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. This vignette provides a comprehensive guide on accessing genomic variant data within the Progenetix database.
If your focus lies in cancer cell lines, you can access data from cancercelllines.org by setting the domain parameter to "cancercelllines.org" in pgxLoader function. 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.
library(pgxRpi)
library(SummarizedExperiment) # for pgxmatrix data
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#> anyMissing, rowMedianspgxLoader functionThis function loads various data from Progenetix database via the Beacon v2 API with some extensions (BeaconPlus).
The parameters of this function used in this tutorial:
type: A string specifying output data type. "g_variants" and "cnv_fraction" are used in this tutorial.output: A string specifying output file format. The available options depend on the type parameter. When type is "g_variants", available options are NULL (default), "pgxseg", or "seg"; when type is "cnv_fraction", available options are NULL (default) or "pgxmatrix".biosample_id: Identifiers used in the query database for identifying biosamples.individual_id: Identifiers used in the query database for identifying individuals.filters: Identifiers used in public repositories, bio-ontology terms, or custom terms such as "NCIT:C7376". For more information about filters, see the documentation.codematches: A logical value determining whether to exclude samples from child concepts
of specified filters in the ontology tree. If TRUE, only samples exactly matching the specified filters will be included. Do not use this parameter when filters include ontology-irrelevant filters such as pubmed or cohort identifiers. Default is FALSE.limit: Integer to specify the number of returned profiles. Default is 0 (return all).skip: Integer to specify the number of skipped profiles. E.g. if skip = 2,limit=500, the first 2*500=1000 profiles are skipped and the next 500 profiles are returned. Default is 0 (no skip).dataset: A string specifying the dataset to query from the Beacon response. Default is NULL, which includes results from all datasets.save_file: A logical value determining whether to save variant data as a local file instead of direct return. Only used when the parameter type is "g_variants". Default is FALSE.filename: A string specifying the path and name of the file to be saved. Only used if the parameter save_file is TRUE. Default is "variants.tsv", saved in the current working directory..num_cores: An integer specifying the number of cores to use for parallel processing during Beacon v2 variant data queries from multiple biosamples. Default is 1.use_https: A logical value indicating whether to use the HTTPS protocol. If TRUE, the domain will be prefixed with "https://"; otherwise, "http://" will be used. Default is TRUE.domain: A string specifying the domain of the query data resource. Default is "progenetix.org".entry_point: A string specifying the entry point of the Beacon v2 API. Default is "beacon", resulting in the endpoint being "https://progenetix.org/beacon".Due to time-out constraints, variant data in Progenetix or cancercelllines can only be queried using biosample ids, rather than filters or individual ids. To speed up the process, you can use the num_cores parameter to enable parallel processing across multiple samples. For more details about retrieving biosample ids, refer to the vignette Introduction_1_load_metadata.
# get 2 samples for demonstration
biosamples <- pgxLoader(type="biosamples", filters = "NCIT:C3512", limit=2)
biosample_id <- biosamples$biosample_id
biosample_id
#> [1] "pgxbs-m84dbahf" "pgxbs-m84d9x03"There are three output formats.
The default output format extracts variant data from the Beacon v2 response, containing variant id and associated analysis id, biosample id and individual id. The CNV data is represented as copy number change class following the GA4GH Variation Representation Specification (VRS).
