| Title: | Enhanced copy-number variation analysis using Illumina DNA methylation arrays |
|---|---|
| Description: | This package contains a set of processing and plotting methods for performing copy-number variation (CNV) analysis using Illumina 450k or EPIC methylation arrays. |
| Authors: | Volker Hovestadt, Marc Zapatka |
| Maintainer: | Volker Hovestadt <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.41.0 |
| Built: | 2024-10-30 05:43:10 UTC |
| Source: | https://github.com/bioc/conumee |
CNV analysis data of a single sample is stored in this class
## S4 method for signature 'CNV.analysis' show(object) ## S4 method for signature 'CNV.analysis' names(x) ## S4 replacement method for signature 'CNV.analysis' names(x) <- value ## S4 method for signature 'CNV.analysis' coef(object)## S4 method for signature 'CNV.analysis' show(object) ## S4 method for signature 'CNV.analysis' names(x) ## S4 replacement method for signature 'CNV.analysis' names(x) <- value ## S4 method for signature 'CNV.analysis' coef(object)
object |
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x |
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value |
Replacement names. |
Use CNV.fit to create. Modified by CNV.bin, CNV.detail and CNV.segment.
CNV.analysis class.
Volker Hovestadt [email protected]
# prepare library(minfiData) data(MsetEx) d <- CNV.load(MsetEx) anno <- CNV.create_anno() # create object x <- CNV.fit(query = d['GroupB_1'], ref = d[c('GroupA_1', 'GroupA_2', 'GroupA_3')], anno) # modify object x <- CNV.bin(x) x <- CNV.detail(x) x <- CNV.segment(x) # general information x show(x) # coefficients of linear regression coef(x) # show or replace sample name names(x) names(x) <- 'Sample 1' # output plots CNV.genomeplot(x) CNV.genomeplot(x, chr = 'chr6') #CNV.detailplot(x, name = 'MYCN') #CNV.detailplot_wrap(x) CNV.write(x, what = 'segments')# prepare library(minfiData) data(MsetEx) d <- CNV.load(MsetEx) anno <- CNV.create_anno() # create object x <- CNV.fit(query = d['GroupB_1'], ref = d[c('GroupA_1', 'GroupA_2', 'GroupA_3')], anno) # modify object x <- CNV.bin(x) x <- CNV.detail(x) x <- CNV.segment(x) # general information x show(x) # coefficients of linear regression coef(x) # show or replace sample name names(x) names(x) <- 'Sample 1' # output plots CNV.genomeplot(x) CNV.genomeplot(x, chr = 'chr6') #CNV.detailplot(x, name = 'MYCN') #CNV.detailplot_wrap(x) CNV.write(x, what = 'segments')
Annotations required for CNV analysis are stored in this class.
## S4 method for signature 'CNV.anno' show(object)## S4 method for signature 'CNV.anno' show(object)
object |
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This class does not contain any sample data. Use CNV.create_anno to create.
CNV.anno class.
Volker Hovestadt [email protected]
# create object anno <- CNV.create_anno() # general information anno show(anno)# create object anno <- CNV.create_anno() # general information anno show(anno)
Combine single probe intensitiy values into predefined bins.
CNV.bin(object, ...) ## S4 method for signature 'CNV.analysis' CNV.bin(object)CNV.bin(object, ...) ## S4 method for signature 'CNV.analysis' CNV.bin(object)
object |
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... |
Additional parameters ( |
The median intensity per bin is calculated. Bins are defined using CNV.create_anno. A value by which all probe and bin intensity values are shifted in subsequent analysis steps is calculated by minimizing the median absolute deviation from all bins to zero (ideally shifting the copy-neutral state to 0).
CNV.analysis object.
