8"),
  connect_exons = TRUE,
  transcript_label = TRUE,
  return_subplots_list = FALSE,
  coverage_type = "area",
  show_legend = FALSE
)
plotCoverageData(
  coverage_data_list,
  heights = c(0.75, 0.25),
  alpha = 1,
  fill_palette = c("#a1dab4", "#41b6c4", "#225ea8"),
  connect_exons = TRUE,
  transcript_label = TRUE,
  return_subplots_list = FALSE,
  coverage_type = "area",
  show_legend = FALSE
)

Arguments

coverage_data_list

List of required from the extractCoverageData function:

exons - list of GRanges objects, each object containing exons for one transcript.
cdss - list of GRanges objects, each object containing the coding regions (CDS) of a single transcript.
plotting_annotations - Transcript labels for plotting.
tx_annotations - Transcript coordinates for plotting.
xlabel - Label of the x-axis.
coverage_df - Read coverage data frame.
limits - x-axis limits.

heights

Specifies the proportion of the height that is dedicated to coverage plots (first value) relative to transcript annotations (second value). (default: c(0.75,0.25))

alpha

Transparency (alpha) value for the read coverage tracks. Useful to set to something < 1 when overlaying multiple tracks (see track_id). (default: 1)

fill_palette

Vector of fill colours used for the coverage tracks. Length must be equal to the number of unique values in track_data$colour_group column.

connect_exons

Print lines that connect exons together. Set to FALSE when plotting peaks (default: TRUE).

transcript_label

If TRUE then transcript labels are printed above each transcript. (default: TRUE).

return_subplots_list

Instead of a joint plot return a list of subplots that can be joined together manually.

coverage_type

Specifies if the read coverage is represented by either 'line', 'area' or 'both'. The 'both' option tends to give better results for wide regions. (default: area).

show_legend

display legend for the colour_group next to the read coverage plot (default: FALSE).

Value

Either object from cow_plot::plot_grid() function or a list of subplots (if return_subplots_list == TRUE)

Examples

require("dplyr")
require("GenomicRanges")
sample_data = dplyr::data_frame(sample_id = c("aipt_A", "aipt_C", "bima_A", "bima_C"), 
    condition = factor(c("Naive", "LPS", "Naive", "LPS"), levels = c("Naive", "LPS")), 
    scaling_factor = 1) %>%
    dplyr::mutate(bigWig = system.file("extdata",  paste0(sample_id, ".str2.bw"), package = "wiggleplotr"))

track_data = dplyr::mutate(sample_data, track_id = condition, colour_group = condition)

selected_transcripts = c("ENST00000438495", "ENST00000392477") #Plot only two transcripts of the gens
## Not run: 
cov_data = extractCoverageData(ncoa7_exons[selected_transcripts], ncoa7_cdss[selected_transcripts], ncoa7_metadata, track_data)
plotCoverageData(cov_data, heights = c(2,1), fill_palette = getGenotypePalette())

## End(Not run)

require("dplyr")
require("GenomicRanges")
sample_data = dplyr::data_frame(sample_id = c("aipt_A", "aipt_C", "bima_A", "bima_C"), 
    condition = factor(c("Naive", "LPS", "Naive", "LPS"), levels = c("Naive", "LPS")), 
    scaling_factor = 1) %>%
    dplyr::mutate(bigWig = system.file("extdata",  paste0(sample_id, ".str2.bw"), package = "wiggleplotr"))

track_data = dplyr::mutate(sample_data, track_id = condition, colour_group = condition)

selected_transcripts = c("ENST00000438495", "ENST00000392477") #Plot only two transcripts of the gens
## Not run: 
cov_data = extractCoverageData(ncoa7_exons[selected_transcripts], ncoa7_cdss[selected_transcripts], ncoa7_metadata, track_data)
plotCoverageData(cov_data, heights = c(2,1), fill_palette = getGenotypePalette())

## End(Not run)

Plot read coverage directly from ensembldb object.

