uniqueGRfilterByCov: Unique 'GRanges-class' of methylation read counts filtered by...
In genomaths/MethylIT: Methylation Analysis Based on Signal Detection
uniqueGRfilterByCov R Documentation
Unique GRanges-class of methylation
read counts filtered by coverage.
Description
Given two GRanges-class objects,
samples '1' and '2', carrying the counts of methylated (mC) and unmethylated
(uC) cytosines in their metacolumns, this function will filter by coverage
each cytosine site from each GRanges-class
object.
Usage
uniqueGRfilterByCov(
x,
y = NULL,
min.coverage = 4,
and.min.cov = TRUE,
min.meth = 0,
min.umeth = 0,
min.sitecov = 4,
percentile = 0.9999,
min.percentile = TRUE,
high.coverage = NULL,
columns = c(mC = 1, uC = 2),
ignore.strand = FALSE,
y.centroid = NULL,
num.cores = 1L,
tasks = 0L,
verbose = TRUE,
...
)
Arguments
x
An object from the classes 'GRanges', 'InfDiv', or 'pDMP' with
methylated and unmethylated counts in its meta-column. If the argument 'y'
is not given, then it is assumed that the first four columns of the
GRanges-class metadata 'x' are counts:
methylated and unmethylated counts for samples '1' and '2'. If x is a
'InfDiv', or 'pDMP', then 'y' is not needed, since samples '1' and '2'
are the first four columns of these objects. In the context of MethylIT
methylation analysis, x is considered a control sample or, more
appropriated (if available) the centroide of control group.
y
A GRanges-class or a
GRangesList-class object with methylated and
unmethylated counts in its meta-column. Default is NULL. If 'y' is a
GRangesList-class object, then the first two
metacolumns from 'x' are used in the pair-wise filtering toguether with the
first two metacolumns from each GRanges-class
element from 'y'.
min.coverage
An integer or an integer vector of length 2. If
'min.coverage' is an integer vector, then the corresponding min coverage
is applied to each sample. Default is 4, i.e., min.coverage = c(4,4).
and.min.cov
Logical. Whether to apply the logical AND to select the
cytosine sites based on min.coverage. If FALSE, then a logical OR is
applied, and cytosine sites where at least one sample hold the
min.coverage are preserved. Default is TRUE.
min.meth
An integer or an integer vector of length 2. Cytosine sites
where the numbers of read counts of methylated cytosine in both samples, '1'
and '2', are less than 'min.meth' are discarded. If 'min.meth' is an integer
vector, then the corresponding min number of reads is applied to each
sample. That is, min.meth implement a logical OR.
min.umeth
An integer or an integer vector of length 2. Minimum number
of reads to consider cytosine position. Specifically cytosine positions
where uC \leq min.umeth are discarded. Default is min.umeth = 0.
min.sitecov
An integer. The minimum total coverage. Only sites where
the total coverage cov1 + cov2 is greater than 'min.sitecov' are
considered for downstream analysis, where cov1 and cov2 are the coverages
for samples 1 and 2, respectively.
percentile
Threshold to remove the outliers (PCR bias) from each file
and all files stacked. If 'high.coverage = NULL', then the threshold q
will be computed as:
q1 = quantile(cov1, probs=percentile)
q2 = quantile(cov2, probs=percentile)
.
where cov1 and cov2 are the coverage vectors from samples 1
and 2, respectively.
min.percentile
Logical. Each sample yield a percentile value. The
user must decide whether to use the minimum or the maximum value from these
percentile values. Default is TRUE. Hence, min.percentile = FALSE
implies to use the maximum value.
high.coverage
An integer for read counts. Cytosine sites having
higher coverage than this are discarded. Default is NULL.
columns
Vector of integer numbers of the columns (from each GRanges
meta-column) where the methylated and unmethylated counts are provided. If
not provided, then the methylated and unmethylated counts are assumed to be
at columns 1 and 2, respectively.
ignore.strand
When set to TRUE, the strand information is ignored in
the overlap of GRanges-class objects. This is a
parameter passed to uniqueGRanges function. Default value:
FALSE.
y.centroid
Optional. A GRanges-class
object corresponding to the treatment/individual centroide. This information
is applied to reduce the bias originated by missing cytosine sites. The
centroide can be compute applying function poolFromGRlist.
num.cores, tasks
Parameters for parallel computation using package
BiocParallel-package: the number of cores to
use, i.e. at most how many child processes will be run simultaneously (see
bplapply and the number of tasks per job (only
for Linux OS).
verbose
if TRUE, prints the function log to stdout
...