Volker Hovestadt [email protected]
# prepare library(minfiData) data(MsetEx) d <- CNV.load(MsetEx) data(detail_regions) anno <- CNV.create_anno(detail_regions = detail_regions) # create object x <- CNV.fit(query = d['GroupB_1'], ref = d[c('GroupA_1', 'GroupA_2', 'GroupA_3')], anno) # modify object x <- CNV.bin(x) #x <- CNV.detail(x) #x <- CNV.segment(x) # general information x show(x) # coefficients of linear regression coef(x) # show or replace sample name names(x) names(x) <- 'Sample 1'# prepare library(minfiData) data(MsetEx) d <- CNV.load(MsetEx) data(detail_regions) anno <- CNV.create_anno(detail_regions = detail_regions) # create object x <- CNV.fit(query = d['GroupB_1'], ref = d[c('GroupA_1', 'GroupA_2', 'GroupA_3')], anno) # modify object x <- CNV.bin(x) #x <- CNV.detail(x) #x <- CNV.segment(x) # general information x show(x) # coefficients of linear regression coef(x) # show or replace sample name names(x) names(x) <- 'Sample 1'
Check intensity values.
CNV.check(object) ## S4 method for signature 'CNV.data' CNV.check(object)CNV.check(object) ## S4 method for signature 'CNV.data' CNV.check(object)
object |
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This method checks if intensities are positive and not NA. If not, they are set to 1. Warnings are given if intensities are abnormally high or low (> 50000 or < 5000, respectively).
CNV.data object.
Volker Hovestadt [email protected]
Create annotations for CNV analysis.
CNV.create_anno(bin_minprobes = 15, bin_minsize = 50000, bin_maxsize = 5000000, array_type = "450k", chrXY = FALSE, exclude_regions = NULL, detail_regions = NULL)CNV.create_anno(bin_minprobes = 15, bin_minsize = 50000, bin_maxsize = 5000000, array_type = "450k", chrXY = FALSE, exclude_regions = NULL, detail_regions = NULL)
bin_minprobes |
numeric. Minimum number of probes per bin. Bins are interatively merged with neighboring bin until minimum number is reached. |
bin_minsize |
numeric. Minimum size of a bin. |
bin_maxsize |
numeric. Maximum size of a bin. Merged bins that are larger are filtered out. |
array_type |
character. One of |
chrXY |
logical. Should chromosome X and Y be included in the analysis? |
exclude_regions |
GRanges object or path to bed file containing genomic regions to be excluded. |
detail_regions |
GRanges object or path to bed file containing genomic regions to be examined in detail. |
This function collects all annotations required for CNV analysis using Illumina 450k or EPIC arrays. The output CNV.anno object is not editable. Rerun CNV.create_anno to change parameters.
CNV.anno object.
Volker Hovestadt [email protected]
# create annotation object anno <- CNV.create_anno() anno# create annotation object anno <- CNV.create_anno() anno
Split genome into bins of defined size.
CNV.create_bins(hg19.anno, bin_minsize = 50000, hg19.gap, hg19.exclude)CNV.create_bins(hg19.anno, bin_minsize = 50000, hg19.gap, hg19.exclude)
hg19.anno |
foo |
bin_minsize |
foo |
hg19.gap |
foo |
hg19.exclude |
foo |
GRanges object.
Intensities of one or multiple samples are stored in this class.
## S4 method for signature 'CNV.data' show(object) ## S4 method for signature 'CNV.data,ANY,ANY,ANY' x[i] ## S4 method for signature 'CNV.data' names(x) ## S4 replacement method for signature 'CNV.data' names(x) <- value## S4 method for signature 'CNV.data' show(object) ## S4 method for signature 'CNV.data,ANY,ANY,ANY' x[i] ## S4 method for signature 'CNV.data' names(x) ## S4 replacement method for signature 'CNV.data' names(x) <- value
object |
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x |
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i |
index. |
value |
Replacement names. |
Use CNV.load to create.
CNV.data class.
Volker Hovestadt [email protected]
# create object library(minfiData) data(MsetEx) d <- CNV.load(MsetEx) # general information d show(d) # show or replace sample names names(d) names(d) <- toupper(names(d)) # subset samples d[1:2]# create object library(minfiData) data(MsetEx) d <- CNV.load(MsetEx) # general information d show(d) # show or replace sample names names(d) names(d) <- toupper(names(d)) # subset samples d[1:2]
Combine single probe values within detail regions.
