Package 'NanoMethViz' reference manual

Title:	Visualise methylation data from Oxford Nanopore sequencing
Description:	NanoMethViz is a toolkit for visualising methylation data from Oxford Nanopore sequencing. It can be used to explore methylation patterns from reads derived from Oxford Nanopore direct DNA sequencing with methylation called by callers including nanopolish, f5c and megalodon. The plots in this package allow the visualisation of methylation profiles aggregated over experimental groups and across classes of genomic features.
Authors:	Shian Su [cre, aut]
Maintainer:	Shian Su <[email protected]>
License:	Apache License (>= 2.0)
Version:	3.3.0
Built:	2024-11-25 06:17:48 UTC
Source:	https://github.com/bioc/NanoMethViz

Convert BSseq object to edgeR methylation matrix

Description

Convert BSseq object to edgeR methylation matrix

Usage

bsseq_to_edger(bsseq, regions = NULL)
bsseq_to_edger(bsseq, regions = NULL)

Arguments

`bsseq`	the BSseq object.
`regions`	the regions to calculate log-methylation ratios over. If left NULL, ratios will be calculated per site.

Value

a matrix compatible with the edgeR differential methylation pipeline

Examples

methy <- system.file("methy_subset.tsv.bgz", package = "NanoMethViz")
bsseq <- methy_to_bsseq(methy)
edger_mat <- bsseq_to_edger(bsseq)
methy <- system.file("methy_subset.tsv.bgz", package = "NanoMethViz")
bsseq <- methy_to_bsseq(methy)
edger_mat <- bsseq_to_edger(bsseq)

Convert BSseq object to log-methylation-ratio matrix

Description

Creates a log-methylation-ratio matrix from a BSseq object that is useful for dimensionality reduction plots.

Usage

bsseq_to_log_methy_ratio(
  bsseq,
  regions = NULL,
  prior_count = 2,
  drop_na = TRUE
)
bsseq_to_log_methy_ratio(
  bsseq,
  regions = NULL,
  prior_count = 2,
  drop_na = TRUE
)

Arguments

`bsseq`	the BSseq object.
`regions`	the regions to calculate log-methylation ratios over. If left NULL, ratios will be calculated per site.
`prior_count`	the prior count added to avoid taking log of 0.
`drop_na`	whether to drop rows with all NA values.

Value

a matrix containing log-methylation-ratios.

Examples

nmr <- load_example_nanomethresult()
bsseq <- methy_to_bsseq(nmr)
regions <- exons_to_genes(NanoMethViz::exons(nmr))
log_m_ratio <- bsseq_to_log_methy_ratio(bsseq, regions)
nmr <- load_example_nanomethresult()
bsseq <- methy_to_bsseq(nmr)
regions <- exons_to_genes(NanoMethViz::exons(nmr))
log_m_ratio <- bsseq_to_log_methy_ratio(bsseq, regions)

Cluster reads based on methylation

Description

Cluster reads based on methylation

Usage

cluster_reads(x, chr, start, end, min_pts = 5)
cluster_reads(x, chr, start, end, min_pts = 5)

Arguments

`x`	a ModBamResult object.
`chr`	the chromosome name where to find the region.
`start`	the start position of the region.
`end`	the end position of the region.
`min_pts`	the minimum number of points needed to form a cluster (default = 10).

Value

A tibble with information about each read's cluster assignment and read statistics.

Cluster regions by K-means

Description

Cluster regions by k-means based on their methylation profiles. In order to cluster using k-means the methylation profile of each region is interpolated and sampled at fixed points. The first 10 principal components are used for the k-means clustering. The clustering is best behaved in regions of similar width and CpG density.

Usage

cluster_regions(x, regions, centers = 2, grid_method = c("density", "uniform"))
cluster_regions(x, regions, centers = 2, grid_method = c("density", "uniform"))

Arguments

`x`	the NanoMethResult object.
`regions`	a table of regions containing at least columns chr, strand, start and end.
`centers`	number of centers for k-means, identical to the number of output clusters.
`grid_method`	the method for generating the sampling grid. The default option "density" attempts to create a grid with similar density as the data, "uniform" creates a grid of uniform density.

Value

the table of regions given by the 'regions' argument with the column 'cluster' added.

Examples

nmr <- load_example_nanomethresult()
gene_anno <- exons_to_genes(NanoMethViz::exons(nmr))
# uniform grid due to low number of input features
gene_anno_clustered <- cluster_regions(nmr, gene_anno, centers = 2, grid_method = "uniform")
plot_agg_regions(nmr, gene_anno_clustered, group_col = "cluster")
nmr <- load_example_nanomethresult()
gene_anno <- exons_to_genes(NanoMethViz::exons(nmr))
# uniform grid due to low number of input features
gene_anno_clustered <- cluster_regions(nmr, gene_anno, centers = 2, grid_method = "uniform")
plot_agg_regions(nmr, gene_anno_clustered, group_col = "cluster")

Create a tabix file using methylation calls

Description

Create a tabix file using methylation calls

Usage

create_tabix_file(
  input_files,
  output_file,
  samples = extract_file_names(input_files),
  verbose = TRUE
)
create_tabix_file(
  input_files,
  output_file,
  samples = extract_file_names(input_files),
  verbose = TRUE
)

Arguments

`input_files`	the files to convert
`output_file`	the output file to write results to (must end in .bgz)
`samples`	the names of samples corresponding to each file
`verbose`	TRUE if progress messages are to be printed

Value

invisibly returns the output file path, creates a tabix file (.bgz) and its index (.bgz.tbi)

Examples

methy_calls <- system.file(package = "NanoMethViz",
    c("sample1_nanopolish.tsv.gz", "sample2_nanopolish.tsv.gz"))
temp_file <- paste0(tempfile(), ".tsv.bgz")

create_tabix_file(methy_calls, temp_file)
methy_calls <- system.file(package = "NanoMethViz",
    c("sample1_nanopolish.tsv.gz", "sample2_nanopolish.tsv.gz"))
temp_file <- paste0(tempfile(), ".tsv.bgz")

create_tabix_file(methy_calls, temp_file)

Convert exon annotation to genes

Description

Convert exon annotation to genes

Usage

exons_to_genes(x)
exons_to_genes(x)

Arguments

`x`	the exon level annotation containing columns "gene_id", "chr", "strand" and "symbol".

Value

the gene level annotation where each gene is taken to span the earliest start position and latest end position of its exons.

Examples

nmr <- load_example_nanomethresult()
exons_to_genes(NanoMethViz::exons(nmr))
nmr <- load_example_nanomethresult()
exons_to_genes(NanoMethViz::exons(nmr))

Create filtered methylation file

Description

Create a filtered methylation file from an existing one.