Additional parameters for uniqueGRanges function.
Details
Cytosine sites with 'coverage' > 'min.coverage' in at least one of
the samples are preserved. Positions with 'coverage' < 'min.coverage' in
both samples, 'x' and 'y', are removed. Positions with 'coverage' >
'percentile' (e.g., 99.9 percentile) are removed as well. It is expected
that the columns of methylated and unmethylated counts are given.
This function is addressed to create pair-wise
GRanges-class object with four metacolumns of
count: samples '1' and '2', carrying the counts of methylated (mC) and
unmethylated (uC) cytosines in their metacolumns, respectively. Counts from
sample 1 are typically used as reference counts in computing information
divergences in the downtstream analysis.
The cut-off value to remove PCR bias is computed as:
If high.coverage is NULL, then the cut-off point is:
q = min(q1, q2) (if min.percentile = TRUE) or
q = max(q1, q2). If high.coverage is not NULL, then
q = max(q, high.coverage)
.
Another source of bias is originated by missing cytosine sites. Missing data
are frequently found in experimental data sets and, in particular, in
bisulfite genomic sequencing data. Typically, in statistical analyses, the
bias originated by missing data (for given variable) is mitigated by using
the mean of the known values for the corresponding variable. That is, in
present case, if the reads for some cytosine site are missed in a sample
from a set of, e.g., three individuals, then the means of reads (methylated
and unmethylated) for such site are applied as an estimation of the best
expected ("guessed") value of missed reads. Obviously, if the reads are
missed in all the samples, then the site is discarded (see examples).
The treatment centroide can be compute applying function
poolFromGRlist. Also notice that, since the centroide
correction is only available for the treat group, it is assumed that sample
x carries reads for each (or almost all) cytosine sites are provide.
Value
if 'x' and 'y' are GRanges-class
object, then a GRanges-class with the columns
of methylated and unmethylated counts filtered for each cytosine position.
A GRangesList-class object will returned, if
'y' is a GRangesList-class object of same
length as length(y) and named as names(y).
Examples
## Create new data. It is assumed that sample 'x' carries reads
## for each cytosine sites are provide.
strands <- c("+","-","+","-", "+","-","+","+","+","+","+")
pos <- c(10,11,11,12,13,13,14,15,16,17,18)
x <- data.frame(chr = 'chr1', start = pos, end = pos,
mC = c(2,3,2,5,10,7,9,11,4,10,7),
uC = c(2,30,20,4,8,0,10,3,0,8,1),
strand = strands)
x <- makeGRangesFromDataFrame(x, keep.extra.columns = TRUE)
x
## sample y
y <- data.frame(chr = 'chr1', start = 11:18, end = 11:18,
mC2 = c(4,1,2,1,4,5:7), uC2 = c(0,0,2:7),
strand = c("+","-","-","+","+","+","+","+"))
y <- makeGRangesFromDataFrame(y, keep.extra.columns = TRUE)
y
## The default settings. Sites where one of the samples has zero methylation
## calling or min.coverage is lesser than 4 in at least one sample are
## discarded. This setting implies a drastic decision and the amount of
## cytosine removed can be lead to strong biased conclusions in the
## downstream analysis.
uniqueGRfilterByCov(x = x, y = y,
percentile = 1,
ignore.strand = FALSE)
## Setting 'and.min.cov = FALSE' undesired cytosine sites are preseved. For
## example, meaningless situations with methylation levels
## p = 1/(1 + 0) = 1.
uniqueGRfilterByCov(x = x, y = y,
and.min.cov = FALSE,
ignore.strand = FALSE,
percentile = 1,
verbose = FALSE)
## Setting 'min.coverage = 8' does not solves the previous issue, but still
## it preserves cytosine sites with one of the samples with zero or small
## coverage:
uniqueGRfilterByCov(x = x, y = y,
and.min.cov = FALSE,
min.coverage = 8,
percentile = 1,
ignore.strand = FALSE,
verbose = FALSE)
