mspc: Multiple Sample Peak Calling
In Genometric/rmspc: Multiple Sample Peak Calling
mspc R Documentation
Multiple Sample Peak Calling
Description
MSPC comparatively evaluates ChIP-seq peaks and combines the
statistical significance of repeated evidences, with the aim
of lowering the minimum significance required to rescue
weak peaks; hence reducing false-negatives.
Usage
mspc(
input,
replicateType,
stringencyThreshold,
weakThreshold,
gamma = NULL,
c = NULL,
alpha = NULL,
multipleIntersections = NULL,
degreeOfParallelism = NULL,
inputParserConfiguration = NULL,
outputPath = NULL,
GRanges = FALSE,
keep = NULL,
directoryGRangesInput = NULL,
mspcPath = NULL
)
Arguments
input
Character vector or GRanges list.
The input argument is a character vector when the data the user wants to use
as a sample is in a BED file format. In this case, the data should
be a tab-delimited file in BED format for each replicate, each
containing enriched regions (aka peaks) called with a
permissive p-value threshold.
The input argument will then be a character vector, each element of the
vector will have the file path of the BED file the user wants to use.
The input argument is a GRanges list when the user wants to use
multiple GRanges objects as samples.
This arguments is required. More information in Details
replicateType
Character string. This argument defines
the replicate type. Possible values of the argument :
'Bio','Biological', 'Tec', 'Technical'.
This arguments is required . More information in Details.
stringencyThreshold
Double. A threshold on p-values,
where peaks with p-value lower than this threshold, are
considered stringent.This arguments is required
weakThreshold
Double. A threshold on p-values,
such that peaks with p-value between this and stringency
threshold, are considered weak peaks. This arguments is required
gamma
Double. The combined stringency threshold.
Peaks with combined p-value below this threshold are confirmed.
Default value is the value of the argument stringencyThreshold.
This arguments is optional.
c
Integer or character string. the minimum number of overlapping peaks
required before the MSPC program combines their p-value.
The value of c can be given in absolute (e.g., c = 2 will require
at least 2 samples) or percentage of input samples (e.g., c = 50
will require at least 50
Default value is 1. This arguments is optional.
More information in Details.
alpha
Double. The threshold for Benjamini-Hochberg
multiple testing correction.
Default value is 0.05. This arguments is optional.
multipleIntersections
Character string. When multiple peaks
from a sample overlap with a given peak, this argument
defines which of the peaks to be considered: the one with
lowest p-value, or the one with highest p-value?
Possible value of the argument are either 'Lowest'
or 'Highest'.
Default value is 'Lowest'. This arguments is optional.
degreeOfParallelism
Integer. The number of parallel
threads the MSPC program can utilize simultaneously when
processing data.
Default value is the number of logical processors
on the current machine. This arguments is optional.
inputParserConfiguration
File path. The path to a JSON file
containing the configuration for the input BED file parser.
This is an optional argument and it has no default value.
outputPath
Directory path. This argument sets the path in
which analysis results should be persisted.
This is an optional argument. More information in Details.
GRanges
Logical. Determines whether or not the mspc
function should import the files, created while running the mspc function,
as GRanges objects into the R environment. The default value is FALSE.
However, when the keep argument is set to FALSE, the value
of the argument GRanges is set to TRUE, and the value
given by the user to the GRanges argument is ignored.
keep
Logical. This argument determines whether the mspc function
should keep or delete all the files generated while running
the MSPC program.This is an optional argument.
More information in Details.
directoryGRangesInput
Folder path. When the input argument is
a GRanges list, the mspc function exports it as
multiple BED files.
The directoryGRangesInput argument specifies the
directory where these BED files should be stored.
More information in Details.
mspcPath
File path. The MSPC program that the rmspc package
uses can be installed from the official Github page of the MSPC program.
If the users wishes to use the version of the MSPC program he installs,
he can specify the installation path of the mspc dll program installed
using the mspcPath argument.
The default value is NULL. If no value is given to this argument,
the MSPC program used is the one included in the rmspc package.
Details
input :
The input can either be BED files or a GRanges list.
Only one type of inputs is supported by the mspc function
at a time, ie, the user can either give all the inputs as names of
BED files, or all the inputs as GRanges objects.
Therefore, the user cannot give an input argument that contains
BED files and GRanges objects.
replicateType:
Samples could be biological or technical replicates.
