Package 'methrix' reference manual

Title:	Fast and efficient summarization of generic bedGraph files from Bisufite sequencing
Description:	Bedgraph files generated by Bisulfite pipelines often come in various flavors. Critical downstream step requires summarization of these files into methylation/coverage matrices. This step of data aggregation is done by Methrix, including many other useful downstream functions.
Authors:	Anand Mayakonda [aut, cre] , Reka Toth [aut] , Rajbir Batra [ctb], Clarissa Feuerstein-Akgöz [ctb], Joschka Hey [ctb], Maximilian Schönung [ctb], Pavlo Lutsik [ctb]
Maintainer:	Anand Mayakonda <[email protected]>
License:	MIT + file LICENSE
Version:	1.19.0
Built:	2024-06-30 06:20:13 UTC
Source:	https://github.com/bioc/methrix

Combine methrix objects

Description

Combine methrix objects

Usage

combine_methrix(m1, m2, by = c("row", "col"))
combine_methrix(m1, m2, by = c("row", "col"))

Arguments

`m1`	Frist `methrix` object
`m2`	Second `methrix` object
`by`	The direction of combine. 'column' (cbind) combines samples with same regions, 'row' combines different regions, e.g. different chromosomes.

Details

Takes two methrix objects and combines them row- or column-wise

Value

An object of class methrix

Converts HDF5 methrix object to standard in-memory object.

Description

Converts HDF5 methrix object to standard in-memory object.

Usage

convert_HDF5_methrix(m = NULL)
convert_HDF5_methrix(m = NULL)

Arguments

`m`	An object of class `methrix`, HDF5 format

Details

Takes a methrix object and returns with the same object with in-memory assay slots.

Value

An object of class methrix

Examples

data(methrix_data)
m2 <- convert_methrix(m=methrix_data)
m <- convert_HDF5_methrix(m=m2)
data(methrix_data)
m2 <- convert_methrix(m=methrix_data)
m <- convert_HDF5_methrix(m=m2)

Converts an in-memory object to an on-disk HDF5 object.

Description

Converts an in-memory object to an on-disk HDF5 object.

Usage

convert_methrix(m = NULL)
convert_methrix(m = NULL)

Arguments

`m`	An object of class `methrix`

Details

Takes a methrix object and returns with the same object with delayed array assay slots with HDF5 backend. Might take long time!

Value

An object of class methrix, HDF5 format

Examples

data(methrix_data)
m2 <- convert_methrix(m=methrix_data)
data(methrix_data)
m2 <- convert_methrix(m=methrix_data)

Filter matrices by coverage

Description

Filter matrices by coverage

Usage

coverage_filter(
  m,
  cov_thr = 1,
  min_samples = 1,
  prop_samples = 0,
  group = NULL,
  n_chunks = 1,
  n_cores = 1
)
coverage_filter(
  m,
  cov_thr = 1,
  min_samples = 1,
  prop_samples = 0,
  group = NULL,
  n_chunks = 1,
  n_cores = 1
)

Arguments

`m`	`methrix` object
`cov_thr`	minimum coverage required to call a loci covered
`min_samples`	Minimum number of samples that should have a loci with coverage >= `cov_thr`. If `group` is given, then this applies per group. Only need one of `prop_samples` or `min_samples`.
`prop_samples`	Minimum proportion of samples that should have a loci with coverage >= `cov_thr`. If `group` is given, then this applies per group. Only need one of `prop_samples` or `min_samples`.
`group`	a column name from sample annotation that defines groups. In this case, the number of min_samples will be tested group-wise.
`n_chunks`	Number of chunks to split the `methrix` object in case it is very large. Default = 1.
`n_cores`	Number of parallel instances. `n_cores` should be less than or equal to `n_chunks`. If `n_chunks` is not specified, then `n_chunks` is initialized to be equal to `n_cores`. Default = 1.

