Introduction_3_loadfrequency
2024-05-03
Contents
Progenetix is an open data resource that provides curated individual cancer copy number aberrations (CNA) profiles along with associated metadata sourced from published oncogenomic studies and various data repositories. This vignette offers a comprehensive guide on accessing and visualizing CNV frequency data within the Progenetix database. CNV frequency is pre-calculated based on CNV segment data in Progenetix and reflects the CNV pattern in a cohort. It is defined as the percentage of samples showing a CNV for a genomic region (1MB-sized genomic bins in this case) over the total number of samples in a cohort specified by filters. If your focus lies in cancer cell lines, you can access data from cancercelllines.org by specifying the dataset parameter as “cancercelllines”. This data repository originates from CNV profiling data of cell lines initially collected as part of Progenetix and currently includes additional types of genomic mutations.
1 Load library
library(pgxRpi)
library(SummarizedExperiment) # for pgxmatrix data
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#>
#> Attaching package: 'MatrixGenerics'
#> The following objects are masked from 'package:matrixStats':
#>
#> colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#> colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#> colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#> colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#> colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#> colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#> colWeightedMeans, colWeightedMedians, colWeightedSds,
#> colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#> rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#> rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#> rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#> rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#> rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#> rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#> rowWeightedSds, rowWeightedVars
#> Loading required package: GenomicRanges
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#>
#> Attaching package: 'BiocGenerics'
#> The following objects are masked from 'package:stats':
#>
#> IQR, mad, sd, var, xtabs
#> The following objects are masked from 'package:base':
#>
#> Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
#> as.data.frame, basename, cbind, colnames, dirname, do.call,
#> duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
#> lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
#> pmin.int, rank, rbind, rownames, sapply, setdiff, table, tapply,
#> union, unique, unsplit, which.max, which.min
#> Loading required package: S4Vectors
#>
#> Attaching package: 'S4Vectors'
#> The following object is masked from 'package:utils':
#>
#> findMatches
#> The following objects are masked from 'package:base':
#>
#> I, expand.grid, unname
#> Loading required package: IRanges
#> Loading required package: GenomeInfoDb
#> Loading required package: Biobase
#> Welcome to Bioconductor
#>
#> Vignettes contain introductory material; view with
#> 'browseVignettes()'. To cite Bioconductor, see
#> 'citation("Biobase")', and for packages 'citation("pkgname")'.
#>
#> Attaching package: 'Biobase'
#> The following object is masked from 'package:MatrixGenerics':
#>
#> rowMedians
#> The following objects are masked from 'package:matrixStats':
#>
#> anyMissing, rowMedians
library(GenomicRanges) # for pgxfreq data1.1 pgxLoader function
This function loads various data from Progenetix database.
The parameters of this function used in this tutorial:
typeA string specifying output data type. Available options are “biosample”, “individual”, “variant” or “frequency”.outputA string specifying output file format. When the parametertypeis “frequency”, available options are “pgxfreq” or “pgxmatrix” .filtersIdentifiers for cancer type, literature, cohorts, and age such as c(“NCIT:C7376”, “pgx:icdom-98353”, “PMID:22824167”, “pgx:cohort-TCGAcancers”, “age:>=P50Y”). For more information about filters, see the documentation.codematchesA logical value determining whether to exclude samples from child concepts of specified filters that belong to cancer type/tissue encoding system (NCIt, icdom/t, Uberon). If TRUE, retrieved samples only keep samples exactly encoded by specified filters. Do not use this parameter whenfiltersinclude cancer-irrelevant filters such as PMID and cohort identifiers. Default is FALSE.datasetA string specifying the dataset to query. Default is “progenetix”. Other available options are “cancercelllines”.
2 Retrieve CNV frequency data
2.1 Relevant parameters
type, output, filters, codematches, dataset
2.2 The first output format (output = “pgxfreq”)
freq_pgxfreq <- pgxLoader(type="frequency", output ="pgxfreq",
filters=c("NCIT:C4038","pgx:icdom-85003"))
freq_pgxfreq
#> GRangesList object of length 2:
#> $`NCIT:C4038`
#> GRanges object with 3106 ranges and 3 metadata columns:
#> seqnames ranges strand | gain_frequency loss_frequency
#> <Rle> <IRanges> <Rle> | <numeric> <numeric>
#> [1] 1 0-400000 * | 0.625 1.875
#> [2] 1 400000-1400000 * | 1.250 8.750
#> [3] 1 1400000-2400000 * | 2.500 9.375
#> [4] 1 2400000-3400000 * | 2.500 13.750
#> [5] 1 3400000-4400000 * | 3.125 14.375
#> ... ... ... ... . ... ...