MSPC differentiates between the two replicate types
based on the fact that less variations between
technical replicates is expected compared to biological
replicates.
c :
It sets the minimum number of overlapping peaks required before MSPC
combines their p-value. For example, given three replicates
(rep1, rep2 and rep3), if c = 3, a peak on rep1 must overlap
with at least two peaks, one from rep2 and one from rep3,
before MSPC combines their p-value; otherwise, MSPC discards
the peak. If c = 2, a peak on rep1 must overlap with at
least one peak from either rep2 or rep3, before MSPC combines
their p-values; otherwise MSPC discards the peak.
outputPath:
When the mspc function is called, it creates a set of files
in the directory specified by the argument outputPath. If
no value is given to this argument, a folder is created in
the current working directory, under the name
"session_ + <Timestamp>". If a folder name is given to the
argument outputPath, a folder under the name specified is
created in the current working directory. If a given folder
name already exists, and is not empty, the MSPC program
will append _n where n is an integer until no duplicate is
found in which analysis results should be persisted.
keep :
When the mspc function is called, it creates a set of files
in the user's computer. The user can choose to keep or not the
files created.
When the argument keep is set to FALSE, all the files
are created in a temporary folder, which is deleted after
the R session is closed.
When the argument keep is set to TRUE, the files are created
in the folder specified by the argument outputPath.
The default value of the argument keep is defined as follows :
if the input argument is a GRanges object, the default value
of the keep argument is FALSE.
if the input argument is a character vector of
the file path of input BED files, the default value of the
keep argument is TRUE.
directoryGRangesInput :
The default value is the current working directory.
It is important to note that when the argument keep is set to FALSE,
the value of this argument is set to a temporary folder.
If the input argument is a character vector of BED files names,
the argument directoryGRangesInput is ignored.
Output of the mspc function :
When the value of the argument keep is set to FALSE,
the argument GRanges is automatically set to TRUE.
all the files are created in a temporary folder,
which is deleted after the R session is closed.
The files created are also imported to the R environment
as GRanges objects.
In this case, the function mspc returns the following :
1. status
2. GRangesObjects
When the value of the argument GRanges is set to FALSE
and the argument keep is set to TRUE, no GRanges object will be
imported to the R environment.
In this case, the function mspc returns the following :
1. status
2. filesCreated.
About the files generated by mspc :
As previously mentioned, when the mspc function is
called, it creates a set of files. These files are
listed in the object filesCreated, returned by mspc.
The files created are :
1. A log file that contains the execution log : This file
contains the information, debugging messages, and exceptions
that occurred during the execution.
2. Consensus peaks in standard BED and MSPC format.
3. One folder per each replicates that contains BED files,
containing stringent, weak, background, confirmed, discarded,
true-positive, and false-positive peaks.
Value
The mspc function prints the results of the MSPC program.
The mspc function also creates a set of files in the directory
specified by the argument outputPath.
The function can return the following :
1. status : Integer. The exit status of running the mspc function.
A zero exit status means the function ran successfully.
2. filesCreated : List of character vectors. The names of the
files generated while running the mspc function.
3. GrangesObjects : GRanges list. All the files generated while
running the mspc function are imported as GRanges objects,
and are combined in a GRanges list.
It is important to note that the mspc function does not
always return these 3 elements.
The output of the function depends on the arguments keep
and GRanges given to the mspc function.
More information regarding the output in Details.
Examples
#Providing input as BED files :
path <- system.file("extdata", package="rmspc")
input1 <- paste0(path, "/rep1.bed")
input2 <- paste0(path, "/rep2.bed")
input <- c(input1, input2)
results <- mspc(input = input, replicateType = "Technical",
stringencyThreshold = 1e-8,
weakThreshold = 1e-4, gamma = 1e-8,
keep = FALSE,GRanges = TRUE,
multipleIntersections = "Lowest",
c = 2,alpha = 0.05)
#Providing input as a GRanges list :
library(GenomicRanges)
library(rtracklayer)
GR1 <- import(input1, format="bed")
GR2 <- import(input2, format="bed")
GR <- GRangesList("GR1"=GR1, "GR2"=GR2)
results <- mspc(
input = GR, replicateType = "Biological",
stringencyThreshold = 1e-8, weakThreshold = 1e-4,
gamma = 1e-8, GRanges = TRUE, keep = FALSE,
multipleIntersections = "Highest",
c = 2,alpha = 0.05)
Genometric/rmspc documentation built on Jan. 2, 2023, 8:19 p.m.