CNV.detail(object, ...) ## S4 method for signature 'CNV.analysis' CNV.detail(object)CNV.detail(object, ...) ## S4 method for signature 'CNV.analysis' CNV.detail(object)
object |
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... |
Additional parameters ( |
The median intensity per detail region is calculated. Detail regions are defined using CNV.create_anno(detail_bed=)
CNV.analysis object.
Volker Hovestadt [email protected]
# prepare library(minfiData) data(MsetEx) d <- CNV.load(MsetEx) data(detail_regions) anno <- CNV.create_anno(detail_regions = detail_regions) # create object x <- CNV.fit(query = d['GroupB_1'], ref = d[c('GroupA_1', 'GroupA_2', 'GroupA_3')], anno) # modify object x <- CNV.bin(x) x <- CNV.detail(x) #x <- CNV.segment(x) # general information x show(x) # coefficients of linear regression coef(x) # show or replace sample name names(x) names(x) <- 'Sample 1'# prepare library(minfiData) data(MsetEx) d <- CNV.load(MsetEx) data(detail_regions) anno <- CNV.create_anno(detail_regions = detail_regions) # create object x <- CNV.fit(query = d['GroupB_1'], ref = d[c('GroupA_1', 'GroupA_2', 'GroupA_3')], anno) # modify object x <- CNV.bin(x) x <- CNV.detail(x) #x <- CNV.segment(x) # general information x show(x) # coefficients of linear regression coef(x) # show or replace sample name names(x) names(x) <- 'Sample 1'
Create CNV plot for detail region.
CNV.detailplot(object, ...) ## S4 method for signature 'CNV.analysis' CNV.detailplot(object, name, yaxt = "l", ylim = c(-1.25, 1.25), set_par = TRUE, cols = c("red", "red", "lightgrey", "green", "green"))CNV.detailplot(object, ...) ## S4 method for signature 'CNV.analysis' CNV.detailplot(object, name, yaxt = "l", ylim = c(-1.25, 1.25), set_par = TRUE, cols = c("red", "red", "lightgrey", "green", "green"))
object |
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... |
Additional parameters ( |
name |
character. Name of detail region to plot. |
yaxt |
character. Include y-axis? |
ylim |
numeric vector. The y limits of the plot. Defaults to |
set_par |
logical. Use recommended graphical parameters for |
cols |
character vector. Colors to use for plotting intensity levels of bins. Centered around 0. Defaults to |
This method provides the functionality for generating detail regions CNV plots. Probes are shown as dots, bins are shown as lines. See parameters for more information.
NULL.
Volker Hovestadt [email protected]
# prepare library(minfiData) data(MsetEx) d <- CNV.load(MsetEx) data(detail_regions) anno <- CNV.create_anno(detail_regions = detail_regions) # create/modify object x <- CNV.segment(CNV.detail(CNV.bin(CNV.fit(query = d['GroupB_1'], ref = d[c('GroupA_1', 'GroupA_2', 'GroupA_3')], anno)))) # output plots CNV.genomeplot(x) CNV.genomeplot(x, chr = 'chr6') CNV.detailplot(x, name = 'PTEN') CNV.detailplot_wrap(x) # output text files CNV.write(x, what = 'segments') CNV.write(x, what = 'detail') CNV.write(x, what = 'bins') CNV.write(x, what = 'probes')# prepare library(minfiData) data(MsetEx) d <- CNV.load(MsetEx) data(detail_regions) anno <- CNV.create_anno(detail_regions = detail_regions) # create/modify object x <- CNV.segment(CNV.detail(CNV.bin(CNV.fit(query = d['GroupB_1'], ref = d[c('GroupA_1', 'GroupA_2', 'GroupA_3')], anno)))) # output plots CNV.genomeplot(x) CNV.genomeplot(x, chr = 'chr6') CNV.detailplot(x, name = 'PTEN') CNV.detailplot_wrap(x) # output text files CNV.write(x, what = 'segments') CNV.write(x, what = 'detail') CNV.write(x, what = 'bins') CNV.write(x, what = 'probes')
Create CNV plot for all detail regions.