Usage

filter_methy(x, output_file, ...)
filter_methy(x, output_file, ...)

Arguments

`x`	the path to the methylation file or a NanoMethResult object.
`output_file`	the output file to write results to (must end in .bgz).
`...`	filtering criteria given in dplyr syntax. Use methy_col_names() to get available column names.

Value

invisibly returns 'output_file' if x is a file path, otherwise returns NanoMethResult object with methy(x) replaced with filtered value.

Examples

nmr <- load_example_nanomethresult()
output_file <- paste0(tempfile(), ".tsv.bgz")
filter_methy(nmr, output_file = output_file, chr == "chrX")
filter_methy(methy(nmr), output_file = output_file, chr == "chrX")
nmr <- load_example_nanomethresult()
output_file <- paste0(tempfile(), ".tsv.bgz")
filter_methy(nmr, output_file = output_file, chr == "chrX")
filter_methy(methy(nmr), output_file = output_file, chr == "chrX")

Get exon annotations

Description

Helper functions are provided for obtaining exon annotations from relevant TxDb packages on Bioconductor for the construction of NanoMethResults objects.

Usage

get_cgi_mm10()

get_cgi_grcm39()

get_cgi_hg19()

get_cgi_hg38()

get_exons_mm10()

get_exons_grcm39()

get_exons_hg19()

get_exons_hg38()
get_cgi_mm10()

get_cgi_grcm39()

get_cgi_hg19()

get_cgi_hg38()

get_exons_mm10()

get_exons_grcm39()

get_exons_hg19()

get_exons_hg38()

Value

tibble (data.frame) object containing exon annotation.

Examples

cgi_mm10 <- get_cgi_mm10()

cgi_GRCm39 <- get_cgi_grcm39()

cgi_hg19 <- get_cgi_hg19()

cgi_hg38 <- get_cgi_hg38()

mm10_exons <- get_exons_mm10()

grcm39_exons <- get_exons_grcm39()

hg19_exons <- get_exons_hg19()

hg38_exons <- get_exons_hg38()

cgi_mm10 <- get_cgi_mm10()

cgi_GRCm39 <- get_cgi_grcm39()

cgi_hg19 <- get_cgi_hg19()

cgi_hg38 <- get_cgi_hg38()

mm10_exons <- get_exons_mm10()

grcm39_exons <- get_exons_grcm39()

hg19_exons <- get_exons_hg19()

hg38_exons <- get_exons_hg38()

Get example exon annotations for mus musculus (mm10)

Description

This is a small subset of the exons returned by get_exons_mus_musculus() for demonstrative purposes. It contains the exons for the genes Brca1, Brca2, Impact, Meg3, Peg3 and Xist.

Usage

get_example_exons_mus_musculus()
get_example_exons_mus_musculus()

Value

data.frame containing exons

Examples

example_exons <- get_example_exons_mus_musculus()

example_exons <- get_example_exons_mus_musculus()

Get exon annotations for Homo sapiens (hg19)

Description

Get exon annotations for Homo sapiens (hg19)

Usage

get_exons_homo_sapiens()
get_exons_homo_sapiens()

Value

data.frame containing exons

Examples

h_sapiens_exons <- get_exons_homo_sapiens()

h_sapiens_exons <- get_exons_homo_sapiens()

Get exon annotations for Mus musculus (mm10)

Description

Get exon annotations for Mus musculus (mm10)

Usage

get_exons_mus_musculus()
get_exons_mus_musculus()

Value

data.frame containing exons

Examples

m_musculus_exons <- get_exons_mus_musculus()

m_musculus_exons <- get_exons_mus_musculus()

Load an example ModBamResult object

Description

Load an example ModBamResult object for demonstration of plotting functions. Run load_example_modbamresult without the function call to see how the object is constructed.

Usage

load_example_modbamresult()
load_example_modbamresult()

Value

a ModBamResult object

Examples

mbr <- load_example_modbamresult()
mbr <- load_example_modbamresult()

Load an example NanoMethResult object

Description

Load an example NanoMethResult object for demonstration of plotting functions. Run load_example_nanomethresults without the function call to see how the object is constructed.

Usage

load_example_nanomethresult()
load_example_nanomethresult()

Value

a NanoMethResults object

Examples

nmr <- load_example_nanomethresult()
nmr <- load_example_nanomethresult()

Column names for methylation data

Description

Column names for methylation data

Usage

methy_col_names()
methy_col_names()

Value

column names for methylation data

Examples

methy_col_names()
methy_col_names()

Create BSSeq object from methylation tabix file

Description

Create BSSeq object from methylation tabix file

Usage

methy_to_bsseq(methy, out_folder = tempdir(), verbose = TRUE)
methy_to_bsseq(methy, out_folder = tempdir(), verbose = TRUE)

Arguments

`methy`	the NanoMethResult object or path to the methylation tabix file.
`out_folder`	the folder to store intermediate files. One file is created for each sample and contains columns "chr", "pos", "total" and "methylated".
`verbose`	TRUE if progress messages are to be printed

Value

a BSSeq object.

Examples

nmr <- load_example_nanomethresult()
bsseq <- methy_to_bsseq(nmr)
nmr <- load_example_nanomethresult()
bsseq <- methy_to_bsseq(nmr)

Convert NanoMethResult object to edgeR methylation matrix

Description

Convert NanoMethResult object to edgeR methylation matrix

Usage

methy_to_edger(methy, regions = NULL, out_folder = tempdir(), verbose = TRUE)
methy_to_edger(methy, regions = NULL, out_folder = tempdir(), verbose = TRUE)

Arguments

`methy`	the NanoMethResult object or path to the methylation tabix file.
`regions`	the regions to calculate log-methylation ratios over. If left NULL, ratios will be calculated per site.
`out_folder`	the folder to store intermediate files. One file is created for each sample and contains columns "chr", "pos", "total" and "methylated".
`verbose`	TRUE if progress messages are to be printed

Value

a matrix compatible with the edgeR differential methylation pipeline

Examples

nmr <- load_example_nanomethresult()
edger_mat <- methy_to_edger(nmr)

nmr <- load_example_nanomethresult()
edger_mat <- methy_to_edger(nmr)

Convert BAM with modifications to tabix format

Description

The modbam_to_tabix function takes a ModBamResult object and converts it into a tabix file format, which is efficient for indexing and querying large datasets.

Usage

modbam_to_tabix(x, out_file, mod_code = NanoMethViz::mod_code(x))
modbam_to_tabix(x, out_file, mod_code = NanoMethViz::mod_code(x))

Arguments

`x`	the `ModBamResult` object.
`out_file`	the path of the output tabix.
`mod_code`	the modification code to use, defaults to 'm' for 5mC methylation.