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| mspc | R Documentation |
Multiple Sample Peak Calling
Description
MSPC comparatively evaluates ChIP-seq peaks and combines the statistical significance of repeated evidences, with the aim of lowering the minimum significance required to rescue weak peaks; hence reducing false-negatives.
Usage
mspc( input, replicateType, stringencyThreshold, weakThreshold, gamma = NULL, c = NULL, alpha = NULL, multipleIntersections = NULL, degreeOfParallelism = NULL, inputParserConfiguration = NULL, outputPath = NULL, GRanges = FALSE, keep = NULL, directoryGRangesInput = NULL, mspcPath = NULL )
Arguments
input |
Character vector or GRanges list. The input argument is a character vector when the data the user wants to use as a sample is in a BED file format. In this case, the data should be a tab-delimited file in BED format for each replicate, each containing enriched regions (aka peaks) called with a permissive p-value threshold. The input argument will then be a character vector, each element of the vector will have the file path of the BED file the user wants to use. The input argument is a GRanges list when the user wants to use multiple GRanges objects as samples. This arguments is required. More information in Details |
replicateType |
Character string. This argument defines the replicate type. Possible values of the argument : 'Bio','Biological', 'Tec', 'Technical'. This arguments is required . More information in Details. |
stringencyThreshold |
Double. A threshold on p-values, where peaks with p-value lower than this threshold, are considered stringent.This arguments is required |
weakThreshold |
Double. A threshold on p-values, such that peaks with p-value between this and stringency threshold, are considered weak peaks. This arguments is required |
gamma |
Double. The combined stringency threshold. Peaks with combined p-value below this threshold are confirmed. Default value is the value of the argument stringencyThreshold. This arguments is optional. |
c |
Integer or character string. the minimum number of overlapping peaks required before the MSPC program combines their p-value. The value of c can be given in absolute (e.g., c = 2 will require at least 2 samples) or percentage of input samples (e.g., c = 50 will require at least 50 Default value is 1. This arguments is optional. More information in Details. |
alpha |
Double. The threshold for Benjamini-Hochberg multiple testing correction. Default value is 0.05. This arguments is optional. |
multipleIntersections |
Character string. When multiple peaks from a sample overlap with a given peak, this argument defines which of the peaks to be considered: the one with lowest p-value, or the one with highest p-value? Possible value of the argument are either 'Lowest' or 'Highest'. Default value is 'Lowest'. This arguments is optional. |
degreeOfParallelism |
Integer. The number of parallel threads the MSPC program can utilize simultaneously when processing data. Default value is the number of logical processors on the current machine. This arguments is optional. |
inputParserConfiguration |
File path. The path to a JSON file containing the configuration for the input BED file parser. This is an optional argument and it has no default value. |
outputPath |
Directory path. This argument sets the path in which analysis results should be persisted. This is an optional argument. More information in Details. |
GRanges |
Logical. Determines whether or not the mspc function should import the files, created while running the mspc function, as GRanges objects into the R environment. The default value is FALSE. However, when the keep argument is set to FALSE, the value of the argument GRanges is set to TRUE, and the value given by the user to the GRanges argument is ignored. |
keep |
Logical. This argument determines whether the mspc function should keep or delete all the files generated while running the MSPC program.This is an optional argument. More information in Details. |
directoryGRangesInput |
Folder path. When the input argument is a GRanges list, the mspc function exports it as multiple BED files. The directoryGRangesInput argument specifies the directory where these BED files should be stored. More information in Details. |
mspcPath |
File path. The MSPC program that the rmspc package uses can be installed from the official Github page of the MSPC program. If the users wishes to use the version of the MSPC program he installs, he can specify the installation path of the mspc dll program installed using the mspcPath argument. The default value is NULL. If no value is given to this argument, the MSPC program used is the one included in the rmspc package. |
Details
input :
The input can either be BED files or a GRanges list. Only one type of inputs is supported by the mspc function at a time, ie, the user can either give all the inputs as names of BED files, or all the inputs as GRanges objects. Therefore, the user cannot give an input argument that contains BED files and GRanges objects.