variant_1 <- pgxLoader(type="g_variants", biosample_id = biosample_id)
#> Warning in read_variant_beacon(i, limit, use_https, domain, entry_point, : NAs
#> introduced by coercion
#> Warning in read_variant_beacon(i, limit, use_https, domain, entry_point, : NAs
#> introduced by coercion
head(variant_1)
#> variant_id analysis_id biosample_id individual_id
#> 1 bycvar-684b062f49354dfc45f7a13b pgxcs-m84dbahf pgxbs-m84dbahf pgxind-m84d7a5q
#> 2 bycvar-684b063049354dfc45f7a14c pgxcs-m84dbahf pgxbs-m84dbahf pgxind-m84d7a5q
#> 3 bycvar-684b063049354dfc45f7a143 pgxcs-m84dbahf pgxbs-m84dbahf pgxind-m84d7a5q
#> 4 bycvar-684b062f49354dfc45f7a139 pgxcs-m84dbahf pgxbs-m84dbahf pgxind-m84d7a5q
#> 5 bycvar-684b063049354dfc45f7a142 pgxcs-m84dbahf pgxbs-m84dbahf pgxind-m84d7a5q
#> 6 bycvar-684b063049354dfc45f7a141 pgxcs-m84dbahf pgxbs-m84dbahf pgxind-m84d7a5q
#> variant_internal_id variation_location_chromosome
#> 1 NC_000005.10:218387-181114186:EFO_0030068 5
#> 2 NC_000017.11:264878-35373143:EFO_0030068 17
#> 3 NC_000012.12:285559-2892178:EFO_0030068 12
#> 4 NC_000003.12:376406-195760866:EFO_0030068 3
#> 5 NC_000010.11:414000-132385563:EFO_0030068 10
#> 6 NC_000009.12:421980-31112859:EFO_0030068 9
#> variation_location_end variation_location_sequence_id
#> 1 181114186 refseq:NC_000005.10
#> 2 35373143 refseq:NC_000017.11
#> 3 2892178 refseq:NC_000012.12
#> 4 195760866 refseq:NC_000003.12
#> 5 132385563 refseq:NC_000010.11
#> 6 31112859 refseq:NC_000009.12
#> variation_location_sequence_reference_refget_accession
#> 1 SQ.aUiQCzCPZ2d0csHbMSbh2NzInhonSXwI
#> 2 SQ.dLZ15tNO1Ur0IcGjwc3Sdi_0A6Yf4zm7
#> 3 SQ.6wlJpONE3oNb4D69ULmEXhqyDZ4vwNfl
#> 4 SQ.Zu7h9AggXxhTaGVsy7h_EZSChSZGcmgX
#> 5 SQ.ss8r_wB0-b9r44TQTMmVTI92884QvBiB
#> 6 SQ.KEO-4XBcm1cxeo_DIQ8_ofqGUkp4iZhI
#> variation_location_sequence_reference_type variation_location_start
#> 1 SequenceReference 218387
#> 2 SequenceReference 264878
#> 3 SequenceReference 285559
#> 4 SequenceReference 376406
#> 5 SequenceReference 414000
#> 6 SequenceReference 421980
#> variation_location_type variant_copychange identifiers molecularAttributes
#> 1 SequenceLocation low-level loss NA NA
#> 2 SequenceLocation low-level loss NA NA
#> 3 SequenceLocation low-level loss NA NA
#> 4 SequenceLocation low-level loss NA NA
#> 5 SequenceLocation low-level loss NA NA
#> 6 SequenceLocation low-level loss NA NAYou can also query variant data from other Beacon v2 resources by setting the domain and entry_point parameters accordingly.
variant_2 <- pgxLoader(type="g_variants", biosample_id = "cellzbs-kftvksak", domain = "cancercelllines.org", entry_point="beacon")
#> Warning in read_variant_beacon(i, limit, use_https, domain, entry_point, : NAs
#> introduced by coercion
head(variant_2)
#> variant_id analysis_id biosample_id
#> 1 bycvar-63d118be42040c6f3561d6b5 cellzcs-kftwtxov cellzbs-kftvksak
#> 2 bycvar-63d118be42040c6f3561d6cd cellzcs-kftwtxov cellzbs-kftvksak
#> 3 bycvar-63d118be42040c6f3561d6b7 cellzcs-kftwtxov cellzbs-kftvksak
#> 4 bycvar-63d118be42040c6f3561d552 cellzcs-kftwtxov cellzbs-kftvksak
#> 5 bycvar-63d118be42040c6f3561d506 cellzcs-kftwtxov cellzbs-kftvksak
#> 6 bycvar-63d118be42040c6f3561d635 cellzcs-kftwtxov cellzbs-kftvksak
#> individual_id variant_internal_id
#> 1 cellzind-B45e11EE NC_000016.10:10777-11588:EFO_0030070
#> 2 cellzind-B45e11EE NC_000018.10:11543-122470:EFO_0030070
#> 3 cellzind-B45e11EE NC_000016.10:12016-14877558:EFO_0030067
#> 4 cellzind-B45e11EE NC_000004.12:12281-48898:EFO_0030067