#> [3102] Y 52400000-53400000 * | 0 0.625
#> [3103] Y 53400000-54400000 * | 0 0.625
#> [3104] Y 54400000-55400000 * | 0 0.625
#> [3105] Y 55400000-56400000 * | 0 0.625
#> [3106] Y 56400000-57227415 * | 0 0.625
#> no
#> <integer>
#> [1] 1
#> [2] 2
#> [3] 3
#> [4] 4
#> [5] 5
#> ... ...
#> [3102] 3102
#> [3103] 3103
#> [3104] 3104
#> [3105] 3105
#> [3106] 3106
#> -------
#> seqinfo: 24 sequences from an unspecified genome; no seqlengths
#>
#> $`pgx:icdom-85003`
#> GRanges object with 3106 ranges and 3 metadata columns:
#> seqnames ranges strand | gain_frequency loss_frequency
#> <Rle> <IRanges> <Rle> | <numeric> <numeric>
#> [1] 1 0-400000 * | 7.333 6.041
#> [2] 1 400000-1400000 * | 9.644 11.995
#> [3] 1 1400000-2400000 * | 7.437 14.554
#> [4] 1 2400000-3400000 * | 9.564 25.883
#> [5] 1 3400000-4400000 * | 7.373 24.655
#> ... ... ... ... . ... ...
#> [3102] Y 52400000-53400000 * | 0.056 1.051
#> [3103] Y 53400000-54400000 * | 0.056 1.051
#> [3104] Y 54400000-55400000 * | 0.056 1.051
#> [3105] Y 55400000-56400000 * | 0.056 1.051
#> [3106] Y 56400000-57227415 * | 0.064 1.059
#> no
#> <integer>
#> [1] 1
#> [2] 2
#> [3] 3
#> [4] 4
#> [5] 5
#> ... ...
#> [3102] 3102
#> [3103] 3103
#> [3104] 3104
#> [3105] 3105
#> [3106] 3106
#> -------
#> seqinfo: 24 sequences from an unspecified genome; no seqlengthsThe returned data is stored in GRangesList container which consists of multiple GRanges objects. Each GRanges object stores CNV frequency from samples pecified by a particular filter. Within each GRanges object, you can find annotation columns “gain_frequency” and “loss_frequency” in each row, which express the percentage values across samples (%) for gains and losses that overlap the corresponding genomic interval.
These genomic intervals are derived from the partitioning of the entire genome (GRCh38). Most of these bins have a size of 1MB, except for a few bins located near the telomeres. In total, there are 3106 intervals encompassing the genome.
To access the CNV frequency data from specific filters, you could access like this
freq_pgxfreq[["NCIT:C4038"]]
#> GRanges object with 3106 ranges and 3 metadata columns:
#> seqnames ranges strand | gain_frequency loss_frequency
#> <Rle> <IRanges> <Rle> | <numeric> <numeric>
#> [1] 1 0-400000 * | 0.625 1.875
#> [2] 1 400000-1400000 * | 1.250 8.750
#> [3] 1 1400000-2400000 * | 2.500 9.375
#> [4] 1 2400000-3400000 * | 2.500 13.750
#> [5] 1 3400000-4400000 * | 3.125 14.375
#> ... ... ... ... . ... ...
#> [3102] Y 52400000-53400000 * | 0 0.625
#> [3103] Y 53400000-54400000 * | 0 0.625
#> [3104] Y 54400000-55400000 * | 0 0.625
#> [3105] Y 55400000-56400000 * | 0 0.625
#> [3106] Y 56400000-57227415 * | 0 0.625
#> no
#> <integer>
#> [1] 1
#> [2] 2
#> [3] 3
#> [4] 4
#> [5] 5
#> ... ...