Details

The possible tags for mod_code can be found at https://samtools.github.io/hts-specs/SAMtags.pdf under the 'Base modifications' section.

Value

invisibly returns the name of the created tabix file.

Examples

out_file <- paste0(tempfile(), ".tsv.bgz")
mbr <- ModBamResult(
    methy = ModBamFiles(
        samples = "sample1",
        paths = system.file("peg3.bam", package = "NanoMethViz")
    ),
    samples = data.frame(
        sample = "sample1",
        group = "group1"
    )
)

modbam_to_tabix(mbr, out_file)

out_file <- paste0(tempfile(), ".tsv.bgz")
mbr <- ModBamResult(
    methy = ModBamFiles(
        samples = "sample1",
        paths = system.file("peg3.bam", package = "NanoMethViz")
    ),
    samples = data.frame(
        sample = "sample1",
        group = "group1"
    )
)

modbam_to_tabix(mbr, out_file)

Constructor for a ModBamFiles object

Description

This function creates a ModBamFiles object containing information about the samples and file paths. This constructor checks that the files are readable and have an index.

Usage

ModBamFiles(samples, paths)

## S4 method for signature 'ModBamFiles'
show(object)
ModBamFiles(samples, paths)

## S4 method for signature 'ModBamFiles'
show(object)

Arguments

`samples`	a character vector with the names of the samples.
`paths`	a character vector with the file paths for the BAM files.
`object`	a ModBamFiles object.

Value

A ModBamFiles object with the sample and path information.

ModBamFiles class

Description

This is a class for holding information about modbam files. It is a data.frame containing information about samples and paths to modbam files.

Modbam methylation results

Description

A ModBamResult object stores modbam data used for NanoMethViz visualisation. It contains stores a ModBamFiles object, sample information and optional exon information. The object is constructed using the ModBamResult() constructor function described in "Usage".

Usage

## S4 method for signature 'ModBamResult'
methy(object)

## S4 replacement method for signature 'ModBamResult,ModBamFiles'
methy(object) <- value

## S4 method for signature 'ModBamResult'
samples(object)

## S4 replacement method for signature 'ModBamResult,data.frame'
samples(object) <- value

## S4 method for signature 'ModBamResult'
exons(object)

## S4 replacement method for signature 'ModBamResult,data.frame'
exons(object) <- value

## S4 method for signature 'ModBamResult'
mod_code(object)

## S4 replacement method for signature 'ModBamResult,character'
mod_code(object) <- value

ModBamResult(methy, samples, exons = NULL, mod_code = "m")
## S4 method for signature 'ModBamResult'
methy(object)

## S4 replacement method for signature 'ModBamResult,ModBamFiles'
methy(object) <- value

## S4 method for signature 'ModBamResult'
samples(object)

## S4 replacement method for signature 'ModBamResult,data.frame'
samples(object) <- value

## S4 method for signature 'ModBamResult'
exons(object)

## S4 replacement method for signature 'ModBamResult,data.frame'
exons(object) <- value

## S4 method for signature 'ModBamResult'
mod_code(object)

## S4 replacement method for signature 'ModBamResult,character'
mod_code(object) <- value

ModBamResult(methy, samples, exons = NULL, mod_code = "m")

Arguments

`object`	the ModBamResult object.
`value`	the mod code.
`methy`	a ModBamFiles object.
`samples`	the data.frame of sample annotation containing at least columns sample and group.
`exons`	(optional) the data.frame of exon information containing at least columns gene_id, chr, strand, start, end, transcript_id and symbol.
`mod_code`	a character with the mod code of interest. Defaults to "m" for 5mC. See details for other options.

Details

The possible tags for mod_code can be found at https://samtools.github.io/hts-specs/SAMtags.pdf under the 'Base modifications' section.

Value

a NanoMethResult object to be used with plotting functions

a ModBamFiles data.frame.

the sample annotation.

the exon annotation.

the mod code.

Functions

methy(ModBamResult): modbam information getter.
methy(object = ModBamResult) <- value: modbam information setter.
samples(ModBamResult): sample annotation getter.
samples(object = ModBamResult) <- value: sample annotation setter.
exons(ModBamResult): exon annotation getter.
exons(object = ModBamResult) <- value: exon annotation setter.
mod_code(ModBamResult): mod code getter.
mod_code(object = ModBamResult) <- value: mod code setter.
ModBamResult(): Constructor

Slots

methy: a ModBamFiles data.frame specifying the samples and paths to bam files.
samples: the data.frame of sample annotation containing at least columns sample and group.
exons: the data.frame of exon information containing at least columns gene_id, chr, strand, start, end, transcript_id and symbol.
mod_code: the modification code of interest.

Nanopore Methylation Result

Description

A NanoMethResult object stores data used for NanoMethViz visualisation. It contains stores a path to the methylation data, sample information and optional exon information. The object is constructed using the NanoMethResult() constructor function described in "Usage".

Usage

NanoMethResult(methy, samples, exons = NULL)

## S4 method for signature 'NanoMethResult'
methy(object)

## S4 replacement method for signature 'NanoMethResult,ANY'
methy(object) <- value

## S4 method for signature 'NanoMethResult'
samples(object)

## S4 replacement method for signature 'NanoMethResult,data.frame'
samples(object) <- value

## S4 method for signature 'NanoMethResult'
exons(object)

## S4 replacement method for signature 'NanoMethResult,data.frame'
exons(object) <- value
NanoMethResult(methy, samples, exons = NULL)

## S4 method for signature 'NanoMethResult'
methy(object)

## S4 replacement method for signature 'NanoMethResult,ANY'
methy(object) <- value

## S4 method for signature 'NanoMethResult'
samples(object)

## S4 replacement method for signature 'NanoMethResult,data.frame'
samples(object) <- value

## S4 method for signature 'NanoMethResult'
exons(object)

## S4 replacement method for signature 'NanoMethResult,data.frame'
exons(object) <- value

Arguments

`methy`	the path to the methylation tabix file.
`samples`	the data.frame of sample annotation containing at least columns sample and group.
`exons`	(optional) the data.frame of exon information containing at least columns gene_id, chr, strand, start, end, transcript_id and symbol.
`object`	the NanoMethResult object.
`value`	the exon annotation.

Value

a NanoMethResult object to be used with plotting functions

the path to the methylation data.

the sample annotation.

the exon annotation.

Functions

NanoMethResult(): Constructor
methy(NanoMethResult): methylation data path getter.
methy(object = NanoMethResult) <- value: methylation data path setter.
samples(NanoMethResult): sample annotation getter.
samples(object = NanoMethResult) <- value: sample annotation setter.
exons(NanoMethResult): exon annotation getter.
exons(object = NanoMethResult) <- value: exon annotation setter.