Details

Takes methrix object and filters CpGs based on coverage statistics

Value

An object of class methrix

Examples

data('methrix_data')
#keep only CpGs which are covered by at-least 1 read across 3 samples
coverage_filter(m = methrix_data, cov_thr = 1, min_samples = 3)
data('methrix_data')
#keep only CpGs which are covered by at-least 1 read across 3 samples
coverage_filter(m = methrix_data, cov_thr = 1, min_samples = 3)

Extracts all CpGs from a genome

Description

Extracts all CpGs from a genome

Usage

extract_CPGs(ref_genome = NULL)
extract_CPGs(ref_genome = NULL)

Arguments

ref_genome

BSgenome object or name of the installed BSgenome package. Example: BSgenome.Hsapiens.UCSC.hg19

Value

a list of data.table containing number of CpG's and contig lengths

Examples

## Not run: 
hg19_cpgs = methrix::extract_CPGs(ref_genome = 'BSgenome.Hsapiens.UCSC.hg19')

## End(Not run)
## Not run: 
hg19_cpgs = methrix::extract_CPGs(ref_genome = 'BSgenome.Hsapiens.UCSC.hg19')

## End(Not run)

Extract methylation or coverage matrices

Description

Extract methylation or coverage matrices

Usage

get_matrix(m, type = "M", add_loci = FALSE, in_granges = FALSE)
get_matrix(m, type = "M", add_loci = FALSE, in_granges = FALSE)

Arguments

`m`	`methrix` object
`type`	can be `M` or `C`. Default 'M'
`add_loci`	Default FALSE. If TRUE adds CpG position info to the matrix and returns as a data.table
`in_granges`	Do you want the outcome in `GRanges`?

Details

Takes methrix object and returns user specified methylation or coverage matrix

Value

Coverage or Methylation matrix

Examples

data('methrix_data')
#Get methylation matrix
get_matrix(m = methrix_data, type = 'M')
#Get methylation matrix along with loci
get_matrix(m = methrix_data, type = 'M', add_loci = TRUE)
#' #Get methylation data as a GRanges object
get_matrix(m = methrix_data, type = 'M', add_loci = TRUE, in_granges=TRUE)
data('methrix_data')
#Get methylation matrix
get_matrix(m = methrix_data, type = 'M')
#Get methylation matrix along with loci
get_matrix(m = methrix_data, type = 'M', add_loci = TRUE)
#' #Get methylation data as a GRanges object
get_matrix(m = methrix_data, type = 'M', add_loci = TRUE, in_granges=TRUE)

Extract and summarize methylation or coverage info by regions of interest

Description

Extract and summarize methylation or coverage info by regions of interest

Usage

get_region_summary(
  m,
  regions = NULL,
  type = "M",
  how = "mean",
  overlap_type = "within",
  na_rm = TRUE,
  elementMetadata.col = NULL,
  verbose = TRUE,
  n_chunks = 1,
  n_cores = 1
)
get_region_summary(
  m,
  regions = NULL,
  type = "M",
  how = "mean",
  overlap_type = "within",
  na_rm = TRUE,
  elementMetadata.col = NULL,
  verbose = TRUE,
  n_chunks = 1,
  n_cores = 1
)

Arguments

`m`	`methrix` object
`regions`	genomic regions to be summarized. Could be a data.table with 3 columns (chr, start, end) or a `GenomicRanges` object
`type`	matrix which needs to be summarized. Coule be ‘M', 'C'. Default ’M'
`how`	mathematical function by which regions should be summarized. Can be one of the following: mean, sum, max, min. Default 'mean'
`overlap_type`	defines the type of the overlap of the CpG sites with the target region. Default value is 'within'. For detailed description, see the `findOverlaps` function of the `IRanges` package.
`na_rm`	Remove NA's? Default `TRUE`
`elementMetadata.col`	columns in `rowData(methrix)` which needs to be summarised. Default = NULL.
`verbose`	Default TRUE
`n_chunks`	Number of chunks to split the `methrix` object in case it is very large. Default = 1.
`n_cores`	Number of parallel instances. `n_cores` should be less than or equal to `n_chunks`. If `n_chunks` is not specified, then `n_chunks` is initialized to be equal to `n_cores`. Default = 1.

Details

Takes methrix object and summarizes regions

Value

a coverage or methylation matrix

Examples

data('methrix_data')
get_region_summary(m = methrix_data,
regions = data.table(chr = 'chr21', start = 27867971, end =  27868103),
type = 'M', how = 'mean')
data('methrix_data')
get_region_summary(m = methrix_data,
regions = data.table(chr = 'chr21', start = 27867971, end =  27868103),
type = 'M', how = 'mean')

Estimate descriptive statistics

Description

Estimate descriptive statistics

Usage

get_stats(m, per_chr = TRUE)
get_stats(m, per_chr = TRUE)

Arguments

`m`	`methrix` object
`per_chr`	Estimate stats per chromosome. Default TRUE

Details

Calculate descriptive statistics

Value

data.table of summary stats

Examples

data('methrix_data')
get_stats(methrix_data)
data('methrix_data')
get_stats(methrix_data)

Loads HDF5 methrix object

Description

Loads HDF5 methrix object

Usage

load_HDF5_methrix(dir = NULL, ...)
load_HDF5_methrix(dir = NULL, ...)