CNV.detailplot_wrap(object, ...) ## S4 method for signature 'CNV.analysis' CNV.detailplot_wrap(object, set_par = TRUE, main = NULL, ...)CNV.detailplot_wrap(object, ...) ## S4 method for signature 'CNV.analysis' CNV.detailplot_wrap(object, set_par = TRUE, main = NULL, ...)
object |
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... |
Additional paramters supplied to |
set_par |
logical. Use recommended graphical parameters for |
main |
character. Title of the plot. Defaults to sample name. |
This method is a wrapper of the CNV.detailplot method to plot all detail regions.
NULL.
Volker Hovestadt [email protected]
# prepare library(minfiData) data(MsetEx) d <- CNV.load(MsetEx) data(detail_regions) anno <- CNV.create_anno(detail_regions = detail_regions) # create/modify object x <- CNV.segment(CNV.detail(CNV.bin(CNV.fit(query = d['GroupB_1'], ref = d[c('GroupA_1', 'GroupA_2', 'GroupA_3')], anno)))) # output plots CNV.genomeplot(x) CNV.genomeplot(x, chr = 'chr6') CNV.detailplot(x, name = 'PTEN') CNV.detailplot_wrap(x) # output text files CNV.write(x, what = 'segments') CNV.write(x, what = 'detail') CNV.write(x, what = 'bins') CNV.write(x, what = 'probes')# prepare library(minfiData) data(MsetEx) d <- CNV.load(MsetEx) data(detail_regions) anno <- CNV.create_anno(detail_regions = detail_regions) # create/modify object x <- CNV.segment(CNV.detail(CNV.bin(CNV.fit(query = d['GroupB_1'], ref = d[c('GroupA_1', 'GroupA_2', 'GroupA_3')], anno)))) # output plots CNV.genomeplot(x) CNV.genomeplot(x, chr = 'chr6') CNV.detailplot(x, name = 'PTEN') CNV.detailplot_wrap(x) # output text files CNV.write(x, what = 'segments') CNV.write(x, what = 'detail') CNV.write(x, what = 'bins') CNV.write(x, what = 'probes')
Normalize query sample intensities by fitting intensities to reference set using a linear regression model.
CNV.fit(query, ref, anno, ...) ## S4 method for signature 'CNV.data,CNV.data,CNV.anno' CNV.fit(query, ref, anno, name = NULL, intercept = TRUE)CNV.fit(query, ref, anno, ...) ## S4 method for signature 'CNV.data,CNV.data,CNV.anno' CNV.fit(query, ref, anno, name = NULL, intercept = TRUE)
query |
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ref |
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anno |
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... |
Additional parameters ( |
name |
character. Optional parameter to set query sample name. |
intercept |
logical. Should intercept be considered? Defaults to |
The log2 ratio of query intensities versus a linear combination of reference set intensities that best reflects query intensities is calculated (as determined by linear regression). The annotations provided to CNV.fit are saved within the returned CNV.analysis object and used for subsequent analysis steps.
CNV.analysis object.
Volker Hovestadt [email protected]
# prepare library(minfiData) data(MsetEx) d <- CNV.load(MsetEx) data(detail_regions) anno <- CNV.create_anno(detail_regions = detail_regions) # create object x <- CNV.fit(query = d['GroupB_1'], ref = d[c('GroupA_1', 'GroupA_2', 'GroupA_3')], anno) # modify object #x <- CNV.bin(x) #x <- CNV.detail(x) #x <- CNV.segment(x) # general information x show(x) # coefficients of linear regression coef(x) # show or replace sample name names(x) names(x) <- 'Sample 1'# prepare library(minfiData) data(MsetEx) d <- CNV.load(MsetEx) data(detail_regions) anno <- CNV.create_anno(detail_regions = detail_regions) # create object x <- CNV.fit(query = d['GroupB_1'], ref = d[c('GroupA_1', 'GroupA_2', 'GroupA_3')], anno) # modify object #x <- CNV.bin(x) #x <- CNV.detail(x) #x <- CNV.segment(x) # general information x show(x) # coefficients of linear regression coef(x) # show or replace sample name names(x) names(x) <- 'Sample 1'
Create CNV plot for the whole genome or chromosomes.