Slots

methy: the path to the methylation tabix file.
samples: the data.frame of sample annotation containing at least columns sample and group.
exons: the data.frame of exon information containing at least columns gene_id, chr, strand, start, end, transcript_id and symbol.

Examples

methy <- system.file(package = "NanoMethViz", "methy_subset.tsv.bgz")
sample <- c(
    "B6Cast_Prom_1_bl6",
    "B6Cast_Prom_1_cast",
    "B6Cast_Prom_2_bl6",
    "B6Cast_Prom_2_cast",
    "B6Cast_Prom_3_bl6",
    "B6Cast_Prom_3_cast"
)
group <- c(
    "bl6",
    "cast",
    "bl6",
    "cast",
    "bl6",
    "cast"
)
sample_anno <- data.frame(sample, group, stringsAsFactors = FALSE)
exon_tibble <- get_example_exons_mus_musculus()
NanoMethResult(methy, sample_anno, exon_tibble)

x <- load_example_nanomethresult()
methy(x)

methy <- system.file(package = "NanoMethViz", "methy_subset.tsv.bgz")
sample <- c(
    "B6Cast_Prom_1_bl6",
    "B6Cast_Prom_1_cast",
    "B6Cast_Prom_2_bl6",
    "B6Cast_Prom_2_cast",
    "B6Cast_Prom_3_bl6",
    "B6Cast_Prom_3_cast"
)
group <- c(
    "bl6",
    "cast",
    "bl6",
    "cast",
    "bl6",
    "cast"
)
sample_anno <- data.frame(sample, group, stringsAsFactors = FALSE)
exon_tibble <- get_example_exons_mus_musculus()
NanoMethResult(methy, sample_anno, exon_tibble)

x <- load_example_nanomethresult()
methy(x)

Plot gene aggregate plot

Description

Plot gene aggregate plot

Usage

plot_agg_genes(
  x,
  genes = NULL,
  binary_threshold = 0.5,
  group_col = NULL,
  flank = 2000,
  stranded = TRUE,
  span = 0.05,
  palette = ggplot2::scale_colour_brewer(palette = "Set1")
)
plot_agg_genes(
  x,
  genes = NULL,
  binary_threshold = 0.5,
  group_col = NULL,
  flank = 2000,
  stranded = TRUE,
  span = 0.05,
  palette = ggplot2::scale_colour_brewer(palette = "Set1")
)

Arguments

`x`	the NanoMethResult object.
`genes`	a character vector of genes to include in aggregate plot, if NULL then all genes are used.
`binary_threshold`	the modification probability such that calls with modification probability above the threshold are considered methylated, and those with probability equal or below are considered unmethylated.
`group_col`	the column to group aggregated trends by. This column can be in from the regions table or samples(x).
`flank`	the number of flanking bases to add to each side of each region.
`stranded`	TRUE if negative strand features should have coordinates flipped to reflect features like transcription start sites.
`span`	the span for loess smoothing.
`palette`	the ggplot colour palette used for groups.

Value

a ggplot object containing the aggregate methylation trend of genes.

Examples

nmr <- load_example_nanomethresult()
plot_agg_genes(nmr)

nmr <- load_example_nanomethresult()
plot_agg_genes(nmr)

Plot aggregate regions

Description

Plot aggregate regions

Usage

plot_agg_regions(
  x,
  regions,
  binary_threshold = 0.5,
  group_col = NULL,
  flank = 2000,
  stranded = TRUE,
  span = 0.05,
  palette = ggplot2::scale_colour_brewer(palette = "Set1")
)
plot_agg_regions(
  x,
  regions,
  binary_threshold = 0.5,
  group_col = NULL,
  flank = 2000,
  stranded = TRUE,
  span = 0.05,
  palette = ggplot2::scale_colour_brewer(palette = "Set1")
)

Arguments

`x`	the NanoMethResult object.
`regions`	a table of regions containing at least columns chr, strand, start and end. Any additional columns can be used for grouping.
`binary_threshold`	the modification probability such that calls with modification probability above the threshold are considered methylated, and those with probability equal or below are considered unmethylated.
`group_col`	the column to group aggregated trends by. This column can be in from the regions table or samples(x).
`flank`	the number of flanking bases to add to each side of each region.
`stranded`	TRUE if negative strand features should have coordinates flipped to reflect features like transcription start sites.
`span`	the span for loess smoothing.
`palette`	the ggplot colour palette used for groups.

Value

a ggplot object containing the aggregate methylation trend.

Examples

nmr <- load_example_nanomethresult()
gene_anno <- exons_to_genes(NanoMethViz::exons(nmr))
plot_agg_regions(nmr, gene_anno)
plot_agg_regions(nmr, gene_anno, group_col = "sample")
plot_agg_regions(nmr, gene_anno, group_col = "group")

nmr <- load_example_nanomethresult()
gene_anno <- exons_to_genes(NanoMethViz::exons(nmr))
plot_agg_regions(nmr, gene_anno)
plot_agg_regions(nmr, gene_anno, group_col = "sample")
plot_agg_regions(nmr, gene_anno, group_col = "group")

Plot gene methylation

Description

Plot the methylation of a gene symbol specified within the exon(x) slot.

Usage

plot_gene(x, gene, ...)

## S4 method for signature 'NanoMethResult,character'
plot_gene(
  x,
  gene,
  window_prop = 0.3,
  anno_regions = NULL,
  binary_threshold = NULL,
  avg_method = c("mean", "median"),
  spaghetti = FALSE,
  heatmap = TRUE,
  heatmap_subsample = 50,
  smoothing_window = 2000,
  gene_anno = TRUE,
  palette = ggplot2::scale_colour_brewer(palette = "Set1"),
  line_size = 1,
  mod_scale = c(0, 1),
  span = NULL
)

## S4 method for signature 'ModBamResult,character'
plot_gene(
  x,
  gene,
  window_prop = 0.3,
  anno_regions = NULL,
  binary_threshold = NULL,
  avg_method = c("mean", "median"),
  spaghetti = FALSE,
  heatmap = TRUE,
  heatmap_subsample = 50,
  smoothing_window = 2000,
  gene_anno = TRUE,
  palette = ggplot2::scale_colour_brewer(palette = "Set1"),
  line_size = 1,
  mod_scale = c(0, 1),
  span = NULL
)
plot_gene(x, gene, ...)