Arguments

`dir`	The directory to read in from. Default NULL
`...`	Parameters to pass to loadHDF5SummarizedExperiment

Details

Takes directory with a previously saved HDF5Array format methrix object and loads it

Value

An object of class methrix

Examples

data('methrix_data')
methrix_data_h5 <- convert_methrix(m=methrix_data)
target_dir = paste0(getwd(), '/temp1/')
save_HDF5_methrix(methrix_data_h5, dir = target_dir, replace = TRUE)
load_HDF5_methrix(target_dir)
data('methrix_data')
methrix_data_h5 <- convert_methrix(m=methrix_data)
target_dir = paste0(getwd(), '/temp1/')
save_HDF5_methrix(methrix_data_h5, dir = target_dir, replace = TRUE)
load_HDF5_methrix(target_dir)

Masks too high or too low coverage

Description

Masks too high or too low coverage

Usage

mask_methrix(m, low_count = NULL, high_quantile = 0.99, n_cores = 1)
mask_methrix(m, low_count = NULL, high_quantile = 0.99, n_cores = 1)

Arguments

`m`	`methrix` object
`low_count`	The minimal coverage allowed. Everything below, will get masked. Default = NULL, nothing gets masked.
`high_quantile`	The quantile limit of coverage. Quantiles are calculated for each sample and everything that belongs to a higher quantile than the defined will be masked. Default = 0.99.
`n_cores`	Number of parallel instances. Can only be used if `methrix` is in HDF5 format. Default = 1.

Details

Takes methrix object and masks sites with too high or too low coverage by putting NA for coverage and beta value. The sites will remain in the object.

Value

An object of class methrix

Examples

data('methrix_data')
mask_methrix(m = methrix_data, low_count = 5, high_quantile = 0.99 )
data('methrix_data')
mask_methrix(m = methrix_data, low_count = 5, high_quantile = 0.99 )

WGBS for colon cancer, chr21 and chr22

Description

This is a subset of original 'bsseqData' converted to 'methrix' containing Whole-genome bisulfite sequencing data (WGBS) for colon cancer on chromosome 21 and 22.

Usage

data('methrix_data')
data('methrix_data')

Format

An object of class 'methrix'

References

Hansen, K. D. et al. (2011) Increased methylation variation in epigenetic domains across cancer types. Nature Genetics 43, 768-775.

Examples

data('methrix_data')
methrix_data
data('methrix_data')
methrix_data

Principal Component Analysis

Description

Principal Component Analysis

Usage

methrix_pca(
  m,
  var = "top",
  top_var = 1000,
  ranges = NULL,
  pheno = NULL,
  do_plot = TRUE,
  n_pc = 2
)
methrix_pca(
  m,
  var = "top",
  top_var = 1000,
  ranges = NULL,
  pheno = NULL,
  do_plot = TRUE,
  n_pc = 2
)

Arguments

`m`	Input `methrix` object
`var`	Choose between random CpG sites ('rand') or most variable CpGs ('top').
`top_var`	Number of variable CpGs to use. Default 1000 Set it to NULL to use all CpGs (which is not recommended due to memory requirements). This option is mutually exclusive with `ranges`.
`ranges`	genomic regions to be summarized. Could be a data.table with 3 columns (chr, start, end) or a `GenomicRanges` object
`pheno`	Column name of colData(m). Default NULL. Will be used as a factor to color different groups
`do_plot`	Should a plot be generated?
`n_pc`	Default 2.

Value

PCA results

Examples

data('methrix_data')
methrix_pca(methrix_data, do_plot = FALSE)
data('methrix_data')
methrix_pca(methrix_data, do_plot = FALSE)

Creates a detailed interative html summary report from Methrix object

Description

Creates a detailed interative html summary report from Methrix object. If the directory contains required files (from previous run), it directly proceeds to generate html report.