## A centroid, a vector of means of methylation read for each cytosine site
## from the treatment group can be used as the best estimation to replace
## missing data: 'mC=0' and 'uC=0' or low coverage sites 'mC=1' and 'uC=0'.
y_centroid <- data.frame(chr = 'chr1',
start = pos, end = pos,
mC2 = c(8,7,6,7,5,8,1:5), uC2 = 0:10,
strand = c("+","-","+","-", "+","-","+","+","+","+","+"))
y_centroid <- makeGRangesFromDataFrame(y_centroid,
keep.extra.columns = TRUE)
y_centroid
## The cytosine sites with missing data or low coverage will be still,
## included, using the centroid of the centroinde of th sample group to
## which 'y' belong to.
uniqueGRfilterByCov(x = x, y = y,
and.min.cov = FALSE,
min.coverage = c(1, 8),
ignore.strand = FALSE,
y.centroid = y_centroid,
min.percentile = FALSE,
percentile = 1,
verbose = FALSE)
genomaths/MethylIT documentation built on Feb. 3, 2024, 1:24 a.m.
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| uniqueGRfilterByCov | R Documentation |
Unique GRanges-class of methylation
read counts filtered by coverage.
Description
Given two GRanges-class objects,
samples '1' and '2', carrying the counts of methylated (mC) and unmethylated
(uC) cytosines in their metacolumns, this function will filter by coverage
each cytosine site from each GRanges-class
object.
Usage
uniqueGRfilterByCov(
x,
y = NULL,
min.coverage = 4,
and.min.cov = TRUE,
min.meth = 0,
min.umeth = 0,
min.sitecov = 4,
percentile = 0.9999,
min.percentile = TRUE,
high.coverage = NULL,
columns = c(mC = 1, uC = 2),
ignore.strand = FALSE,
y.centroid = NULL,
num.cores = 1L,
tasks = 0L,
verbose = TRUE,
...
)
Arguments
x |
An object from the classes 'GRanges', 'InfDiv', or 'pDMP' with
methylated and unmethylated counts in its meta-column. If the argument 'y'
is not given, then it is assumed that the first four columns of the
|
y |
A |
min.coverage |
An integer or an integer vector of length 2. If
'min.coverage' is an integer vector, then the corresponding min coverage
is applied to each sample. Default is 4, i.e., |
and.min.cov |
Logical. Whether to apply the logical AND to select the
cytosine sites based on |
min.meth |
An integer or an integer vector of length 2. Cytosine sites
where the numbers of read counts of methylated cytosine in both samples, '1'
and '2', are less than 'min.meth' are discarded. If 'min.meth' is an integer
vector, then the corresponding min number of reads is applied to each
sample. That is, |
min.umeth |
An integer or an integer vector of length 2. Minimum number
of reads to consider cytosine position. Specifically cytosine positions
where |
min.sitecov |
An integer. The minimum total coverage. Only sites where
the total coverage |
percentile |
Threshold to remove the outliers (PCR bias) from each file
and all files stacked. If 'high.coverage = NULL', then the threshold
. where |
min.percentile |
Logical. Each sample yield a percentile value. The
user must decide whether to use the minimum or the maximum value from these
percentile values. Default is TRUE. Hence, |
high.coverage |
An integer for read counts. Cytosine sites having higher coverage than this are discarded. Default is NULL. |
columns |
Vector of integer numbers of the columns (from each GRanges meta-column) where the methylated and unmethylated counts are provided. If not provided, then the methylated and unmethylated counts are assumed to be at columns 1 and 2, respectively. |
ignore.strand |
When set to TRUE, the strand information is ignored in
the overlap of |
y.centroid |
Optional. A |
num.cores, tasks |
Parameters for parallel computation using package
|
verbose |
if TRUE, prints the function log to stdout |
... |
Additional parameters for |
Details
Cytosine sites with 'coverage' > 'min.coverage' in at least one of the samples are preserved. Positions with 'coverage' < 'min.coverage' in both samples, 'x' and 'y', are removed. Positions with 'coverage' > 'percentile' (e.g., 99.9 percentile) are removed as well. It is expected that the columns of methylated and unmethylated counts are given.
This function is addressed to create pair-wise
GRanges-class object with four metacolumns of
count: samples '1' and '2', carrying the counts of methylated (mC) and
unmethylated (uC) cytosines in their metacolumns, respectively. Counts from
sample 1 are typically used as reference counts in computing information
divergences in the downtstream analysis.