#> [3102] 3102
#> [3103] 3103
#> [3104] 3104
#> [3105] 3105
#> [3106] 3106
#> -------
#> seqinfo: 24 sequences from an unspecified genome; no seqlengthsTo get metadata such as count of samples used to calculate frequency, use mcols function from GenomicRanges package:
mcols(freq_pgxfreq)
#> DataFrame with 2 rows and 3 columns
#> filter label sample_count
#> <character> <character> <numeric>
#> NCIT:C4038 NCIT:C4038 Lung Carcinoid Tumor 160
#> pgx:icdom-85003 pgx:icdom-85003 Infiltrating duct ca.. 12464The parameter codematches determines whether the calculation of CNV frequency excludes samples from child terms.
2.3 The second output format (output = “pgxmatrix”)
Choose 8 NCIT codes of interests that correspond to different tumor types
code <-c("C3059","C3716","C4917","C3512","C3493","C3771","C4017","C4001")
# add prefix for query
code <- sub(".",'NCIT:C',code)load data with the specified code
freq_pgxmatrix <- pgxLoader(type="frequency",output ="pgxmatrix",filters=code)
freq_pgxmatrix
#> class: RangedSummarizedExperiment
#> dim: 6212 8
#> metadata(0):
#> assays(1): frequency
#> rownames(6212): 1 2 ... 6211 6212
#> rowData names(1): type
#> colnames(8): NCIT:C3059 NCIT:C3493 ... NCIT:C4017 NCIT:C4917
#> colData names(3): filter label sample_countThe returned data is stored in RangedSummarizedExperiment object, which is a matrix-like container where rows represent ranges of interest (as a GRanges object) and columns represent filters.
To get metadata such as count of samples used to calculate frequency, use colData function from SummarizedExperiment package:
colData(freq_pgxmatrix)
#> DataFrame with 8 rows and 3 columns
#> filter label sample_count
#> <character> <character> <numeric>
#> NCIT:C3059 NCIT:C3059 Glioma 8183
#> NCIT:C3493 NCIT:C3716 Lung Squamous Cell C.. 1938
#> NCIT:C3512 NCIT:C4917 Lung Adenocarcinoma 4664
#> NCIT:C3716 NCIT:C3512 Primitive Neuroectod.. 2214
#> NCIT:C3771 NCIT:C3493 Breast Lobular Carci.. 904
#> NCIT:C4001 NCIT:C3771 Breast Inflammatory .. 27
#> NCIT:C4017 NCIT:C4017 Breast Ductal Carcin.. 10183
#> NCIT:C4917 NCIT:C4001 Lung Small Cell Carc.. 558To access the CNV frequency matrix, use assay accesssor from SummarizedExperiment package
head(assay(freq_pgxmatrix))
#> NCIT:C3059 NCIT:C3493 NCIT:C3512 NCIT:C3716 NCIT:C3771 NCIT:C4001 NCIT:C4017
#> 1 3.410 5.315 1.951 3.568 0.996 3.704 8.720
#> 2 8.457 8.978 6.089 7.678 2.434 3.704 11.166
#> 3 10.644 6.553 5.832 8.988 2.655 3.704 8.642
#> 4 11.964 10.475 12.693 8.762 4.204 3.704 10.734
#> 5 12.440 8.824 11.342 9.033 3.761 3.704 8.279
#> 6 9.068 8.359 10.828 7.949 2.655 7.407 5.755
#> NCIT:C4917
#> 1 11.470
#> 2 25.448
#> 3 24.373
#> 4 29.032
#> 5 27.419
#> 6 27.240The matrix has 6212 rows (genomic regions) and 8 columns (filters). The rows comprised 3106 intervals with “gain status” plus 3106 intervals with “loss status”.
The value is the percentage of samples from the corresponding filter having one or more CNV events in the
specific genomic intervals. You could get the interval information by rowRanges function from SummarizedExperiment package
rowRanges(freq_pgxmatrix)
#> GRanges object with 6212 ranges and 1 metadata column:
#> seqnames ranges strand | type
#> <Rle> <IRanges> <Rle> | <character>
#> 1 1 0-400000 * | DUP
#> 2 1 400000-1400000 * | DUP
#> 3 1 1400000-2400000 * | DUP
#> 4 1 2400000-3400000 * | DUP
#> 5 1 3400000-4400000 * | DUP
#> ... ... ... ... . ...