Usage

methrix_report(
  meth,
  output_dir = NULL,
  recal_stats = FALSE,
  plot_beta_dist = TRUE,
  beta_nCpG = 10000,
  prefix = NULL,
  n_thr = 4
)
methrix_report(
  meth,
  output_dir = NULL,
  recal_stats = FALSE,
  plot_beta_dist = TRUE,
  beta_nCpG = 10000,
  prefix = NULL,
  n_thr = 4
)

Arguments

`meth`	`methrix` object
`output_dir`	Output directory name where the files should be saved. If `NULL` creats a `tempdir`
`recal_stats`	Whether summary statistics should be recalculated? If you are using subsetted methrix object set this to TRUE.
`plot_beta_dist`	Default TRUE. Can be time consuming.
`beta_nCpG`	Number of CpGs rto use for estimating beta value distribution. Default 10000
`prefix`	If provided, the name of the report and the intermediate files will start with the prefix.
`n_thr`	Default 4. Only used if `plot_beta_dist` is TRUE

Value

an interactive html report

Examples

## Not run: 
data('methrix_data')
methrix::methrix_report(meth = methrix_data)

## End(Not run)
## Not run: 
data('methrix_data')
methrix::methrix_report(meth = methrix_data)

## End(Not run)

Class methrix

Description

S4 class Methrix

Slots

assays: A list of tw omatrices containing 'Methylation' and 'Coverage' information
elementMetadata: A DataFrame describing rows in correspoding assay matrices.
colData: genome: the name of the BSgenome that was used to extract CpGs, isHDF5: is it stored in HDF5 Array format
metadata: a list of meta data associated with the assays
NAMES: NULL

Convert `methrix` to `bsseq` object

Description

Convert methrix to bsseq object

Usage

methrix2bsseq(m)
methrix2bsseq(m)

Arguments

`m`	`methrix` object

Details

Takes methrix object and returns a bsseq object

Value

An object of class bsseq

Examples

## Not run: 
data('methrix_data')
methrix2bsseq(m = methrix_data)

## End(Not run)
## Not run: 
data('methrix_data')
methrix2bsseq(m = methrix_data)

## End(Not run)

Order mathrix object by SD

Description

Order mathrix object by SD

Usage

order_by_sd(m)
order_by_sd(m)

Arguments

`m`	`methrix` object

Details

Takes methrix object and reorganizes the data by standard deviation

Value

An object of class methrix

Examples

data('methrix_data')
order_by_sd(m = methrix_data)
data('methrix_data')
order_by_sd(m = methrix_data)

Coverage QC Plots

Description

Coverage QC Plots

Usage

plot_coverage(
  m,
  type = c("hist", "dens"),
  pheno = NULL,
  perGroup = FALSE,
  lim = 100,
  size.lim = 1e+06,
  col_palette = "RdYlGn"
)
plot_coverage(
  m,
  type = c("hist", "dens"),
  pheno = NULL,
  perGroup = FALSE,
  lim = 100,
  size.lim = 1e+06,
  col_palette = "RdYlGn"
)

Arguments

`m`	Input `methrix` object
`type`	Choose between 'hist' (histogram) or 'dens' (density plot).
`pheno`	Column name of colData(m). Will be used as a factor to color different groups in the plot.
`perGroup`	Color the plots in a sample-wise manner?
`lim`	Maximum coverage value to be plotted.
`size.lim`	The maximum number of observarions (sites*samples) to use. If the dataset is larger that this, random sites will be selected from the genome.
`col_palette`	Name of the RColorBrewer palette to use for plotting.

Value

ggplot2 object

Examples

data('methrix_data')
plot_coverage(m = methrix_data)
data('methrix_data')
plot_coverage(m = methrix_data)

Density Plot of $\beta$ -Values

Description

Density Plot of $\beta$ -Values

Usage

plot_density(
  m,
  ranges = NULL,
  n_cpgs = 25000,
  pheno = NULL,
  col_palette = "RdYlGn"
)
plot_density(
  m,
  ranges = NULL,
  n_cpgs = 25000,
  pheno = NULL,
  col_palette = "RdYlGn"
)

Arguments

`m`	Input `methrix` object
`ranges`	genomic regions to be summarized. Could be a data.table with 3 columns (chr, start, end) or a `GenomicRanges` object
`n_cpgs`	Use these many random CpGs for plotting. Default 25000. Set it to `NULL` to use all - which can be memory expensive.
`pheno`	Column name of colData(m). Will be used as a factor to color different groups in the violin plot.
`col_palette`	Name of the RColorBrewer palette to use for plotting.