Description

A wrapper around the plotCoverage function. See the documentation for (plotCoverage) for more information.

Usage

plotCoverageFromEnsembldb(ensembldb, gene_names, transcript_ids = NULL, ...)
plotCoverageFromEnsembldb(ensembldb, gene_names, transcript_ids = NULL, ...)

Arguments

ensembldb

ensembldb object.

gene_names

List of gene names to be plotted.

transcript_ids

Optional list of transcript ids to be plotted.

...

Additional parameters to be passed to plotCoverage.

Value

ggplot2 object

Examples

require("EnsDb.Hsapiens.v86")
require("dplyr")
require("GenomicRanges")
sample_data = dplyr::data_frame(sample_id = c("aipt_A", "aipt_C", "bima_A", "bima_C"), 
 condition = factor(c("Naive", "LPS", "Naive", "LPS"), levels = c("Naive", "LPS")), 
 scaling_factor = 1) %>%
 dplyr::mutate(bigWig = system.file("extdata",  paste0(sample_id, ".str2.bw"), package = "wiggleplotr"))
 
track_data = dplyr::mutate(sample_data, track_id = condition, colour_group = condition)
## Not run: 
plotCoverageFromEnsembldb(EnsDb.Hsapiens.v86, "NCOA7", transcript_ids = c("ENST00000438495", "ENST00000392477"), 
track_data, heights = c(2,1), fill_palette = getGenotypePalette())

## End(Not run)
require("EnsDb.Hsapiens.v86")
require("dplyr")
require("GenomicRanges")
sample_data = dplyr::data_frame(sample_id = c("aipt_A", "aipt_C", "bima_A", "bima_C"), 
 condition = factor(c("Naive", "LPS", "Naive", "LPS"), levels = c("Naive", "LPS")), 
 scaling_factor = 1) %>%
 dplyr::mutate(bigWig = system.file("extdata",  paste0(sample_id, ".str2.bw"), package = "wiggleplotr"))
 
track_data = dplyr::mutate(sample_data, track_id = condition, colour_group = condition)
## Not run: 
plotCoverageFromEnsembldb(EnsDb.Hsapiens.v86, "NCOA7", transcript_ids = c("ENST00000438495", "ENST00000392477"), 
track_data, heights = c(2,1), fill_palette = getGenotypePalette())

## End(Not run)

Plot read coverage directly from UCSC OrgDb and TxDb objects.

Description

A wrapper around the plotCoverage function. See the documentation for (plotCoverage) for more information.

Usage

plotCoverageFromUCSC(orgdb, txdb, gene_names, transcript_ids = NULL, ...)
plotCoverageFromUCSC(orgdb, txdb, gene_names, transcript_ids = NULL, ...)

Arguments

orgdb

UCSC OrgDb object.

txdb

UCSC TxDb obejct.

gene_names

List of gene names to be plotted.

transcript_ids

Optional list of transcript ids to be plotted.

...

Additional parameters to be passed to plotCoverage.

Value

ggplot2 object

Examples

require("dplyr")
require("GenomicRanges")
require("org.Hs.eg.db")
require("TxDb.Hsapiens.UCSC.hg38.knownGene")

orgdb = org.Hs.eg.db
txdb = TxDb.Hsapiens.UCSC.hg38.knownGene

sample_data = dplyr::data_frame(sample_id = c("aipt_A", "aipt_C", "bima_A", "bima_C"), 
 condition = factor(c("Naive", "LPS", "Naive", "LPS"), levels = c("Naive", "LPS")), 
 scaling_factor = 1) %>%
 dplyr::mutate(bigWig = system.file("extdata",  paste0(sample_id, ".str2.bw"), package = "wiggleplotr"))
 
track_data = dplyr::mutate(sample_data, track_id = condition, colour_group = condition)
## Not run: 
#Note: This example does not work, becasue UCSC and Ensembl use different chromosome names
plotCoverageFromUCSC(orgdb, txdb, "NCOA7", transcript_ids = c("ENST00000438495.6", "ENST00000368357.7"), 
track_data, heights = c(2,1), fill_palette = getGenotypePalette())

