NGScopy-class: Detection of copy number variations in next generation sequencing (NGS)

Description

NGScopy is a reference class to detect copy number variations by ``restriction-imposed windowing'' in next generation sequencing (NGS).

Arguments

Fields

inFpathN: character, the file path to the control (normal) sample
inFpathT: character, the file path to the case (tumor) sample
outFpre: character, the file path of the directory for output.
libsizeN: numeric, the library size of the control (normal) sample.
libsizeT: numeric, the library size of the case (tumor) sample.
mindepth: numeric, the minimal depth of reads per window.
minsize: numeric, the minimal size of a window.
regions: data.frame of three columns (chr/start/end), the regions to study. It follows the BED format: zero-based, half-open; (start,end].
segtype: character, the type of change to capture during segmentation, mean and/or variance, normal or nonparametric distributions. A character vector with a single or multiple values from c("mean.norm","meanvar.norm","mean.cusum","var.css"). see changepoint.
dsN: integer, the downsampling factor of the control (normal) sample.
dsT: integer, the downsampling factor of the case (tumor) sample.
MoreArgs.cn: list, a list of arguments for method `calc_cn'. See set_MoreArgs.cn.
MoreArgs.segm: list, a list of arguments for method `calc_segm'. See set_MoreArgs.segm.
pcThreads: integer, the number of processors performing the parallel computing.
auto.save: logical, whether to save (any completed results) automatically.
auto.load: logical, whether to load (any previously completed results) automatically.
force.rerun: character, the names of methods to rerun regardless of any previous runs, default to c().
out: list, the output.