CNV.genomeplot(object, ...) ## S4 method for signature 'CNV.analysis' CNV.genomeplot(object, chr = "all", chrX = TRUE, chrY = TRUE, centromere = TRUE, detail = TRUE, main = NULL, ylim = c(-1.25, 1.25), set_par = TRUE, cols = c("red", "red", "lightgrey", "green", "green"))CNV.genomeplot(object, ...) ## S4 method for signature 'CNV.analysis' CNV.genomeplot(object, chr = "all", chrX = TRUE, chrY = TRUE, centromere = TRUE, detail = TRUE, main = NULL, ylim = c(-1.25, 1.25), set_par = TRUE, cols = c("red", "red", "lightgrey", "green", "green"))
object |
|
... |
Additional parameters ( |
chr |
character vector. Which chromomsomes to plot. Defaults to |
chrX |
logical. Plot values for chrX? Defaults to |
chrY |
logical. Plot values for chrY? Defaults to |
centromere |
logical. Show dashed lines at centromeres? Defaults to |
detail |
logical. If available, include labels of detail regions? Defaults to |
main |
character. Title of the plot. Defaults to sample name. |
ylim |
numeric vector. The y limits of the plot. Defaults to |
set_par |
logical. Use recommended graphical parameters for |
cols |
character vector. Colors to use for plotting intensity levels of bins. Centered around 0. Defaults to |
This method provides the functionality for generating CNV plots for the whole genome or defined chromosomes. Bins are shown as dots, segments are shown as lines. See parameters for more information.
NULL.
Volker Hovestadt [email protected]
# prepare library(minfiData) data(MsetEx) d <- CNV.load(MsetEx) data(detail_regions) anno <- CNV.create_anno(detail_regions = detail_regions) # create/modify object x <- CNV.segment(CNV.detail(CNV.bin(CNV.fit(query = d['GroupB_1'], ref = d[c('GroupA_1', 'GroupA_2', 'GroupA_3')], anno)))) # output plots CNV.genomeplot(x) CNV.genomeplot(x, chr = 'chr6') CNV.detailplot(x, name = 'PTEN') CNV.detailplot_wrap(x) # output text files CNV.write(x, what = 'segments') CNV.write(x, what = 'detail') CNV.write(x, what = 'bins') CNV.write(x, what = 'probes')# prepare library(minfiData) data(MsetEx) d <- CNV.load(MsetEx) data(detail_regions) anno <- CNV.create_anno(detail_regions = detail_regions) # create/modify object x <- CNV.segment(CNV.detail(CNV.bin(CNV.fit(query = d['GroupB_1'], ref = d[c('GroupA_1', 'GroupA_2', 'GroupA_3')], anno)))) # output plots CNV.genomeplot(x) CNV.genomeplot(x, chr = 'chr6') CNV.detailplot(x, name = 'PTEN') CNV.detailplot_wrap(x) # output text files CNV.write(x, what = 'segments') CNV.write(x, what = 'detail') CNV.write(x, what = 'bins') CNV.write(x, what = 'probes')
Prepare combined intensities from various input objects.
CNV.load(input, ...) ## S4 method for signature 'GenomicRatioSet' CNV.load(input, names = NULL) ## S4 method for signature 'MethylSet' CNV.load(input, names = NULL) ## S4 method for signature 'data.frame' CNV.load(input, names = NULL) ## S4 method for signature 'matrix' CNV.load(input, names = NULL) ## S4 method for signature 'numeric' CNV.load(input, names = NULL)CNV.load(input, ...) ## S4 method for signature 'GenomicRatioSet' CNV.load(input, names = NULL) ## S4 method for signature 'MethylSet' CNV.load(input, names = NULL) ## S4 method for signature 'data.frame' CNV.load(input, names = NULL) ## S4 method for signature 'matrix' CNV.load(input, names = NULL) ## S4 method for signature 'numeric' CNV.load(input, names = NULL)
input |
Object of MethylSet class (minfi package), data.frame class, matrix class or numeric class. |
... |
Additional parameters ( |
names |
Vector specifying sample names. If not supplied, colnames are used. For MethylSet input, the first column of pData(input) matching 'name' (grep) is used. |
This method gathers combined intensities of the Methylated and Unmethylated signals for all supplied probes. Probe IDs must be supplied as row names or in a seperate column named 'ID_REF' or 'TargetID'. If column names match 'intensity', only those columns are used. Else, if column names match 'signal' or 'methylated', only those columns are used. Otherwise, all columns are used.