## S4 method for signature 'NanoMethResult,character'
plot_gene(
  x,
  gene,
  window_prop = 0.3,
  anno_regions = NULL,
  binary_threshold = NULL,
  avg_method = c("mean", "median"),
  spaghetti = FALSE,
  heatmap = TRUE,
  heatmap_subsample = 50,
  smoothing_window = 2000,
  gene_anno = TRUE,
  palette = ggplot2::scale_colour_brewer(palette = "Set1"),
  line_size = 1,
  mod_scale = c(0, 1),
  span = NULL
)

## S4 method for signature 'ModBamResult,character'
plot_gene(
  x,
  gene,
  window_prop = 0.3,
  anno_regions = NULL,
  binary_threshold = NULL,
  avg_method = c("mean", "median"),
  spaghetti = FALSE,
  heatmap = TRUE,
  heatmap_subsample = 50,
  smoothing_window = 2000,
  gene_anno = TRUE,
  palette = ggplot2::scale_colour_brewer(palette = "Set1"),
  line_size = 1,
  mod_scale = c(0, 1),
  span = NULL
)

Arguments

`x`	the NanoMethResult or ModBamResult object.
`gene`	the gene symbol for the gene to plot.
`...`	additional arguments.
`window_prop`	the size of flanking region to plot. Can be a vector of two values for left and right window size. Values indicate proportion of gene length.
`anno_regions`	the data.frame of regions to be annotated.
`binary_threshold`	the modification probability such that calls with modification probability above the threshold are set to 1 and probabilities equal to or below the threshold are set to 0.
`avg_method`	the average method for pre-smoothing at each genomic position. Data is pre-smoothed at each genomic position before the smoothed aggregate line is generated for performance reasons. The default is "mean" which corresponds to the average methylation fraction. The alternative "median" option is closer to an average within the more common methylation state.
`spaghetti`	whether or not individual reads should be shown.
`heatmap`	whether or not read-methylation heatmap should be shown.
`heatmap_subsample`	how many packed rows of reads to subsample to.
`smoothing_window`	the window size for smoothing the trend line.
`gene_anno`	whether to show gene annotation.
`palette`	the ggplot colour palette used for groups.
`line_size`	the size of the lines.
`mod_scale`	the scale range for modification probabilities. Default c(0, 1), set to "auto" for automatic limits.
`span`	DEPRECATED, use smoothing_window instead. Will be removed in next version.

Details

This function plots the methylation data for a given gene. Since V3.0.0 NanoMethViz has changed the smoothing strategy from a loess smoothing to a weighted moving average. This is because the loess smoothing was too computationally expensive for large datasets and had a span parameter that was difficult to tune. The new smoothing strategy is controlled by the smoothing_window argument.

Value

a patchwork plot containing the methylation profile in the specified region.

Functions

plot_gene(x = ModBamResult, gene = character): S4 method for ModBamResult

Examples

nmr <- load_example_nanomethresult()
plot_gene(nmr, "Peg3")

nmr <- load_example_nanomethresult()
plot_gene(nmr, "Peg3")

Plot gene methylation heatmap

Description

Plot the methylation heatmap of a gene symbol specified within the exon(x) slot.

Usage

plot_gene_heatmap(x, gene, ...)

## S4 method for signature 'NanoMethResult,character'
plot_gene_heatmap(
  x,
  gene,
  window_prop = 0.3,
  pos_style = c("to_scale", "compact"),
  subsample = 50
)

## S4 method for signature 'ModBamResult,character'
plot_gene_heatmap(
  x,
  gene,
  window_prop = 0.3,
  pos_style = c("to_scale", "compact"),
  subsample = 50
)
plot_gene_heatmap(x, gene, ...)

## S4 method for signature 'NanoMethResult,character'
plot_gene_heatmap(
  x,
  gene,
  window_prop = 0.3,
  pos_style = c("to_scale", "compact"),
  subsample = 50
)

## S4 method for signature 'ModBamResult,character'
plot_gene_heatmap(
  x,
  gene,
  window_prop = 0.3,
  pos_style = c("to_scale", "compact"),
  subsample = 50
)

Arguments

`x`	the NanoMethResult or ModBamResult object.
`gene`	the gene symbol for the gene to plot.
`...`	additional arguments.
`window_prop`	the size of flanking region to plot. Can be a vector of two values for left and right window size. Values indicate proportion of gene length.
`pos_style`	the style for plotting the base positions along the x-axis. Defaults to "to_scale", plotting (potentially) overlapping squares along the genomic position to scale. The "compact" options plots only the positions with measured modification.
`subsample`	the number of read of packed read rows to subsample to.

Value

a ggplot object of the heatmap

a ggplot plot containing the heatmap.

Examples

nmr <- load_example_nanomethresult()
plot_gene_heatmap(nmr, "Peg3")

nmr <- load_example_nanomethresult()
plot_gene_heatmap(nmr, "Peg3")

Plot GRanges

Description

Plot GRanges

Usage

plot_grange(
  x,
  grange,
  anno_regions = NULL,
  binary_threshold = NULL,
  avg_method = c("mean", "median"),
  spaghetti = FALSE,
  heatmap = TRUE,
  heatmap_subsample = 50,
  gene_anno = TRUE,
  smoothing_window = 2000,
  window_prop = 0,
  palette = ggplot2::scale_colour_brewer(palette = "Set1"),
  line_size = 1,
  span = NULL
)
plot_grange(
  x,
  grange,
  anno_regions = NULL,
  binary_threshold = NULL,
  avg_method = c("mean", "median"),
  spaghetti = FALSE,
  heatmap = TRUE,
  heatmap_subsample = 50,
  gene_anno = TRUE,
  smoothing_window = 2000,
  window_prop = 0,
  palette = ggplot2::scale_colour_brewer(palette = "Set1"),
  line_size = 1,
  span = NULL
)

Arguments

`x`	the NanoMethResult object.
`grange`	the GRanges object with one entry.
`anno_regions`	the data.frame of regions to be annotated.
`binary_threshold`	the modification probability such that calls with modification probability above the threshold are set to 1 and probabilities equal to or below the threshold are set to 0.
`avg_method`	the average method for pre-smoothing at each genomic position. Data is pre-smoothed at each genomic position before the smoothed aggregate line is generated for performance reasons. The default is "mean" which corresponds to the average methylation fraction. The alternative "median" option is closer to an average within the more common methylation state.
`spaghetti`	whether or not individual reads should be shown.
`heatmap`	whether or not read-methylation heatmap should be shown.
`heatmap_subsample`	how many packed rows of reads to subsample to.
`gene_anno`	whether to show gene annotation.
`smoothing_window`	the window size for smoothing the trend line.
`window_prop`	the size of flanking region to plot. Can be a vector of two values for left and right window size. Values indicate proportion of gene length.
`palette`	the ggplot colour palette used for groups.
`line_size`	the size of the lines.
`span`	DEPRECATED, use smoothing_window instead. Will be removed in next version.

Value

a patchwork plot containing the methylation profile in the specified region.