#> 6208 Y 52400000-53400000 * | DEL
#> 6209 Y 53400000-54400000 * | DEL
#> 6210 Y 54400000-55400000 * | DEL
#> 6211 Y 55400000-56400000 * | DEL
#> 6212 Y 56400000-57227415 * | DEL
#> -------
#> seqinfo: 24 sequences from an unspecified genome; no seqlengthsFor example, if the value in the second row and first column is 8.457, it means that 8.457% samples from the corresponding filter NCIT:C3059 having one or more duplication events in the genomic interval in chromosome 1: 400000-1400000.
Note: it is different from CNV status matrix introduced in Introduction_2_loadvariants. Value in this matrix is percentage (%) of samples having one or more CNVs overlapped with the binned interval while the value in CNV status matrix is fraction in individual samples to indicate how much the binned interval overlaps with one or more CNVs in the individual sample.
3 Calculate CNV frequency data
3.1 segtoFreq function
This function computes the binned CNV frequency from segment data.
The parameters of this function:
data: Segment data with CNV states. The first four columns should specify sample ID, chromosome, start position, and end position, respectively. The column representing CNV states should contain either “DUP” for duplications or “DEL” for deletions.cnv_column_idx: Index of the column specifying CNV state. Default is 6, following the “pgxseg” format used in Progenetix. If the input segment data uses the general.segfile format, it might need to be set differently.cohort_name: A string specifying the cohort name. Default is “unspecified cohort”.assembly: A string specifying the genome assembly version for CNV frequency calculation. Allowed options are “hg19” or “hg38”. Default is “hg38”.bin_size: Size of genomic bins used to split the genome, in base pairs (bp). Default is 1,000,000.overlap: Numeric value defining the amount of overlap between bins and segments considered as bin-specific CNV, in base pairs (bp). Default is 1,000.soft_expansion: Fraction ofbin_sizeto determine merge criteria. During the generation of genomic bins, division starts at the centromere and expands towards the telomeres on both sides. If the size of the last bin is smaller thansoft_expansion* bin_size, it will be merged with the previous bin. Default is 0.1.
Suppose you have segment data from several biosamples:
# access variant data
vardata <- pgxLoader(type="variant",biosample_id = c("pgxbs-kftvhmz9", "pgxbs-kftvhnqz","pgxbs-kftvhupd"),output="pgxseg")
# only keep segment cnv data
segdata <- vardata[vardata$variant_type %in% c("DUP","DEL"),]You can then calculate the CNV frequency from this cohort comprised of these samples. The output is stored in “pgxfreq” format:
segfreq <- segtoFreq(segdata,cohort_name="c1")
segfreq
#> GRangesList object of length 1:
#> $c1
#> 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 0.0000
#> [2] 1 400000-1400000 * | 0 0.0000
#> [3] 1 1400000-2400000 * | 0 0.0000
#> [4] 1 2400000-3400000 * | 0 0.0000
#> [5] 1 3400000-4400000 * | 0 33.3333
#> ... ... ... ... . ... ...
#> [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 seqlengths4 Visualization of CNV frequency data using ‘pgxfreq’ format
4.1 pgxFreqplot function
This function provides CNV frequency plots by genome or chromosomes as you request.
The parameters of this function:
data: frequency object returned bypgxLoaderfunction.chrom: a vector with chromosomes to be plotted. If NULL, return the plot by genome. If specified the frequencies are plotted with one panel for each chromosome. Default is NULL.layout: number of columns and rows in plot. Only used in plot by chromosome. Default is c(1,1).filters: Index or string value to indicate which filter to be plotted. The length of filters is limited to one if the parametercircosis False. Default is the first filter.circos: a logical value to indicate if return a circos plot. If TRUE, it can return a circos plot with multiple group ids for display and comparison. Default is FALSE.highlight: Indices of genomic bins to be highlighted with red color.assembly: A string specifying which genome assembly version should be applied to CNV frequency plotting. Allowed options are “hg19”, “hg38”. Default is “hg38” (genome version used in Progenetix).