CNV.data object.
Volker Hovestadt [email protected]
library(minfiData) d <- CNV.load(MsetEx) dlibrary(minfiData) d <- CNV.load(MsetEx) d
Merge bins containing less than the defined number probes with neighboring bin containing fewer probes.
CNV.merge_bins(hg19.anno, hg19.tile, bin_minprobes = 20, hg19.probes, bin_maxsize = 5e+06, verbose = FALSE)CNV.merge_bins(hg19.anno, hg19.tile, bin_minprobes = 20, hg19.probes, bin_maxsize = 5e+06, verbose = FALSE)
hg19.anno |
foo |
hg19.tile |
foo |
bin_minprobes |
foo |
hg19.probes |
foo |
bin_maxsize |
foo |
verbose |
foo |
GRanges object.
Given a case index, control indices, CNV.data, and CNV.anno, along with hints about sex chromosomes, call CN for a sample.
CNV.process(case, controls, CNdata, anno) ## S4 method for signature 'integer,integer,CNV.data,CNV.anno' CNV.process(case, controls, CNdata, anno)CNV.process(case, controls, CNdata, anno) ## S4 method for signature 'integer,integer,CNV.data,CNV.anno' CNV.process(case, controls, CNdata, anno)
case |
index of the case to process CN for. |
controls |
indices of the control samples. |
CNdata |
|
anno |
|
This method wraps most of conumee, and tries to call sex chromosomes properly using chrX/chrY information derived from the source GenomicRatioSet. For female subjects, chrY is dropped.
CNV.analysis object.
Tim Triche, Jr. [email protected]
Segment bin values (wrapper of DNAcopy package).
CNV.segment(object, ...) ## S4 method for signature 'CNV.analysis' CNV.segment(object, alpha = 0.001, nperm = 50000, min.width = 5, undo.splits = "sdundo", undo.SD = 2.2, verbose = 0, ...)CNV.segment(object, ...) ## S4 method for signature 'CNV.analysis' CNV.segment(object, alpha = 0.001, nperm = 50000, min.width = 5, undo.splits = "sdundo", undo.SD = 2.2, verbose = 0, ...)
object |
|
... |
Additional parameters supplied to the |
alpha |
See details. Defaults to 0.001. |
nperm |
See details. Defaults to 50000. |
min.width |
See details. Defaults to 5. |
undo.splits |
See details. Defaults to 'sdundo'. |
undo.SD |
See details. Defaults to 2.2. |
verbose |
See details. Defaults to 0. |
This method is a wrapper of the CNA, segment, segments.summary and segments.p methods of the DNAcopy package. Please refer to the respective man pages for more detailed information. The default parameters of CNV.segment override some of the default parameters of segment and are optimized for 450k data CNV analysis.
CNV.analysis object.
Volker Hovestadt [email protected]
# prepare library(minfiData) data(MsetEx) d <- CNV.load(MsetEx) data(detail_regions) anno <- CNV.create_anno(detail_regions = detail_regions) # create object x <- CNV.fit(query = d['GroupB_1'], ref = d[c('GroupA_1', 'GroupA_2', 'GroupA_3')], anno) # modify object x <- CNV.bin(x) x <- CNV.detail(x) x <- CNV.segment(x) # general information x show(x) # coefficients of linear regression coef(x) # show or replace sample name names(x) names(x) <- 'Sample 1'# prepare library(minfiData) data(MsetEx) d <- CNV.load(MsetEx) data(detail_regions) anno <- CNV.create_anno(detail_regions = detail_regions) # create object x <- CNV.fit(query = d['GroupB_1'], ref = d[c('GroupA_1', 'GroupA_2', 'GroupA_3')], anno) # modify object x <- CNV.bin(x) x <- CNV.detail(x) x <- CNV.segment(x) # general information x show(x) # coefficients of linear regression coef(x) # show or replace sample name names(x) names(x) <- 'Sample 1'
Output CNV analysis results as table.