Examples

nmr <- load_example_nanomethresult()
plot_grange(nmr, GenomicRanges::GRanges("chr7:6703892-6730431"))

nmr <- load_example_nanomethresult()
plot_grange(nmr, GenomicRanges::GRanges("chr7:6703892-6730431"))

Plot GRanges heatmap

Description

Plot GRanges heatmap

Usage

plot_grange_heatmap(
  x,
  grange,
  pos_style = c("to_scale", "compact"),
  window_prop = 0,
  subsample = 50
)
plot_grange_heatmap(
  x,
  grange,
  pos_style = c("to_scale", "compact"),
  window_prop = 0,
  subsample = 50
)

Arguments

`x`	the NanoMethResult object.
`grange`	the GRanges object with one entry.
`pos_style`	the style for plotting the base positions along the x-axis. Defaults to "to_scale", plotting (potentially) overlapping squares along the genomic position to scale. The "compact" options plots only the positions with measured modification.
`window_prop`	the size of flanking region to plot. Can be a vector of two values for left and right window size. Values indicate proportion of gene length.
`subsample`	the number of read of packed read rows to subsample to.

Value

a ggplot plot containing the heatmap.

Examples

nmr <- load_example_nanomethresult()
plot_grange_heatmap(nmr, GenomicRanges::GRanges("chr7:6703892-6730431"))

nmr <- load_example_nanomethresult()
plot_grange_heatmap(nmr, GenomicRanges::GRanges("chr7:6703892-6730431"))

Plot MDS

Description

Plot multi-dimensional scaling plot using algorithm of limma::plotMDS(). It is recommended this be done with the log-methylation-ratio matrix generated by bsseq_to_log_methy_ratio().

Usage

plot_mds(
  x,
  top = 500,
  plot_dims = c(1, 2),
  labels = colnames(x),
  groups = NULL,
  legend_name = "group"
)
plot_mds(
  x,
  top = 500,
  plot_dims = c(1, 2),
  labels = colnames(x),
  groups = NULL,
  legend_name = "group"
)

Arguments

`x`	the log-methylation-ratio matrix.
`top`	the number of top genes used to calculate pairwise distances.
`plot_dims`	the numeric vector of the two dimensions to be plotted.
`labels`	the character vector of labels for data points. By default uses column names of x, set to NULL to plot points.
`groups`	the character vector of groups the data points will be coloured by. Colour palette can be adjusted using scale_colour_*() functions from ggplot2. If groups is numeric, the points will be coloured by a continuous colour palette. By default, groups is NULL and the points will not be coloured.
`legend_name`	the name for the legend.

Value

ggplot object of the MDS plot.

Examples

nmr <- load_example_nanomethresult()
bss <- methy_to_bsseq(nmr)
lmr <- bsseq_to_log_methy_ratio(bss)
plot_mds(lmr)

nmr <- load_example_nanomethresult()
bss <- methy_to_bsseq(nmr)
lmr <- bsseq_to_log_methy_ratio(bss)
plot_mds(lmr)

Plot PCA

Description

Plot multi-dimensional scaling plot using algorithm of BiocSingular::runPCA(). It is recommended this be done with the log-methylation-ratio matrix generated by bsseq_to_log_methy_ratio().

Usage

plot_pca(
  x,
  plot_dims = c(1, 2),
  labels = colnames(x),
  groups = NULL,
  legend_name = "group"
)
plot_pca(
  x,
  plot_dims = c(1, 2),
  labels = colnames(x),
  groups = NULL,
  legend_name = "group"
)

Arguments

`x`	the log-methylation-ratio matrix.
`plot_dims`	the numeric vector of the two dimensions to be plotted.
`labels`	the character vector of labels for data points. By default uses column names of x, set to NULL to plot points.
`groups`	the character vector of groups the data points will be coloured by.
`legend_name`	the name for the legend.

Value

ggplot object of the MDS plot.

Examples

nmr <- load_example_nanomethresult()
bss <- methy_to_bsseq(nmr)
lmr <- bsseq_to_log_methy_ratio(bss)
plot_pca(lmr)

nmr <- load_example_nanomethresult()
bss <- methy_to_bsseq(nmr)
lmr <- bsseq_to_log_methy_ratio(bss)
plot_pca(lmr)

Plot region methylation

Description

Plot the methylation of a genomic region.

Usage

plot_region(x, chr, start, end, ...)

## S4 method for signature 'NanoMethResult,character,numeric,numeric'
plot_region(
  x,
  chr,
  start,
  end,
  anno_regions = NULL,
  binary_threshold = NULL,
  avg_method = c("mean", "median"),
  spaghetti = FALSE,
  heatmap = TRUE,
  heatmap_subsample = 50,
  smoothing_window = 2000,
  gene_anno = TRUE,
  window_prop = 0,
  palette = ggplot2::scale_colour_brewer(palette = "Set1"),
  line_size = 1,
  mod_scale = c(0, 1),
  span = NULL
)

## S4 method for signature 'ModBamResult,character,numeric,numeric'
plot_region(
  x,
  chr,
  start,
  end,
  anno_regions = NULL,
  binary_threshold = NULL,
  avg_method = c("mean", "median"),
  spaghetti = FALSE,
  heatmap = TRUE,
  heatmap_subsample = 50,
  smoothing_window = 2000,
  gene_anno = TRUE,
  window_prop = 0,
  palette = ggplot2::scale_colour_brewer(palette = "Set1"),
  line_size = 1,
  mod_scale = c(0, 1),
  span = NULL
)

## S4 method for signature 'NanoMethResult,factor,numeric,numeric'
plot_region(
  x,
  chr,
  start,
  end,
  anno_regions = NULL,
  binary_threshold = NULL,
  avg_method = c("mean", "median"),
  spaghetti = FALSE,
  heatmap = TRUE,
  heatmap_subsample = 50,
  smoothing_window = 2000,
  gene_anno = TRUE,
  window_prop = 0,
  palette = ggplot2::scale_colour_brewer(palette = "Set1"),
  line_size = 1,
  mod_scale = c(0, 1),
  span = NULL
)

## S4 method for signature 'ModBamResult,factor,numeric,numeric'
plot_region(
  x,
  chr,
  start,
  end,
  anno_regions = NULL,
  binary_threshold = NULL,
  avg_method = c("mean", "median"),
  spaghetti = FALSE,
  heatmap = TRUE,
  heatmap_subsample = 50,
  smoothing_window = 2000,
  gene_anno = TRUE,
  window_prop = 0,
  palette = ggplot2::scale_colour_brewer(palette = "Set1"),
  line_size = 1,
  mod_scale = c(0, 1),
  span = NULL
)
plot_region(x, chr, start, end, ...)