4.2 CNV frequency plot by genome
4.2.1 Input is pgxfreq object.
pgxFreqplot(freq_pgxfreq, filters="pgx:icdom-85003")
4.2.2 Input is pgxmatrix object.
pgxFreqplot(freq_pgxmatrix, filters = "NCIT:C3512")
4.3 CNV frequency plot by chromosomes
pgxFreqplot(freq_pgxfreq, filters='NCIT:C4038',chrom=c(1,2,3), layout = c(3,1)) 
4.4 CNV frequency circos plot
pgxFreqplot(freq_pgxfreq, filters='pgx:icdom-85003', circos = TRUE)
The circos plot also supports multiple group comparison
pgxFreqplot(freq_pgxfreq,filters= c("NCIT:C4038","pgx:icdom-85003"),circos = TRUE) 
4.5 Highlight interesting genomic intervals
If you want to look at the CNV frequency at specific genomic bins, you can use highlight parameter.
For example, when you are interested in CNV pattern of CCND1 gene in samples with infiltrating duct carcinoma (icdom-85003).
You could first find the genomic bin where CCND1 (chr11:69641156-69654474) is located.
# Extract the CNV frequency data frame of samples from 'icdom-85003' from
# the previously returned object
freq_IDC <- freq_pgxfreq[['pgx:icdom-85003']]
# search the genomic bin where CCND1 is located
bin <- which(seqnames(freq_IDC) == 11 & start(freq_IDC) <= 69641156 &
end(freq_IDC) >= 69654474)
freq_IDC[bin,]
#> GRanges object with 1 range and 3 metadata columns:
#> seqnames ranges strand | gain_frequency loss_frequency
#> <Rle> <IRanges> <Rle> | <numeric> <numeric>
#> [1] 11 69400000-70400000 * | 30.375 9.074
#> no
#> <integer>
#> [1] 1887
#> -------
#> seqinfo: 24 sequences from an unspecified genome; no seqlengthsThen you could highlight this genomic bin like this
pgxFreqplot(freq_pgxfreq,filters = 'pgx:icdom-85003', chrom = 11,highlight = bin)
Note: For CNV analysis of specific genes, the highlighted plot is rough as a reference, because the bin size in frequency plots is 1MB, which is possible to cover multiple genes.
The highlighting is also available for genome plots and circos plots. And you could highlight multiple bins by a vector of indices.
pgxFreqplot(freq_pgxfreq,filters = 'pgx:icdom-85003',highlight = c(1:100))
5 Session Info
#> R version 4.4.0 RC (2024-04-16 r86468)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] SummarizedExperiment_1.35.0 Biobase_2.65.0
#> [3] GenomicRanges_1.57.0 GenomeInfoDb_1.41.0
#> [5] IRanges_2.39.0 S4Vectors_0.43.0
#> [7] BiocGenerics_0.51.0 MatrixGenerics_1.17.0
#> [9] matrixStats_1.3.0 pgxRpi_1.1.2
#> [11] BiocStyle_2.33.0
#>
#> loaded via a namespace (and not attached):
#> [1] sass_0.4.9 SparseArray_1.5.0 shape_1.4.6.1
#> [4] lattice_0.22-6 digest_0.6.35 magrittr_2.0.3
#> [7] evaluate_0.23 attempt_0.3.1 grid_4.4.0
#> [10] bookdown_0.39 circlize_0.4.16 fastmap_1.1.1
#> [13] plyr_1.8.9 jsonlite_1.8.8 Matrix_1.7-0
#> [16] GlobalOptions_0.1.2 tinytex_0.50 BiocManager_1.30.22
#> [19] httr_1.4.7 UCSC.utils_1.1.0 jquerylib_0.1.4
#> [22] abind_1.4-5 cli_3.6.2 rlang_1.1.3
#> [25] crayon_1.5.2 XVector_0.45.0 cachem_1.0.8
#> [28] DelayedArray_0.31.0 yaml_2.3.8 S4Arrays_1.5.0
#> [31] tools_4.4.0 colorspace_2.1-0 GenomeInfoDbData_1.2.12
#> [34] curl_5.2.1 R6_2.5.1 lifecycle_1.0.4
#> [37] magick_2.8.3 zlibbioc_1.51.0 bslib_0.7.0
#> [40] Rcpp_1.0.12 xfun_0.43 highr_0.10
#> [43] knitr_1.46 htmltools_0.5.8.1 rmarkdown_2.26
#> [46] compiler_4.4.0