## End(Not run)
require("dplyr")
require("GenomicRanges")
require("org.Hs.eg.db")
require("TxDb.Hsapiens.UCSC.hg38.knownGene")

orgdb = org.Hs.eg.db
txdb = TxDb.Hsapiens.UCSC.hg38.knownGene

sample_data = dplyr::data_frame(sample_id = c("aipt_A", "aipt_C", "bima_A", "bima_C"), 
 condition = factor(c("Naive", "LPS", "Naive", "LPS"), levels = c("Naive", "LPS")), 
 scaling_factor = 1) %>%
 dplyr::mutate(bigWig = system.file("extdata",  paste0(sample_id, ".str2.bw"), package = "wiggleplotr"))
 
track_data = dplyr::mutate(sample_data, track_id = condition, colour_group = condition)
## Not run: 
#Note: This example does not work, becasue UCSC and Ensembl use different chromosome names
plotCoverageFromUCSC(orgdb, txdb, "NCOA7", transcript_ids = c("ENST00000438495.6", "ENST00000368357.7"), 
track_data, heights = c(2,1), fill_palette = getGenotypePalette())

## End(Not run)

Quickly plot transcript structure without read coverage tracks

Description

Quickly plot transcript structure without read coverage tracks

Usage

plotTranscripts(
  exons,
  cdss = NULL,
  transcript_annotations = NULL,
  rescale_introns = TRUE,
  new_intron_length = 50,
  flanking_length = c(50, 50),
  connect_exons = TRUE,
  transcript_label = TRUE,
  region_coords = NULL
)
plotTranscripts(
  exons,
  cdss = NULL,
  transcript_annotations = NULL,
  rescale_introns = TRUE,
  new_intron_length = 50,
  flanking_length = c(50, 50),
  connect_exons = TRUE,
  transcript_label = TRUE,
  region_coords = NULL
)

Arguments

exons

list of GRanges objects, each object containing exons for one transcript. The list must have names that correspond to transcript_id column in transript_annotations data.frame.

cdss

transcript_annotations

Data frame with at least three columns: transcript_id, gene_name, strand. Used to construct transcript labels. (default: NULL)

rescale_introns

Specifies if the introns should be scaled to fixed length or not. (default: TRUE)

new_intron_length

length (bp) of introns after scaling. (default: 50)

flanking_length

Lengths of the flanking regions upstream and downstream of the gene. (default: c(50,50))

connect_exons

Print lines that connect exons together. Set to FALSE when plotting peaks (default: TRUE).

transcript_label

If TRUE then transcript labels are printed above each transcript. (default: TRUE).

region_coords

Start and end coordinates of the region to plot, overrides flanking_length parameter.

Value

ggplot2 object

Examples

plotTranscripts(ncoa7_exons, ncoa7_cdss, ncoa7_metadata, rescale_introns = FALSE)

plotTranscripts(ncoa7_exons, ncoa7_cdss, ncoa7_metadata, rescale_introns = FALSE)

Plot transcripts directly from ensembldb object.

Description

A wrapper around the plotTranscripts function. See the documentation for (plotTranscripts) for more information.

Usage

plotTranscriptsFromEnsembldb(ensembldb, gene_names, transcript_ids = NULL, ...)
plotTranscriptsFromEnsembldb(ensembldb, gene_names, transcript_ids = NULL, ...)

Arguments

ensembldb

ensembldb object.

gene_names

List of gene names to be plotted.

transcript_ids

Optional list of transcript ids to be plotted.

...