## S4 method for signature 'NanoMethResult,character,numeric,numeric'
plot_region(
  x,
  chr,
  start,
  end,
  anno_regions = NULL,
  binary_threshold = NULL,
  avg_method = c("mean", "median"),
  spaghetti = FALSE,
  heatmap = TRUE,
  heatmap_subsample = 50,
  smoothing_window = 2000,
  gene_anno = TRUE,
  window_prop = 0,
  palette = ggplot2::scale_colour_brewer(palette = "Set1"),
  line_size = 1,
  mod_scale = c(0, 1),
  span = NULL
)

## S4 method for signature 'ModBamResult,character,numeric,numeric'
plot_region(
  x,
  chr,
  start,
  end,
  anno_regions = NULL,
  binary_threshold = NULL,
  avg_method = c("mean", "median"),
  spaghetti = FALSE,
  heatmap = TRUE,
  heatmap_subsample = 50,
  smoothing_window = 2000,
  gene_anno = TRUE,
  window_prop = 0,
  palette = ggplot2::scale_colour_brewer(palette = "Set1"),
  line_size = 1,
  mod_scale = c(0, 1),
  span = NULL
)

## S4 method for signature 'NanoMethResult,factor,numeric,numeric'
plot_region(
  x,
  chr,
  start,
  end,
  anno_regions = NULL,
  binary_threshold = NULL,
  avg_method = c("mean", "median"),
  spaghetti = FALSE,
  heatmap = TRUE,
  heatmap_subsample = 50,
  smoothing_window = 2000,
  gene_anno = TRUE,
  window_prop = 0,
  palette = ggplot2::scale_colour_brewer(palette = "Set1"),
  line_size = 1,
  mod_scale = c(0, 1),
  span = NULL
)

## S4 method for signature 'ModBamResult,factor,numeric,numeric'
plot_region(
  x,
  chr,
  start,
  end,
  anno_regions = NULL,
  binary_threshold = NULL,
  avg_method = c("mean", "median"),
  spaghetti = FALSE,
  heatmap = TRUE,
  heatmap_subsample = 50,
  smoothing_window = 2000,
  gene_anno = TRUE,
  window_prop = 0,
  palette = ggplot2::scale_colour_brewer(palette = "Set1"),
  line_size = 1,
  mod_scale = c(0, 1),
  span = NULL
)

Arguments

`x`	the NanoMethResult or ModBamResult object.
`chr`	the chromosome to plot.
`start`	the start of the plotting region.
`end`	the end of the plotting region.
`...`	additional arguments.
`anno_regions`	the data.frame of regions to be annotated.
`binary_threshold`	the modification probability such that calls with modification probability above the threshold are set to 1 and probabilities equal to or below the threshold are set to 0.
`avg_method`	the average method for pre-smoothing at each genomic position. Data is pre-smoothed at each genomic position before the smoothed aggregate line is generated for performance reasons. The default is "mean" which corresponds to the average methylation fraction. The alternative "median" option is closer to an average within the more common methylation state.
`spaghetti`	whether or not individual reads should be shown.
`heatmap`	whether or not read-methylation heatmap should be shown.
`heatmap_subsample`	how many packed rows of reads to subsample to.
`smoothing_window`	the window size for smoothing the trend line.
`gene_anno`	whether to show gene annotation.
`window_prop`	the size of flanking region to plot. Can be a vector of two values for left and right window size. Values indicate proportion of gene length.
`palette`	the ggplot colour palette used for groups.
`line_size`	the size of the lines.
`mod_scale`	the scale range for modification probabilities. Default c(0, 1), set to "auto" for automatic limits.
`span`	DEPRECATED, use smoothing_window instead. Will be removed in next version.

Details

This function plots the methylation data for a given region. The region is specified by chromosome, start and end positions. The basic plot contains a smoothed line plot of the methylation of each experimental group. Since V3.0.0 NanoMethViz has changed the smoothing strategy from a loess smoothing to a weighted moving average. This is because the loess smoothing was too computationally expensive for large datasets and had a span parameter that was difficult to tune. The new smoothing strategy is controlled by the smoothing_window argument.

Value

a patchwork plot containing the methylation profile in the specified region.

Examples

nmr <- load_example_nanomethresult()
plot_region(nmr, "chr7", 6703892, 6730431)

nmr <- load_example_nanomethresult()
plot_region(nmr, "chr7", 6703892, 6730431)

Plot region methylation heatmap

Description

Plot the methylation heatmap of a genomic region.

Usage

plot_region_heatmap(x, chr, start, end, ...)

## S4 method for signature 'NanoMethResult,character,numeric,numeric'
plot_region_heatmap(
  x,
  chr,
  start,
  end,
  pos_style = c("to_scale", "compact"),
  window_prop = 0,
  subsample = 50
)

## S4 method for signature 'ModBamResult,character,numeric,numeric'
plot_region_heatmap(
  x,
  chr,
  start,
  end,
  pos_style = c("to_scale", "compact"),
  window_prop = 0,
  subsample = 50
)

## S4 method for signature 'NanoMethResult,factor,numeric,numeric'
plot_region_heatmap(
  x,
  chr,
  start,
  end,
  pos_style = c("to_scale", "compact"),
  window_prop = 0,
  subsample = 50
)

## S4 method for signature 'ModBamResult,factor,numeric,numeric'
plot_region_heatmap(
  x,
  chr,
  start,
  end,
  pos_style = c("to_scale", "compact"),
  window_prop = 0,
  subsample = 50
)
plot_region_heatmap(x, chr, start, end, ...)

## S4 method for signature 'NanoMethResult,character,numeric,numeric'
plot_region_heatmap(
  x,
  chr,
  start,
  end,
  pos_style = c("to_scale", "compact"),
  window_prop = 0,
  subsample = 50
)

## S4 method for signature 'ModBamResult,character,numeric,numeric'
plot_region_heatmap(
  x,
  chr,
  start,
  end,
  pos_style = c("to_scale", "compact"),
  window_prop = 0,
  subsample = 50
)

## S4 method for signature 'NanoMethResult,factor,numeric,numeric'
plot_region_heatmap(
  x,
  chr,
  start,
  end,
  pos_style = c("to_scale", "compact"),
  window_prop = 0,
  subsample = 50
)

## S4 method for signature 'ModBamResult,factor,numeric,numeric'
plot_region_heatmap(
  x,
  chr,
  start,
  end,
  pos_style = c("to_scale", "compact"),
  window_prop = 0,
  subsample = 50
)

Arguments

`x`	the NanoMethResult or ModBamResult object.
`chr`	the chromosome to plot.
`start`	the start of the plotting region.
`end`	the end of the plotting region.
`...`	additional arguments.
`pos_style`	the style for plotting the base positions along the x-axis. Defaults to "to_scale", plotting (potentially) overlapping squares along the genomic position to scale. The "compact" options plots only the positions with measured modification.
`window_prop`	the size of flanking region to plot. Can be a vector of two values for left and right window size. Values indicate proportion of gene length.
`subsample`	the number of read of packed read rows to subsample to.

