Introduction_4_process_pgxseg (original) (raw)
Contents
- 1 Load library
- 2 Extract segment data
- 3 Extract metadata
- 4 Visualize survival data in metadata
- 5 Calculate CNV frequency
- 6 Extract all data
- 7 Session Info
Progenetix is an open data resource that provides curated individual cancer copy number variation (CNV) profiles along with associated metadata sourced from published oncogenomic studies and various data repositories. Progenetix uses the “.pgxseg” data format to store variant data, which encompasses CNV (Copy Number Variation) and SNV (Single Nucleotide Variant), as well as the metadata of associated samples. This vignette describes how to work with local “.pgxseg” files using this package. For more details about the “.pgxseg” file format, please refer to the the documentation.
Load library
library(pgxRpi)
library(GenomicRanges) # for pgxfreq objectpgxSegprocess function
This function extracts segment variants, CNV frequency, and metadata from local “pgxseg” files. Additionally, it supports survival data visualization if survival data is available within the file.
The parameters of this function used in this tutorial:
file: A string specifying the path and name of the “pgxseg” file where the data is to be read.group_id: A string specifying which id is used for grouping in KM plot or CNV frequency calculation. Default is"group_id".show_KM_plot: A logical value determining whether to return the Kaplan-Meier plot based on metadata. Default isFALSE.return_metadata: A logical value determining whether to return metadata. Default isFALSE.return_seg: A logical value determining whether to return segment data. Default isFALSE.return_frequency: A logical value determining whether to return CNV frequency data. The frequency calculation is based on segments in segment data and specified group id in metadata. Default isFALSE.cnv_column_idx: Index of the column specifying the CNV state used for calculating CNV frequency. The index must be at least 6, with the default set to6. The CNV states should either contain “DUP” for duplications and “DEL” for deletions, or level-specific CNV states represented using Experimental Factor Ontology (EFO) codes.assembly: A string specifying the genome assembly version to apply to CNV frequency calculation and plotting. Allowed options are “hg19” and “hg38”. Default is"hg38"....Other parameters relevant to KM plot. These includepval,pval.coord,pval.method,conf.int,linetype, andpalette(see ggsurvplot function from survminer package)
Calculate CNV frequency
The CNV frequency is calculated from segments of samples with the same group id. The group id is specified in group_id parameter. More details about CNV frequency see the vignette Introduction_3_access_cnv_frequency.
# Default is "group_id" in metadata
frequency <- pgxSegprocess(file=file_name,return_frequency = TRUE)
# Use different ids for grouping
frequency_2 <- pgxSegprocess(file=file_name,return_frequency = TRUE,
group_id ='icdo_morphology_id')
frequency
#> GRangesList object of length 4:
#> $`pgx:icdot-C16.9`
#> GRanges object with 3106 ranges and 2 metadata columns:
#> seqnames ranges strand | gain_frequency loss_frequency
#> <Rle> <IRanges> <Rle> | <numeric> <numeric>
#> [1] 1 0-400000 * | 0 28.5714
#> [2] 1 400000-1400000 * | 0 28.5714
#> [3] 1 1400000-2400000 * | 0 28.5714
#> [4] 1 2400000-3400000 * | 0 28.5714
#> [5] 1 3400000-4400000 * | 0 28.5714
#> ... ... ... ... . ... ...
#> [3102] Y 52400000-53400000 * | 0 0
#> [3103] Y 53400000-54400000 * | 0 0
#> [3104] Y 54400000-55400000 * | 0 0
#> [3105] Y 55400000-56400000 * | 0 0
#> [3106] Y 56400000-57227415 * | 0 0
#> -------
#> seqinfo: 24 sequences from an unspecified genome; no seqlengths
#>
#> ...
#> <3 more elements>The returned object is same as the CNV frequency object with “pgxfreq” format returned by pgxLoader function. The CNV frequency is calculated from groups which exist in both metadata and segment data. It is noted that not all groups in metadata must exist in segment data (e.g. some samples don’t have CNV calls).
head(frequency[["pgx:icdot-C16.9"]])
#> GRanges object with 6 ranges and 2 metadata columns:
#> seqnames ranges strand | gain_frequency loss_frequency
#> <Rle> <IRanges> <Rle> | <numeric> <numeric>
#> [1] 1 0-400000 * | 0 28.5714
#> [2] 1 400000-1400000 * | 0 28.5714
#> [3] 1 1400000-2400000 * | 0 28.5714
#> [4] 1 2400000-3400000 * | 0 28.5714
#> [5] 1 3400000-4400000 * | 0 28.5714
#> [6] 1 4400000-5400000 * | 0 28.5714
#> -------
#> seqinfo: 24 sequences from an unspecified genome; no seqlengthsThe associated metadata in CNV frequency objects looks like this
mcols(frequency)
#> DataFrame with 4 rows and 3 columns
#> group_id group_label sample_count
#> <character> <character> <integer>
#> pgx:icdot-C16.9 pgx:icdot-C16.9 stomach 28
#> pgx:icdot-C73.9 pgx:icdot-C73.9 thyroid gland 6
#> pgx:icdot-C50.9 pgx:icdot-C50.9 breast 2
#> pgx:icdot-C49.9 pgx:icdot-C49.9 connective and soft .. 7mcols(frequency_2)
#> DataFrame with 3 rows and 3 columns
#> group_id group_label sample_count
#> <character> <character> <integer>
#> pgx:icdom-89363 pgx:icdom-89363 Gastrointestinal str.. 35
#> pgx:icdom-83353 pgx:icdom-83353 Follicular carcinoma 6
#> pgx:icdom-80753 pgx:icdom-80753 Squamous cell carcin.. 2You can visualize the CNV frequency of the interesting group using pgxFreqplot function. For more details on this function, see the vignette Introduction_3_access_cnv_frequency.
pgxFreqplot(frequency, filters="pgx:icdot-C16.9")pgxFreqplot(frequency, filters="pgx:icdot-C16.9",chrom = c(1,8,14), layout = c(3,1))pgxFreqplot(frequency, filters=c("pgx:icdot-C16.9","pgx:icdot-C73.9"),circos = TRUE)Session Info
#> R version 4.5.0 RC (2025-04-04 r88126)
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