Additional parameters to be passed to plotTranscripts

Value

ggplot2 object

Examples

require("EnsDb.Hsapiens.v86")
plotTranscriptsFromEnsembldb(EnsDb.Hsapiens.v86, "NCOA7", transcript_ids = c("ENST00000438495", "ENST00000392477"))
require("EnsDb.Hsapiens.v86")
plotTranscriptsFromEnsembldb(EnsDb.Hsapiens.v86, "NCOA7", transcript_ids = c("ENST00000438495", "ENST00000392477"))

Plot transcripts directly from UCSC OrgDb and TxDb objects.

Description

A wrapper around the plotTranscripts function. See the documentation for (plotTranscripts) for more information. Note that this function is much slower than (plotTranscripts) or (plotTranscriptsFromEnsembldb) functions, because indivudally extracting exon coordinates from txdb objects is quite inefficient.

Usage

plotTranscriptsFromUCSC(orgdb, txdb, gene_names, transcript_ids = NULL, ...)
plotTranscriptsFromUCSC(orgdb, txdb, gene_names, transcript_ids = NULL, ...)

Arguments

orgdb

UCSC OrgDb object.

txdb

UCSC TxDb obejct.

gene_names

List of gene genaes to be plot.

transcript_ids

Optional list of transcript ids to be plot. (default = NULL)

...

Additional parameters to be passed to plotTranscripts

Value

Transcript plot.

Examples

#Load OrgDb and TxDb objects with UCSC gene annotations
require("org.Hs.eg.db")
require("TxDb.Hsapiens.UCSC.hg38.knownGene")
orgdb = org.Hs.eg.db
txdb = TxDb.Hsapiens.UCSC.hg38.knownGene

plotTranscriptsFromUCSC(orgdb, txdb, "NCOA7", transcript_ids = c("ENST00000438495.6", "ENST00000368357.7"))
#Load OrgDb and TxDb objects with UCSC gene annotations
require("org.Hs.eg.db")
require("TxDb.Hsapiens.UCSC.hg38.knownGene")
orgdb = org.Hs.eg.db
txdb = TxDb.Hsapiens.UCSC.hg38.knownGene

plotTranscriptsFromUCSC(orgdb, txdb, "NCOA7", transcript_ids = c("ENST00000438495.6", "ENST00000368357.7"))

wiggleplotr

Description

wiggleplotr package provides tools to visualise transcript annotations (plotTranscripts) and plot sequencing read coverage over annotated transcripts (plotCoverage).

Details

You can also use covenient wrapper functions (plotTranscriptsFromEnsembldb), (plotCoverageFromEnsembldb), (plotTranscriptsFromUCSC) and (plotCoverageFromUCSC).

To learn more about wiggleplotr, start with the vignette: browseVignettes(package = "wiggleplotr")

Package 'wiggleplotr'

Help Index

Extract read coverage data from the bigWig files

Description

Usage

Arguments

Value

Examples

Returns a three-colour palette suitable for visualising read coverage stratified by genotype

Description

Usage

Arguments

Value

Examples

Make a Manahattan plot of p-values

Description

Usage

Arguments

Value

Examples

Coding sequences from 9 protein coding transcripts of NCOA7

Description

Usage

Format

Source

Exons from 9 protein coding transcripts of NCOA7

Description

Usage

Format

Source

Gene metadata for NCOA7

Description

Usage

Format

Source

Paste two factors together and preserved their joint order.

Description

Usage

Arguments

Value

Plot read coverage across a genomic region

Description

Usage

Arguments

Value

Examples

Plot read coverage across a genomic region

Description

Usage

Arguments

Value

Examples

Plot read coverage directly from ensembldb object.

Description

Usage

Arguments

Value

Examples

Plot read coverage directly from UCSC OrgDb and TxDb objects.

Description

Usage

Arguments

Value

Examples

Quickly plot transcript structure without read coverage tracks

Description

Usage

Arguments

Value

Examples

Plot transcripts directly from ensembldb object.

Description

Usage

Arguments

Value

Examples

Plot transcripts directly from UCSC OrgDb and TxDb objects.

Description

Usage

Arguments