Value

ggplot2 object

Examples

data('methrix_data')
plot_density(m = methrix_data)
data('methrix_data')
plot_density(m = methrix_data)

Plot PCA results

Description

Plot PCA results

Usage

plot_pca(
  pca_res,
  m = NULL,
  col_anno = NULL,
  shape_anno = NULL,
  pc_x = "PC1",
  pc_y = "PC2",
  show_labels = FALSE
)
plot_pca(
  pca_res,
  m = NULL,
  col_anno = NULL,
  shape_anno = NULL,
  pc_x = "PC1",
  pc_y = "PC2",
  show_labels = FALSE
)

Arguments

`pca_res`	Results from `methrix_pca`
`m`	optinal methrix object. Default NULL
`col_anno`	Column name of colData(m). Default NULL. Will be used as a factor to color different groups. Required `methrix` object
`shape_anno`	Column name of colData(m). Default NULL. Will be used as a factor to shape different groups. Required `methrix` object
`pc_x`	Default 'PC1'
`pc_y`	Default 'PC2'
`show_labels`	Default FLASE

Value

ggplot2 object

Examples

data('methrix_data')
mpc = methrix_pca(methrix_data, do_plot = FALSE)
plot_pca(mpc)
data('methrix_data')
mpc = methrix_pca(methrix_data, do_plot = FALSE)
plot_pca(mpc)

Plot descriptive statistics

Description

Plot descriptive statistics

Usage

plot_stats(
  plot_dat,
  what = "M",
  stat = "mean",
  ignore_chr = NULL,
  samples = NULL,
  n_col = NULL,
  n_row = NULL
)
plot_stats(
  plot_dat,
  what = "M",
  stat = "mean",
  ignore_chr = NULL,
  samples = NULL,
  n_col = NULL,
  n_row = NULL
)

Arguments

`plot_dat`	results from `get_stats`
`what`	Can be `M` or `C`. Default `M`
`stat`	Can be `mean` or `median`. Default `mean`
`ignore_chr`	Chromsomes to ignore. Default `NULL`
`samples`	Use only these samples. Default `NULL`
`n_col`	number of columns. Passed to 'facet_wrap'
`n_row`	number of rows. Passed to 'facet_wrap'

Details

plot descriptive statistics results from get_stats

Value

ggplot2 object

Examples

data('methrix_data')
gs = get_stats(methrix_data)
plot_stats(gs)
data('methrix_data')
gs = get_stats(methrix_data)
plot_stats(gs)

Violin Plot for $\beta$ -Values

Description

Violin Plot for $\beta$ -Values

Usage

plot_violin(
  m,
  ranges = NULL,
  n_cpgs = 25000,
  pheno = NULL,
  col_palette = "RdYlGn"
)
plot_violin(
  m,
  ranges = NULL,
  n_cpgs = 25000,
  pheno = NULL,
  col_palette = "RdYlGn"
)

Arguments

`m`	Input `methrix` object
`ranges`	genomic regions to be summarized. Could be a data.table with 3 columns (chr, start, end) or a `GenomicRanges` object
`n_cpgs`	Use these many random CpGs for plotting. Default 25000. Set it to `NULL` to use all - which can be memory expensive.
`pheno`	Column name of colData(m). Will be used as a factor to color different groups in the violin plot.
`col_palette`	Name of the RColorBrewer palette to use for plotting.

Value

ggplot2 object

Examples

data('methrix_data')
plot_violin(m = methrix_data)
data('methrix_data')
plot_violin(m = methrix_data)

Versatile BedGraph reader.

Description

Versatile BedGraph reader.

Usage

read_bedgraphs(
  files = NULL,
  pipeline = NULL,
  zero_based = TRUE,
  stranded = FALSE,
  collapse_strands = FALSE,
  ref_cpgs = NULL,
  ref_build = NULL,
  contigs = NULL,
  vect = FALSE,
  vect_batch_size = NULL,
  coldata = NULL,
  chr_idx = NULL,
  start_idx = NULL,
  end_idx = NULL,
  beta_idx = NULL,
  M_idx = NULL,
  U_idx = NULL,
  strand_idx = NULL,
  cov_idx = NULL,
  synced_coordinates = FALSE,
  n_threads = 1,
  h5 = FALSE,
  h5_dir = NULL,
  h5temp = NULL,
  verbose = TRUE
)
read_bedgraphs(
  files = NULL,
  pipeline = NULL,
  zero_based = TRUE,
  stranded = FALSE,
  collapse_strands = FALSE,
  ref_cpgs = NULL,
  ref_build = NULL,
  contigs = NULL,
  vect = FALSE,
  vect_batch_size = NULL,
  coldata = NULL,
  chr_idx = NULL,
  start_idx = NULL,
  end_idx = NULL,
  beta_idx = NULL,
  M_idx = NULL,
  U_idx = NULL,
  strand_idx = NULL,
  cov_idx = NULL,
  synced_coordinates = FALSE,
  n_threads = 1,
  h5 = FALSE,
  h5_dir = NULL,
  h5temp = NULL,
  verbose = TRUE
)