#> 5 cellzind-B45e11EE NC_000003.12:18667-46751887:EFO_0030067
#> 6 cellzind-B45e11EE NC_000010.11:26823-58258:EFO_0030070
#> variation_location_chromosome variation_location_end
#> 1 16 11588
#> 2 18 122470
#> 3 16 14877558
#> 4 4 48898
#> 5 3 46751887
#> 6 10 58258
#> variation_location_sequence_id
#> 1 refseq:NC_000016.10
#> 2 refseq:NC_000018.10
#> 3 refseq:NC_000016.10
#> 4 refseq:NC_000004.12
#> 5 refseq:NC_000003.12
#> 6 refseq:NC_000010.11
#> variation_location_sequence_reference_refget_accession
#> 1 SQ.yC_0RBj3fgBlvgyAuycbzdubtLxq-rE0
#> 2 SQ.vWwFhJ5lQDMhh-czg06YtlWqu0lvFAZV
#> 3 SQ.yC_0RBj3fgBlvgyAuycbzdubtLxq-rE0
#> 4 SQ.HxuclGHh0XCDuF8x6yQrpHUBL7ZntAHc
#> 5 SQ.Zu7h9AggXxhTaGVsy7h_EZSChSZGcmgX
#> 6 SQ.ss8r_wB0-b9r44TQTMmVTI92884QvBiB
#> variation_location_sequence_reference_type variation_location_start
#> 1 SequenceReference 10777
#> 2 SequenceReference 11543
#> 3 SequenceReference 12016
#> 4 SequenceReference 12281
#> 5 SequenceReference 18667
#> 6 SequenceReference 26823
#> variation_location_type variant_copychange identifiers molecularAttributes
#> 1 SequenceLocation gain NA NA
#> 2 SequenceLocation gain NA NA
#> 3 SequenceLocation loss NA NA
#> 4 SequenceLocation loss NA NA
#> 5 SequenceLocation loss NA NA
#> 6 SequenceLocation gain NA NAoutput = “pgxseg”)This format accesses data from Progenetix services API, which utilizes specialized resources within Progenetix. The ‘.pgxseg’ file format contains segment mean values (in log2 column), which are equal to log2(copy number of measured sample/copy number of control sample (usually 2)). A few variants are point mutations represented by columns reference_bases and alternate_bases.
variant_3 <- pgxLoader(type="g_variants", biosample_id = biosample_id,output = "pgxseg")
head(variant_3)
#> biosample_id reference_name start end log2 variant_type
#> 1 pgxbs-m84dbahf 1 120005459 247433974 0.6774 DUP
#> 2 pgxbs-m84dbahf 3 376406 195760866 -0.1637 DEL
#> 3 pgxbs-m84dbahf 4 53377881 189341332 -0.4039 DEL
#> 4 pgxbs-m84dbahf 5 218387 181114186 -0.1362 DEL
#> 5 pgxbs-m84dbahf 6 97282077 114057806 -0.8663 DEL
#> 6 pgxbs-m84dbahf 6 161350155 170489803 -0.2514 DEL
#> reference_sequence sequence variant_state_id variant_state_label
#> 1 . . EFO:0030071 low-level gain
#> 2 . . EFO:0030068 low-level loss
#> 3 . . EFO:0030068 low-level loss
#> 4 . . EFO:0030068 low-level loss
#> 5 . . EFO:0020073 high-level loss
#> 6 . . EFO:0030068 low-level lossoutput = “seg”)This format accesses data from Progenetix services API, which utilizes specialized resources within Progenetix. This format is compatible with IGV tool for visualization.
variant_4 <- pgxLoader(type="g_variants", biosample_id = biosample_id,output = "seg")
head(variant_4)
#> ID chrom loc.start loc.end num.mark seg.mean
#> 1 pgxbs-m84dbahf 1 120005460 247433974 NA 0.6774
#> 2 pgxbs-m84dbahf 3 376407 195760866 NA -0.1637
#> 3 pgxbs-m84dbahf 4 53377882 189341332 NA -0.4039
#> 4 pgxbs-m84dbahf 5 218388 181114186 NA -0.1362
#> 5 pgxbs-m84dbahf 6 97282078 114057806 NA -0.8663
#> 6 pgxbs-m84dbahf 6 161350156 170489803 NA -0.2514Setting save_file to TRUE in the pgxLoader function will save the retrieved variants data to a file
rather than returning it directly. By default, the data will be saved in the current working directory,
but you can specify a different path using the filename parameter. This export functionality is
only available for variants data (when type='g_variants').