Methods

calc_cn(): Calculate the relative copy number ratios (CNRs) per window, using the depth in the control (normal) sample as denominator and the case (tumor) sample as numerator.
calc_segm(): Calculate the segment and detect the change points.
get_cn(): Get the copy number object.
get_cnr(): Get the copy number ratios.
get_data.cn(as.granges = FALSE): Get the data.frame of copy number.
get_dataN(): Get the data of the normal per window.
get_data.segm(as.granges = FALSE): Get the data.frame of segmentation.
get_dataT(): Get the data of the normal per window.
get_depthN(): Get the depth of the normal per window.
get_depthT(): Get the depth of the tumor per window.
get_dsN(): Get dsN
get_dsT(): Get dsT
get_inFpathN(): Get inFpathN
get_inFpathT(): Get inFpathT
get_libsizeN(): Get libsizeN
get_libsizeT(): Get libsizeT
get_mindepth(): Get mindepth
get_minsize(): Get minsize
get_MoreArgs.cn(): Get MoreArgs.cn
get_MoreArgs.segm(): Get MoreArgs.segm
get_outFpre(): Get outFpre
get_pcThreads(): Get pcThreads
get_pos(): Get the position (midpoint) per window.
get_reflength(): Get reference genome length in the normal sample.
get_refname(): Get reference genome name in the normal sample.
get_regions(): Get regions.
get_segm(): Get the segmentation object.
get_segmtype(): Get segtype, segmentation type(s).
get_size(): Get the size per window.
get_windows(): Get the windows.
loadme(): Load a previously saved output.
load_normal(normalDpath): Load a previously saved output of the normal. normalDpath: the path to the .RData file for the output of the normal.
plot_out(pdfFpath, width, height, scales, xlim, ylim, xlab, ylab, ..., MoreArgs.plotcn, MoreArgs.plotseg): Plot the output and save to a pdf file. pdfFpath: a file path (relative to outFpre) for the pdf output; width,height: see `grDevices::pdf'; scales: are scales shared across all chromossomes (i.e. x coordinates reflect the range of genomic coordinates per chromosome, y coordinates reflect the range of of CNRs per chromosome), given no specific `xlim' and `ylim' (the default, "fixed"), or do they vary across x coordinates ("free_x"), y coordinates ("free_y"), or both ("free"); xlim,ylim,xlab,ylab,...: see `graphics::plot'; MoreArgs.plotcn: additional arguments as in `graphics::points'; MoreArgs.plotseg: additional arguments as in `graphics::segments'.
proc_cn(): Process the output of coy number object and return as a data.frame.
proc_normal(): Process the normal sample: make the windows and count the reads per window.
proc_segm(): Process the output of segmentation object and return as a data.frame.
proc_tumor(): Process the tumor sample: count the reads per window.
save_normal(): Get the output of the normal for later usage.
set_ds(dsN, dsT): Set downsampling factors. See `set_dsN', `set_dsT'.
set_dsN(dsN): Set downsampling factor of the control (normal). dsN: numeric, the library size of the control (normal) sample.
set_dsT(dsT): Set downsampling factor of the case (tumor). dsT: numeric, the library size of the case (tumor) sample.
set_force.rerun(force.rerun): Set force.rerun. Reset it with missing input.
set_inFpathN(inFpathN): Set a control (normal) sample. inFpathN: The file path to the control (normal) sample.
set_inFpathT(inFpathT): Set a case (tumor) sample. inFpathT: The file path to the case (tumor) sample.
set_libsize(libsizeN, libsizeT): Set library sizes. See `set_libsizeN', `set_libsizeT'.
set_libsizeN(libsizeN): Set library size of the control (normal). libsizeN: numeric, the library size of the control (normal) sample.
set_libsizeT(libsizeT): Set library size of the case (tumor). libsizeT: numeric, the library size of the case (tumor) sample.
set_mindepth(mindepth): Set the minimal depth per window. mindepth: the minimal depth of reads per window in the control (normal) sample.
set_minsize(minsize): Set the minimal size per window. minsize: the minimal size of a window in the control (normal) sample.
set_MoreArgs.cn(...): Set MoreArgs.cn. ..., (pseudocount: the pseudocounts added to the observed depth per window, default to 1; logr: logical, whether to make log2 transformation of the ratios, default to TRUE.)
set_MoreArgs.segm(...): Set MoreArgs.segm. ..., a list of other arguments to the funciton of segmentation given by segtype. See `help_segmtype'.
set_normal(inFpathN, mindepth, minsize): Set a control (normal) sample and minimal depth/size per window. See `set_inFpathN', `set_mindepth', `set_minsize'.
set_outFpre(outFpre): Set a directory for output. outFpre: the file path of the directory for output.
set_pcThreads(pcThreads): Set the number of processors. pcThreads: numeric, the number of processors performing the parallel computing. It should not exceed the system's capacity.
set_regions(regions): Set regions. regions: the regions in study, matrix, data.frame, character, file or connection with three columns (chr/start/end).
set_segmtype(segtype): Set the type of segmentation. segtype: a character vector with a single or multiple values from c("mean.norm","meanvar.norm","mean.cusum","var.css"). See `changepoint'.
set_tumor(inFpathT): Set a case (tumor) sample. See `set_inFpathT'.
write_cn(cnFpath, ...): Write the output of copy numbers as a data.frame to a tab separated file. cnFpath: a file path (relative to outFpre) for `cn' output; ...: see `Xmisc::write.data.table'.
write_segm(segFpath, ...): Write the output of segments as a data.frame to a tab separated file. segFpath: a file path (relative to outFpre) for `seg' output; ...: see `Xmisc::write.data.table'.

Details

Detection of copy number variations in next generation sequencing (NGS)

Examples

Run this code

require(NGScopy)
require(NGScopyData)

## Create an instance of `NGScopy' class
obj <- NGScopy$new(
  outFpre="ngscopy-case1",         # specified directory for output
  inFpathN=tps_N8.chr6()$bamFpath, # normal sample: tps_90.chr6.sort.bam
  inFpathT=tps_90.chr6()$bamFpath, # tumor sample:  tps_N8.chr6.sort.bam
  libsizeN=5777087,                # the library size of the normal sample
  libsizeT=4624267,                # the library size of the tumor sample
  mindepth=20,                     # the minimal depth of reads per window
  minsize=20000,                   # the minimal size of a window
  pcThreads=1                      # the number of processors for computing
  )

obj$show()                         # print the instance

## Not run: 
# 
# ## Compute the copy number and save it
# ## A data.frame will be saved to file `ngscopy_cn.txt' in the output directory
# obj$write_cn()
# 
# ## Compute the segmentation and save it
# ## A data.frame will be saved to file `ngscopy_segm.txt' in the output directory
# obj$write_segm()
# 
# ## Visualization
# ## A figure will be saved to file `ngscopy_out.pdf' in the output directory
# obj$plot_out()
# ## End(Not run)