Arguments

`files`	bedgraph files.
`pipeline`	Default NULL. Currently supports "Bismark_cov", "MethylDackel", "MethylcTools", "BisSNP", "BSseeker2_CGmap" If not known use idx arguments for manual column assignments.
`zero_based`	Are bedgraph regions zero based ? Default TRUE
`stranded`	Default FALSE
`collapse_strands`	If TRUE collapses CpGs on different crick strand into watson. Deafult FALSE
`ref_cpgs`	BSgenome object, or name of the installed BSgenome package, or an output from `extract_CPGs`. Example: BSgenome.Hsapiens.UCSC.hg19
`ref_build`	reference genome for bedgraphs. Default NULL. Only used for additional details. Doesnt affect in any way.
`contigs`	contigs to restrict genomic CpGs to. Default all autosomes and allosomes - ignoring extra contigs.
`vect`	To use vectorized code. Default FALSE. Set to TRUE if you don't have large number of BedGraph files.
`vect_batch_size`	Default NULL. Process samples in batches. Applicable only when vect = TRUE
`coldata`	An optional DataFrame describing the samples. Row names, if present, become the column names of the matrix. If NULL, then a DataFrame will be created with basename of files used as the row names.
`chr_idx`	column index for chromosome in bedgraph files
`start_idx`	column index for start position in bedgraph files
`end_idx`	column index for end position in bedgraph files
`beta_idx`	column index for beta values in bedgraph files
`M_idx`	column index for read counts supporting Methylation in bedgraph files
`U_idx`	column index for read counts supporting Un-methylation in bedgraph files
`strand_idx`	column index for strand information in bedgraph files
`cov_idx`	column index for total-coverage in bedgraph files
`synced_coordinates`	Are the start and end coordinates of a stranded bedgraph are synchronized between + and - strands? Possible values: FALSE (default), TRUE if the start coordinates are the start coordinates of the C on the plus strand.
`n_threads`	number of threads to use. Default 1. Be-careful - there is a linear increase in memory usage with number of threads. This option is does not work with Windows OS.
`h5`	Should the coverage and methylation matrices be stored as 'HDF5Array'
`h5_dir`	directory to store H5 based object
`h5temp`	temporary directory to store hdf5
`verbose`	Be little chatty ? Default TRUE.

Details

Reads BedGraph files and generates methylation and coverage matrices. Optionally arrays can be serialized as on-disk HDFS5 arrays.

Value

An object of class methrix

Examples

## Not run: 
bdg_files = list.files(path = system.file('extdata', package = 'methrix'),
pattern = '*\\.bedGraph\\.gz$', full.names = TRUE)
hg19_cpgs = methrix::extract_CPGs(ref_genome = 'BSgenome.Hsapiens.UCSC.hg19')
meth = methrix::read_bedgraphs( files = bdg_files, ref_cpgs = hg19_cpgs,
chr_idx = 1, start_idx = 2, M_idx = 3, U_idx = 4,
stranded = FALSE, zero_based = FALSE, collapse_strands = FALSE)

## End(Not run)

## Not run: 
bdg_files = list.files(path = system.file('extdata', package = 'methrix'),
pattern = '*\\.bedGraph\\.gz$', full.names = TRUE)
hg19_cpgs = methrix::extract_CPGs(ref_genome = 'BSgenome.Hsapiens.UCSC.hg19')
meth = methrix::read_bedgraphs( files = bdg_files, ref_cpgs = hg19_cpgs,
chr_idx = 1, start_idx = 2, M_idx = 3, U_idx = 4,
stranded = FALSE, zero_based = FALSE, collapse_strands = FALSE)

## End(Not run)

Filter matrices by region

Description

Filter matrices by region

Usage

region_filter(m, regions, type = "within")
region_filter(m, regions, type = "within")

Arguments

`m`	`methrix` object
`regions`	genomic regions to filter-out. Could be a data.table with 3 columns (chr, start, end) or a `GenomicRanges` object
`type`	defines the type of the overlap of the CpG sites with the target regions. Default value is 'within'. For detailed description, see the `foverlaps` function of the `data.table` package.