Value

a ggplot object of the heatmap.

a ggplot plot containing the heatmap.

Examples

nmr <- load_example_nanomethresult()
plot_region_heatmap(nmr, "chr7", 6703892, 6730431)

nmr <- load_example_nanomethresult()
plot_region_heatmap(nmr, "chr7", 6703892, 6730431)

Plot violin for regions

Description

This function plots a violin plot of the methylation proportion for each region in the regions table. The methylation proportion is calculated as the mean of the modification probability wihtin each region and the violin represents the . The regions are then grouped and coloured by the group_col column in the regions table or samples(x).

Usage

plot_violin(
  x,
  regions,
  binary_threshold = 0.5,
  group_col = "group",
  palette = ggplot2::scale_colour_brewer(palette = "Set1")
)
plot_violin(
  x,
  regions,
  binary_threshold = 0.5,
  group_col = "group",
  palette = ggplot2::scale_colour_brewer(palette = "Set1")
)

Arguments

`x`	the NanoMethResult object.
`regions`	a table of regions containing at least columns chr, strand, start and end. Any additional columns can be used for grouping.
`binary_threshold`	the modification probability such that calls with modification probability above the threshold are considered methylated, and those with probability equal or below are considered unmethylated.
`group_col`	the column to group aggregated trends by. This column can be in from the regions table or samples(x).
`palette`	the ggplot colour palette used for groups.

Value

a ggplot object containing the methylation violin plot.

Examples

nmr <- load_example_nanomethresult()
gene_anno <- exons_to_genes(NanoMethViz::exons(nmr))
plot_violin(nmr, gene_anno)
plot_violin(nmr, gene_anno, group_col = "sample")

nmr <- load_example_nanomethresult()
gene_anno <- exons_to_genes(NanoMethViz::exons(nmr))
plot_violin(nmr, gene_anno)
plot_violin(nmr, gene_anno, group_col = "sample")

Query exons

Description

Query a data.frame, NanoMethResult or ModBamResult for exon annotation.

Usage

query_exons_region(x, chr, start, end)

query_exons_gene_id(x, gene_id)

query_exons_symbol(x, symbol)
query_exons_region(x, chr, start, end)

query_exons_gene_id(x, gene_id)

query_exons_symbol(x, symbol)

Arguments

`x`	the object to query.
`chr`	the chromosome to query.
`start`	the start of the query region.
`end`	the end of the query region.
`gene_id`	the gene_id to query.
`symbol`	the gene_id to query.

Value

data.frame of queried exons.

Functions

query_exons_region(): Query region.
query_exons_gene_id(): Query gene ID.
query_exons_symbol(): Query gene symbol.

Query methylation data

Description

Query methylation data

Usage

query_methy(
  x,
  chr,
  start,
  end,
  simplify = TRUE,
  force = FALSE,
  truncate = TRUE,
  site_filter = getOption("NanoMethViz.site_filter", 3L)
)
query_methy(
  x,
  chr,
  start,
  end,
  simplify = TRUE,
  force = FALSE,
  truncate = TRUE,
  site_filter = getOption("NanoMethViz.site_filter", 3L)
)

Arguments

`x`	the NanoMethResults object or a path to the methylation data (tabix-bgzipped).
`chr`	the vector of chromosomes
`start`	the vector of start positions
`end`	the vector of end positions
`simplify`	whether returned results should be row-concatenated
`force`	whether to force empty output when query region 'chr' does not appear in data. Without 'force', an empty result indicates that the requested 'chr' appears in the data but no data overlaps with requested region, and an invalid 'chr' will cause an error.
`truncate`	when querying from ModBamFiles, whether or not to truncate returned results to only those within the specified region. Otherwise methylation data for entire reads overlapping the region will be returned.
`site_filter`	the minimum amount of coverage to report a site. This filters the queried data such that any site with less than the filter is not returned. The default is 1, which means that all sites are returned. This option can be set globally using the `options(site_filter = ...)` which will affect all plotting functions in NanoMethviz.

Details

The argument site_filter can be set globally using the options(site_filter = ...) command. The same data entry may appear multiple times in the output if it overlaps multiple regions.

Value

A table containing the data within the queried regions. If simplify is TRUE (default) then returns all data in a single table, otherwise returns a list of tables where each table is the data from one region.

Examples

nmr <- load_example_nanomethresult()
query_methy(methy(nmr), "chr7", 6703892, 6730431)

nmr <- load_example_nanomethresult()
query_methy(methy(nmr), "chr7", 6703892, 6730431)

Calculate region methylation statistics

Description

Calculate the average methylation probability and prevalence based on specified probability threshold.

Usage

region_methy_stats(nmr, regions, threshold = 0.5)
region_methy_stats(nmr, regions, threshold = 0.5)

Arguments

`nmr`	the NanoMethResult object.
`regions`	the table of regions to query statistics for.
`threshold`	the threshold to use for determining methylation calls for the calculation of prevalence.

Value

table of regions with additional columns of methylation summary statistics.

Examples

nmr <- load_example_nanomethresult()
gene_anno <- exons_to_genes(NanoMethViz::exons(nmr))
region_methy_stats(nmr, gene_anno)
nmr <- load_example_nanomethresult()
gene_anno <- exons_to_genes(NanoMethViz::exons(nmr))
region_methy_stats(nmr, gene_anno)

Package 'NanoMethViz'

Help Index

Convert BSseq object to edgeR methylation matrix

Description

Usage

Arguments

Value

Examples

Convert BSseq object to log-methylation-ratio matrix

Description

Usage

Arguments

Value

Examples

Cluster reads based on methylation

Description

Usage

Arguments

Value

Cluster regions by K-means

Description

Usage

Arguments

Value

Examples

Create a tabix file using methylation calls

Description

Usage

Arguments

Value

Examples

Convert exon annotation to genes

Description

Usage

Arguments

Value

Examples

Create filtered methylation file

Description

Usage

Arguments

Value

Examples

Get exon annotations

Description

Usage

Value

Examples

Get example exon annotations for mus musculus (mm10)

Description

Usage

Value

Examples

Get exon annotations for Homo sapiens (hg19)

Description

Usage

Value

Examples

Get exon annotations for Mus musculus (mm10)

Description

Usage

Value

Examples

Load an example ModBamResult object

Description

Usage

Value

Examples

Load an example NanoMethResult object

Description

Usage

Value

Examples

Column names for methylation data

Description

Usage

Value

Examples

Create BSSeq object from methylation tabix file

Description