CNV.write(object, ...) ## S4 method for signature 'CNV.analysis' CNV.write(object, file = NULL, what = "segments")CNV.write(object, ...) ## S4 method for signature 'CNV.analysis' CNV.write(object, file = NULL, what = "segments")
object |
|
... |
Additional parameters ( |
file |
Path where output file should be written to. Defaults to |
what |
character. This should be (an unambiguous abbreviation of) one of |
if parameter file is not supplied, the table is returned as a data.frame object.
# prepare library(minfiData) data(MsetEx) d <- CNV.load(MsetEx) data(detail_regions) anno <- CNV.create_anno(detail_regions = detail_regions) # create/modify object x <- CNV.segment(CNV.detail(CNV.bin(CNV.fit(query = d['GroupB_1'], ref = d[c('GroupA_1', 'GroupA_2', 'GroupA_3')], anno)))) # output plots CNV.genomeplot(x) CNV.genomeplot(x, chr = 'chr6') CNV.detailplot(x, name = 'PTEN') CNV.detailplot_wrap(x) # output text files CNV.write(x, what = 'segments') CNV.write(x, what = 'detail') CNV.write(x, what = 'bins') CNV.write(x, what = 'probes')# prepare library(minfiData) data(MsetEx) d <- CNV.load(MsetEx) data(detail_regions) anno <- CNV.create_anno(detail_regions = detail_regions) # create/modify object x <- CNV.segment(CNV.detail(CNV.bin(CNV.fit(query = d['GroupB_1'], ref = d[c('GroupA_1', 'GroupA_2', 'GroupA_3')], anno)))) # output plots CNV.genomeplot(x) CNV.genomeplot(x, chr = 'chr6') CNV.detailplot(x, name = 'PTEN') CNV.detailplot_wrap(x) # output text files CNV.write(x, what = 'segments') CNV.write(x, what = 'detail') CNV.write(x, what = 'bins') CNV.write(x, what = 'probes')
Example of genomic regions to be analyzed in detail (e.g. candidate oncogenes/TSGs).
Imported using rtracklayer. Raw data stored in inst/extdata/detail_regions.bed.
Volker Hovestadt [email protected]
Example of genomic regions to exclude (e.g. known polymorphic regions).
Imported using rtracklayer. Raw data stored in inst/extdata/exclude_regions.bed.
Volker Hovestadt [email protected]
Read IDAT files from the web.
read.450k.url(url = NULL, idat = NULL)read.450k.url(url = NULL, idat = NULL)
url |
URL of the directory in which the IDAT files are located. |
idat |
Vector of IDAT names. |
This method downloads the provided list of IDAT files to a temporary folder (using the RCurl package). It then uses the 'read.450k.exp' method of the 'minfi' package.
RGChannelSet object.
Volker Hovestadt [email protected]
RGsetTCGA <- read.450k.url()RGsetTCGA <- read.450k.url()
UCSC tables required for creating annotation object.
Imported using rtracklayer::browserSession('UCSC'): chromInfo, gap, cytoBand.
Volker Hovestadt [email protected]
Named vector for Sentrix ID to TCGA ID conversion of breast cancer example data (see README).
Based on https://tcga-data.nci.nih.gov/tcgafiles/ftp_auth/distro_ftpusers/anonymous/tumor/brca/cgcc/jhu-usc.edu/humanmethylation450/methylation/jhu-usc.edu_BRCA.HumanMethylation450.aux.1.8.0/BRCA.mappings.csv.
Volker Hovestadt [email protected]