replicateType:
Samples could be biological or technical replicates. MSPC differentiates between the two replicate types based on the fact that less variations between technical replicates is expected compared to biological replicates.
c :
It sets the minimum number of overlapping peaks required before MSPC combines their p-value. For example, given three replicates (rep1, rep2 and rep3), if c = 3, a peak on rep1 must overlap with at least two peaks, one from rep2 and one from rep3, before MSPC combines their p-value; otherwise, MSPC discards the peak. If c = 2, a peak on rep1 must overlap with at least one peak from either rep2 or rep3, before MSPC combines their p-values; otherwise MSPC discards the peak.
outputPath:
When the mspc function is called, it creates a set of files in the directory specified by the argument outputPath. If no value is given to this argument, a folder is created in the current working directory, under the name "session_ + <Timestamp>". If a folder name is given to the argument outputPath, a folder under the name specified is created in the current working directory. If a given folder name already exists, and is not empty, the MSPC program will append _n where n is an integer until no duplicate is found in which analysis results should be persisted.
keep :
When the mspc function is called, it creates a set of files in the user's computer. The user can choose to keep or not the files created. When the argument keep is set to FALSE, all the files are created in a temporary folder, which is deleted after the R session is closed. When the argument keep is set to TRUE, the files are created in the folder specified by the argument outputPath. The default value of the argument keep is defined as follows : if the input argument is a GRanges object, the default value of the keep argument is FALSE. if the input argument is a character vector of the file path of input BED files, the default value of the keep argument is TRUE.
directoryGRangesInput :
The default value is the current working directory. It is important to note that when the argument keep is set to FALSE, the value of this argument is set to a temporary folder. If the input argument is a character vector of BED files names, the argument directoryGRangesInput is ignored.
Output of the mspc function :
When the value of the argument keep is set to FALSE, the argument GRanges is automatically set to TRUE. all the files are created in a temporary folder, which is deleted after the R session is closed. The files created are also imported to the R environment as GRanges objects.
In this case, the function mspc returns the following :
1. status
2. GRangesObjects
When the value of the argument GRanges is set to FALSE and the argument keep is set to TRUE, no GRanges object will be imported to the R environment. In this case, the function mspc returns the following :
1. status
2. filesCreated.
About the files generated by mspc :
As previously mentioned, when the mspc function is called, it creates a set of files. These files are listed in the object filesCreated, returned by mspc.
The files created are :
1. A log file that contains the execution log : This file contains the information, debugging messages, and exceptions that occurred during the execution.
2. Consensus peaks in standard BED and MSPC format.
3. One folder per each replicates that contains BED files, containing stringent, weak, background, confirmed, discarded, true-positive, and false-positive peaks.
Value
The mspc function prints the results of the MSPC program.
The mspc function also creates a set of files in the directory specified by the argument outputPath.
The function can return the following :
1. status : Integer. The exit status of running the mspc function. A zero exit status means the function ran successfully.
2. filesCreated : List of character vectors. The names of the files generated while running the mspc function.
3. GrangesObjects : GRanges list. All the files generated while running the mspc function are imported as GRanges objects, and are combined in a GRanges list.
It is important to note that the mspc function does not always return these 3 elements.
The output of the function depends on the arguments keep and GRanges given to the mspc function.
More information regarding the output in Details.
Examples
#Providing input as BED files :
path <- system.file("extdata", package="rmspc")
input1 <- paste0(path, "/rep1.bed")
input2 <- paste0(path, "/rep2.bed")
input <- c(input1, input2)
results <- mspc(input = input, replicateType = "Technical",
stringencyThreshold = 1e-8,
weakThreshold = 1e-4, gamma = 1e-8,
keep = FALSE,GRanges = TRUE,
multipleIntersections = "Lowest",
c = 2,alpha = 0.05)
#Providing input as a GRanges list :
library(GenomicRanges)
library(rtracklayer)
GR1 <- import(input1, format="bed")
GR2 <- import(input2, format="bed")
GR <- GRangesList("GR1"=GR1, "GR2"=GR2)
results <- mspc(
input = GR, replicateType = "Biological",
stringencyThreshold = 1e-8, weakThreshold = 1e-4,
gamma = 1e-8, GRanges = TRUE, keep = FALSE,
multipleIntersections = "Highest",
c = 2,alpha = 0.05)
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