estimate_nkr: Estimate CNA "normal" karyotype ranges
This generic function is used to run normal karyotype range estimation.
This function estimates the normal karyotype range for each feature in the annotation file. It creates an .RData object for each feature, which is placed in the output directory under /nkr. This intermediate output is used in estimate_feature_score .
This function estimates the normal karyotype range for each feature in the annotation file. It creates an .RData object for each feature, which is placed in the output directory under /nkr. This intermediate output is used in estimate_feature_score .
estimate_nkr(obj, ...) ## Default S3 method: estimate_nkr( obj = NULL, df = NULL, sample.names = NULL, ano.file, out.dir = NULL, ncpus = 1, file.ci.coverage = NULL, nkr = 0.9, x.norm = NULL, norm.cov.matrix = NULL, verbose = FALSE, ... ) ## S3 method for class 'RCNA_object' estimate_nkr(obj, verbose = FALSE, ...)
obj: A RCNA_object type object - parameters will be pulled from the object instead, specifically from the nkrParams slot....: Additional arguments (unused)df: Path to the config file, or a data.frame object containing the valid parameters. Valid column names are file.nkr.coverage, x.norm, and sample.names. Additional columns will be ignored.sample.names: Character vector of sample names. Alternatively can be specified in df.ano.file: Location of the annotation file. This file must be in CSV format and contain the following information (with column headers as specified): "feature,chromosome,start,end".out.dir: Output directory for results. A subdirectory for results will be created under this + /nkr/.ncpus: Integer number of CPUs to use. Specifying more than one allows this function to be parallelized by feature.file.ci.coverage: Character vector listing the input coverage files. Must be the same length as sample.names. Alternatively can be specified in df.nkr: Numeric between 0 and 1 which specifies the coverage quantile that should be considered a "normal" karyotype range for each position. Lowering this value may increase sensitivity but also Type I error.x.norm: Whether or not to perform normalization for normal female/XX karyotype (default = FALSE). Can be specified for each sample separately via df column labeled x.norm.norm.cov.matrix: Character file path detailing the location of the normalized coverage matrix generated by this function. Re-using this file between runs can cut down on runtime significantly for large sample sizes. If the file doesn't exist yet it will be created at this location. If this file name ends in ".gz" then the output will be compressed using gzip.verbose: If set to TRUE will display more detailA RCNA_analysis class object that describes the input parameters and output files generated by this step of the workflow.
A RCNA_analysis class object that describes the input parameters and output files generated by this step of the workflow.
A RCNA_analysis class object that describes the input parameters and output files generated by this step of the workflow.
This function can be run as a stand-alone or as part of run_RCNA
The df argument corresponds to the nkrParams matrix on RCNA_object . Valid column names are sample.names, file.ci.coverage, and x.norm. Additional columns will be ignored.
For more parameter information, see estimate_nkr.default .
## Run NKR estimation on example object # See \link{example_obj} for more information on example example_obj@ano.file <- system.file("examples" ,"annotations-example.csv", package = "RCNA") example_obj # Create output directory dir.create(file.path("output", "nkr"), recursive = TRUE) # Copy example GC-corrected coverage files cov.corrected <- system.file("examples", "gc", package = "RCNA") file.copy(from = cov.corrected, to = "output", recursive = TRUE) # Run NKR estimation, append results estimate_nkr_analysisObj <- estimate_nkr(example_obj) example_obj@commands <- c(example_obj@commands, estimate_nkr_analysisObj)
RCNA_object , RCNA_analysis , run_RCNA
Useful links