Details

Takes methrix object and filters CpGs based on supplied regions in data.table or GRanges format

Value

An object of class methrix

Examples

data('methrix_data')
region_filter(m = methrix_data,
regions = data.table(chr = 'chr21', start = 27867971, end =  27868103))
data('methrix_data')
region_filter(m = methrix_data,
regions = data.table(chr = 'chr21', start = 27867971, end =  27868103))

Removes CpG sites from the object if they overlap with common SNPs

Description

Removes CpG sites from the object if they overlap with common SNPs

Usage

remove_snps(
  m,
  populations = NULL,
  maf_threshold = 0.01,
  reduce_filtering = FALSE,
  forced = FALSE,
  keep = FALSE,
  n_chunks = 1,
  n_cores = 1
)
remove_snps(
  m,
  populations = NULL,
  maf_threshold = 0.01,
  reduce_filtering = FALSE,
  forced = FALSE,
  keep = FALSE,
  n_chunks = 1,
  n_cores = 1
)

Arguments

`m`	`methrix` object
`populations`	Populations to use. Default is all.
`maf_threshold`	The frequency threshold, above which the SNPs will be removed. Default is 0.01
`reduce_filtering`	If TRUE, the SNPs with a MAF < 0.1 will be evaluated and only the highly variable ones will be removed. Default FALSE.
`forced`	the reduce_filtering is not recommended with less than 10 samples, but can be forced. Default is FALSE.
`keep`	Do you want to keep the sites that were filtered out? In this case, the function will return with a list of wo methrix objects.
`n_chunks`	Number of chunks to split the `methrix` object in case it is very large. Can only be used if input data is in HDF5 format. Default = 1.
`n_cores`	Number of parallel instances. Can only be used if input data is in HDF5 format. `n_cores` should be less than or equal to `n_chunks`. If `n_chunks` is not specified, then `n_chunks` is initialized to be equal to `n_cores`. Default = 1.

Details

Takes methrix object and removes common SNPs. SNPs overlapping with a CpG site and have a minor allele frequency (MAF) above a threshold in any of the populations used will be selected and the corresponding CpG sites will be removed from the methrix object. With the reduce_filtering option, SNPs with MAP < 0.1 will be further evaluated. If they show low variance in the dataset, there is probably no genotype variability in the population, therefore the corresponding CpG site won't be removed. Please keep in mind that variance thresholds are

Value

methrix object or a list of methrix objects

Examples

data('methrix_data')
remove_snps(m = methrix_data, maf_threshold=0.01)
data('methrix_data')
remove_snps(m = methrix_data, maf_threshold=0.01)

Remove loci that are uncovered across all samples

Description

Remove loci that are uncovered across all samples

Usage

remove_uncovered(m)
remove_uncovered(m)

Arguments

`m`	`methrix` object

Details

Takes methrix object and removes loci that are uncovered across all samples

Value

An object of class methrix

Examples

data('methrix_data')
remove_uncovered(m = methrix_data)
data('methrix_data')
remove_uncovered(m = methrix_data)

Saves HDF5 methrix object

Description

Saves HDF5 methrix object

Usage

save_HDF5_methrix(m = NULL, dir = NULL, replace = FALSE, ...)
save_HDF5_methrix(m = NULL, dir = NULL, replace = FALSE, ...)

Arguments

`m`	`methrix` object
`dir`	The directory to use. Created, if not existing. Default NULL
`replace`	Should it overwrite the pre-existing data? FALSE by default.
`...`	Parameters to pass to saveHDF5SummarizedExperiment

Details

Takes methrix object and saves it

Value

Nothing

Examples

data('methrix_data')
methrix_data_h5 <- convert_methrix(m=methrix_data)
target_dir = paste0(getwd(), '/temp/')
save_HDF5_methrix(methrix_data_h5, dir = target_dir, replace = TRUE)
data('methrix_data')
methrix_data_h5 <- convert_methrix(m=methrix_data)
target_dir = paste0(getwd(), '/temp/')
save_HDF5_methrix(methrix_data_h5, dir = target_dir, replace = TRUE)

Subsets `methrix` object based on given conditions.

Description

Subsets methrix object based on given conditions.

Usage

subset_methrix(
  m,
  regions = NULL,
  contigs = NULL,
  samples = NULL,
  overlap_type = "within"
)
subset_methrix(
  m,
  regions = NULL,
  contigs = NULL,
  samples = NULL,
  overlap_type = "within"
)

Arguments

`m`	`methrix` object
`regions`	genomic regions to subset by. Could be a data.table with 3 columns (chr, start, end) or a `GenomicRanges` object
`contigs`	chromosome names to subset by
`samples`	sample names to subset by
`overlap_type`	defines the type of the overlap of the CpG sites with the target region. Default value is 'within'. For detailed description, see the `foverlaps` function of the `data.table` package.