The following codes creates a ‘.pgxseg’ file with the name “variants.pgxseg” in “~/Downloads/” folder.
pgxLoader(type="g_variants", output="pgxseg", biosample_id=biosample_id, save_file=TRUE,
filename="~/Downloads/variants.pgxseg")To visualize the ‘.pgxseg’ file, you can either upload it to this link or use the byconaut package for local visualization when dealing with a large number of samples.
The following codes creates a special ‘.seg’ file with the name “variants.seg” in “~/Downloads/” folder.
pgxLoader(type="g_variants", output="seg", biosample_id=biosample_id, save_file=TRUE,
filename="~/Downloads/variants.seg")You can upload this ‘.seg’ file to IGV tool for visualization.
Because segment variants are not harmonized across samples, Progenetix provides processed CNV features, known as CNV fractions. These fractions represent the proportion of genomic regions overlapping one or more CNVs of a given type, facilitating sample-wise comparisons. The following query is based on filters, but biosample id and individual id are also available for sample-specific CNV fraction queries. For more information about filters, biosample id and individual id, as well as the use of parameters skip, limit, and codematches, see the vignette Introduction_1_load_metadata.
CNV fraction data is retrieved through the Progenetix services API. When making a query, the domain of the data resource should be set to either "progenetix.org" (by default) or "cancercelllines.org".
cnv_fraction <- pgxLoader(type="cnv_fraction", filters = "NCIT:C2948")This includes CNV fraction across chromosome arms, whole chromosomes, or the whole genome.
names(cnv_fraction)
#> [1] "arm_cnv_frac" "chr_cnv_frac" "genome_cnv_frac"The CNV fraction across chromosomal arms looks like this
head(cnv_fraction$arm_cnv_frac)[,c(1:4, 49:52)]
#> chr1p.dup chr1q.dup chr2p.dup chr2q.dup chr1p.del chr1q.del
#> pgxcs-kftvs0ri 0 0.000 0.000 0.000 0.000 0
#> pgxcs-kftvu9w2 0 0.000 0.000 0.000 0.000 0
#> pgxcs-kftvw1kw 0 0.000 0.000 0.000 0.000 0
#> pgxcs-kftvw1ld 0 0.000 0.000 0.000 0.000 0
#> pgxcs-kftvw1lu 0 0.000 0.000 0.000 0.225 0
#> pgxcs-kftvw1mb 0 0.979 0.989 0.003 0.000 0
#> chr2p.del chr2q.del
#> pgxcs-kftvs0ri 0 0
#> pgxcs-kftvu9w2 0 0
#> pgxcs-kftvw1kw 0 0
#> pgxcs-kftvw1ld 0 0
#> pgxcs-kftvw1lu 0 0
#> pgxcs-kftvw1mb 0 0The row names are analyses ids from samples that belong to the input filter NCIT:C2948. There are 96 columns. The first 48 columns are duplication fraction across chromosomal arms, followed by deletion fraction. CNV fraction across whole chromosomes is similar, with the only difference in columns.