The cut-off value to remove PCR bias is computed as:
If high.coverage is NULL, then the cut-off point is:
q = min(q1, q2) (if min.percentile = TRUE) or
q = max(q1, q2). If high.coverage is not NULL, then
q = max(q, high.coverage)
.
Another source of bias is originated by missing cytosine sites. Missing data are frequently found in experimental data sets and, in particular, in bisulfite genomic sequencing data. Typically, in statistical analyses, the bias originated by missing data (for given variable) is mitigated by using the mean of the known values for the corresponding variable. That is, in present case, if the reads for some cytosine site are missed in a sample from a set of, e.g., three individuals, then the means of reads (methylated and unmethylated) for such site are applied as an estimation of the best expected ("guessed") value of missed reads. Obviously, if the reads are missed in all the samples, then the site is discarded (see examples).
The treatment centroide can be compute applying function
poolFromGRlist. Also notice that, since the centroide
correction is only available for the treat group, it is assumed that sample
x carries reads for each (or almost all) cytosine sites are provide.
Value
if 'x' and 'y' are GRanges-class
object, then a GRanges-class with the columns
of methylated and unmethylated counts filtered for each cytosine position.
A GRangesList-class object will returned, if
'y' is a GRangesList-class object of same
length as length(y) and named as names(y).
Examples
## Create new data. It is assumed that sample 'x' carries reads
## for each cytosine sites are provide.
strands <- c("+","-","+","-", "+","-","+","+","+","+","+")
pos <- c(10,11,11,12,13,13,14,15,16,17,18)
x <- data.frame(chr = 'chr1', start = pos, end = pos,
mC = c(2,3,2,5,10,7,9,11,4,10,7),
uC = c(2,30,20,4,8,0,10,3,0,8,1),
strand = strands)
x <- makeGRangesFromDataFrame(x, keep.extra.columns = TRUE)
x
## sample y
y <- data.frame(chr = 'chr1', start = 11:18, end = 11:18,
mC2 = c(4,1,2,1,4,5:7), uC2 = c(0,0,2:7),
strand = c("+","-","-","+","+","+","+","+"))
y <- makeGRangesFromDataFrame(y, keep.extra.columns = TRUE)
y
## The default settings. Sites where one of the samples has zero methylation
## calling or min.coverage is lesser than 4 in at least one sample are
## discarded. This setting implies a drastic decision and the amount of
## cytosine removed can be lead to strong biased conclusions in the
## downstream analysis.
uniqueGRfilterByCov(x = x, y = y,
percentile = 1,
ignore.strand = FALSE)
## Setting 'and.min.cov = FALSE' undesired cytosine sites are preseved. For
## example, meaningless situations with methylation levels
## p = 1/(1 + 0) = 1.
uniqueGRfilterByCov(x = x, y = y,
and.min.cov = FALSE,
ignore.strand = FALSE,
percentile = 1,
verbose = FALSE)
## Setting 'min.coverage = 8' does not solves the previous issue, but still
## it preserves cytosine sites with one of the samples with zero or small
## coverage:
uniqueGRfilterByCov(x = x, y = y,
and.min.cov = FALSE,
min.coverage = 8,
percentile = 1,
ignore.strand = FALSE,
verbose = FALSE)
## A centroid, a vector of means of methylation read for each cytosine site
## from the treatment group can be used as the best estimation to replace
## missing data: 'mC=0' and 'uC=0' or low coverage sites 'mC=1' and 'uC=0'.
y_centroid <- data.frame(chr = 'chr1',
start = pos, end = pos,
mC2 = c(8,7,6,7,5,8,1:5), uC2 = 0:10,
strand = c("+","-","+","-", "+","-","+","+","+","+","+"))
y_centroid <- makeGRangesFromDataFrame(y_centroid,
keep.extra.columns = TRUE)
y_centroid
## The cytosine sites with missing data or low coverage will be still,
## included, using the centroid of the centroinde of th sample group to
## which 'y' belong to.
uniqueGRfilterByCov(x = x, y = y,
and.min.cov = FALSE,
min.coverage = c(1, 8),
ignore.strand = FALSE,
y.centroid = y_centroid,
min.percentile = FALSE,
percentile = 1,
verbose = FALSE)
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