Details

Takes methrix object and filters CpGs based on coverage statistics

Value

An object of class methrix

Examples

data('methrix_data')
#Subset to chromosome 1
subset_methrix(methrix_data, contigs = 'chr21')
data('methrix_data')
#Subset to chromosome 1
subset_methrix(methrix_data, contigs = 'chr21')

Writes bedGraphs from methrix object

Description

Writes bedGraphs from methrix object

Usage

write_bedgraphs(
  m,
  output_dir = NULL,
  rm_NA = TRUE,
  force = FALSE,
  n_thr = 4,
  compress = TRUE,
  SeqStyle = "UCSC",
  multiBed = NULL,
  metilene = FALSE,
  phenoCol = NULL,
  add_coverage = FALSE
)
write_bedgraphs(
  m,
  output_dir = NULL,
  rm_NA = TRUE,
  force = FALSE,
  n_thr = 4,
  compress = TRUE,
  SeqStyle = "UCSC",
  multiBed = NULL,
  metilene = FALSE,
  phenoCol = NULL,
  add_coverage = FALSE
)

Arguments

`m`	`methrix` object
`output_dir`	Output directory name where the files should be saved. If `NULL` creats a `tempdir`
`rm_NA`	remove NAs
`force`	forces to create files if they are existing
`n_thr`	Default 4.
`compress`	Whether to compress the output. Default TRUE
`SeqStyle`	Default 'UCSC' with 'chr' prefix.
`multiBed`	Default NULL. If provided a filename, a single bedGraph file with all samples included is generated.
`metilene`	Default FALSE. If TRUE outputs bedgraphs in 'metilene' format that can be directly used for DMR calling with 'metilene'. This option works only when `multiBed = TRUE`.
`phenoCol`	Default NULL. 'condition' column from colData. Only applicable if `metilene = TRUE`
`add_coverage`	Should the output file contain information on coverage? Default FALSE

Value

writes bedgraph files to output

Examples

data('methrix_data')
write_bedgraphs(m = methrix_data, output_dir = './temp')
#Export to metline format for DMR calling with metline
write_bedgraphs(m = methrix_data, output_dir = "./temp", rm_NA = FALSE,
metilene = TRUE,multiBed = "metline_ip", phenoCol = "Condition")
data('methrix_data')
write_bedgraphs(m = methrix_data, output_dir = './temp')
#Export to metline format for DMR calling with metline
write_bedgraphs(m = methrix_data, output_dir = "./temp", rm_NA = FALSE,
metilene = TRUE,multiBed = "metline_ip", phenoCol = "Condition")

Exports methrix object as bigWigs

Description

Exports methrix object as bigWigs

Usage

write_bigwigs(m, output_dir = getwd(), samp_names = NULL)
write_bigwigs(m, output_dir = getwd(), samp_names = NULL)

Arguments

`m`	`methrix` object
`output_dir`	Output directory name where the files should be saved. Default getwd()
`samp_names`	sample names to export

Examples

data('methrix_data')
write_bigwigs(m = methrix_data, output_dir = './temp')
data('methrix_data')
write_bigwigs(m = methrix_data, output_dir = './temp')

Package 'methrix'

Help Index

Combine methrix objects

Description

Usage

Arguments

Details

Value

Converts HDF5 methrix object to standard in-memory object.

Description

Usage

Arguments

Details

Value

Examples

Converts an in-memory object to an on-disk HDF5 object.

Description

Usage

Arguments

Details

Value

Examples

Filter matrices by coverage

Description

Usage

Arguments

Details

Value

Examples

Extracts all CpGs from a genome

Description

Usage

Arguments

Value

Examples

Extract methylation or coverage matrices

Description

Usage

Arguments

Details

Value

Examples

Extract and summarize methylation or coverage info by regions of interest

Description

Usage

Arguments

Details

Value

Examples

Estimate descriptive statistics

Description

Usage

Arguments

Details

Value

See Also

Examples

Loads HDF5 methrix object

Description

Usage

Arguments

Details

Value

Examples

Masks too high or too low coverage

Description

Usage

Arguments

Details

Value

Examples

WGBS for colon cancer, chr21 and chr22

Description

Usage

Format

References

Examples

Principal Component Analysis

Description

Usage

Convert `methrix` to `bsseq` object

Density Plot of $\beta$ -Values

Violin Plot for $\beta$ -Values