The CNV fraction across the genome (hg38) looks like this
head(cnv_fraction$genome_cnv_frac)
#> cnvfraction dupfraction delfraction
#> pgxcs-kftvs0ri 0.080 0.036 0.044
#> pgxcs-kftvu9w2 0.058 0.010 0.048
#> pgxcs-kftvw1kw 0.000 0.000 0.000
#> pgxcs-kftvw1ld 0.000 0.000 0.000
#> pgxcs-kftvw1lu 0.027 0.000 0.027
#> pgxcs-kftvw1mb 0.176 0.159 0.017The first column is the total called fraction, followed by duplication fraction and deletion fraction.
cnvfrac_matrix <- pgxLoader(type="cnv_fraction", output="pgxmatrix", filters = "NCIT:C2948")The 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 analyses derived from biosamples. The data looks like this
cnvfrac_matrix
#> class: RangedSummarizedExperiment
#> dim: 6212 87
#> metadata(0):
#> assays(1): cnv_matrix
#> rownames(6212): 1 2 ... 6211 6212
#> rowData names(1): type
#> colnames(87): pgxcs-kftvs0ri pgxcs-kftvu9w2 ... pgxcs-m84dipwe
#> pgxcs-m84dir2n
#> colData names(3): analysis_id biosample_id group_idYou could get the interval information by rowRanges function from SummarizedExperiment package.
rowRanges(cnvfrac_matrix)
#> GRanges object with 6212 ranges and 1 metadata column:
#> seqnames ranges strand | type
#> <Rle> <IRanges> <Rle> | <character>
#> 1 chr1 0-400000 * | DUP
#> 2 chr1 400000-1400000 * | DUP
#> 3 chr1 1400000-2400000 * | DUP
#> 4 chr1 2400000-3400000 * | DUP
#> 5 chr1 3400000-4400000 * | DUP
#> ... ... ... ... . ...
#> 6208 chrY 52400000-53400000 * | DEL
#> 6209 chrY 53400000-54400000 * | DEL
#> 6210 chrY 54400000-55400000 * | DEL
#> 6211 chrY 55400000-56400000 * | DEL
#> 6212 chrY 56400000-57227415 * | DEL
#> -------
#> seqinfo: 24 sequences from an unspecified genome; no seqlengthsTo access the CNV fraction matrix, use assay accesssor from SummarizedExperiment package
assay(cnvfrac_matrix)[1:3,1:3]
#> pgxcs-kftvs0ri pgxcs-kftvu9w2 pgxcs-kftvw1kw
#> 1 0 0 0
#> 2 0 0 0
#> 3 0 0 0The matrix contains 6212 rows, corresponding to genomic regions. These rows include 3,106 intervals with “gain status” and 3,106 intervals with “loss status”. The columns represent analysis profiles derived from samples that belong to the input filter.
The value is the fraction of the binned interval overlapping with one or more CNVs of the given type (DUP/DEL). For example, if the value in the second row, the first column is 0.2, it means that one or more duplication events overlapped with 20% of the genomic bin located in chromosome 1: 400000-1400000 in the first analysis profile.
To get associated biosample id and filters for analyses, use colData function from SummarizedExperiment package:
colData(cnvfrac_matrix)
#> DataFrame with 87 rows and 3 columns
#> analysis_id biosample_id group_id
#> <character> <character> <character>
#> pgxcs-kftvs0ri pgxcs-kftvs0ri pgxbs-kftvh262 NCIT:C2948
#> pgxcs-kftvu9w2 pgxcs-kftvu9w2 pgxbs-kftvh9fp NCIT:C2948
#> pgxcs-kftvw1kw pgxcs-kftvw1kw pgxbs-kftvhf4h NCIT:C8893
#> pgxcs-kftvw1ld pgxcs-kftvw1ld pgxbs-kftvhf4i NCIT:C8893
#> pgxcs-kftvw1lu pgxcs-kftvw1lu pgxbs-kftvhf4k NCIT:C8893
#> ... ... ... ...
#> pgxcs-m84dghm4 pgxcs-m84dghm4 pgxbs-m84dghm4 NCIT:C27246
#> pgxcs-m84dgpmm pgxcs-m84dgpmm pgxbs-m84dgpmm NCIT:C4515
#> pgxcs-m84dhq9w pgxcs-m84dhq9w pgxbs-m84dhq9w NCIT:C7733
#> pgxcs-m84dipwe pgxcs-m84dipwe pgxbs-m84dipwe NCIT:C7733
#> pgxcs-m84dir2n pgxcs-m84dir2n pgxbs-m84dir2n NCIT:C27246It is noted that the number of analysis profiles does not necessarily equal the number of samples. One biosample id may correspond to multiple analysis ids. group_id corresponds to the meaning of filters.
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