SomaScan.db 0.99.10
The SomaScan.db package provides extended biological annotations to be used
in conjunction with the results of SomaLogic’s SomaScan assay, a
fee-for-service proteomic technology platform designed to detect proteins
across numerous biological pathways.
This vignette describes how to use the SomaScan.db
package to annotate SomaScan data, i.e. add additional information to an ADAT
file that will give biological context (at the gene level) to the platform’s
reagents and their protein targets. SomaScan.db performs annotation by
mapping SomaScan reagent IDs (SeqIds) to their corresponding protein(s) and
gene(s), as well as biological pathways (GO, KEGG, etc.) and identifiers from
other public data repositories.
SomaScan.db utilizes the same methods and setup as other Bioconductor
annotation packages, and therefore the methods should be familiar if you’ve
worked with such packages previously.
To begin, install and load the SomaScan.db package:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("SomaScan.db", version = remotes::bioc_version())Once installed, the package can be loaded as follows:
library(SomaScan.db)Loading this package will expose an annotation object with the same name
as the package, SomaScan.db. This object is a SQLite database
containing annotation data for the SomaScan assay derived from popular public
repositories. Viewing the object will present a metadata table containing
information about the annotations and where they were obtained:
SomaScan.db## SomaDb object:
## | DBSCHEMAVERSION: 2.1
## | Db type: ChipDb
## | Supporting package: SomaScan.db
## | DBSCHEMA: HUMANCHIP_DB
## | ORGANISM: Homo sapiens
## | SPECIES: Human
## | MANUFACTURER: SomaLogic
## | CHIPNAME: SomaScan
## | MANUFACTURERURL: https://somalogic.com/somascan-platform/
## | EGSOURCEDATE: 2023-Sep11
## | EGSOURCENAME: Entrez Gene
## | EGSOURCEURL: ftp://ftp.ncbi.nlm.nih.gov/gene/DATA
## | CENTRALID: ENTREZID
## | TAXID: 9606
## | GOSOURCENAME: Gene Ontology
## | GOSOURCEURL: http://current.geneontology.org/ontology/go-basic.obo
## | GOSOURCEDATE: 2023-07-27
## | GOEGSOURCEDATE: 2023-Sep11
## | GOEGSOURCENAME: Entrez Gene
## | GOEGSOURCEURL: ftp://ftp.ncbi.nlm.nih.gov/gene/DATA
## | KEGGSOURCENAME: KEGG GENOME
## | KEGGSOURCEURL: ftp://ftp.genome.jp/pub/kegg/genomes
## | KEGGSOURCEDATE: 2011-Mar15
## | GPSOURCENAME: UCSC Genome Bioinformatics (Homo sapiens)
## | GPSOURCEURL:
## | GPSOURCEDATE: 2023-Aug20
## | ENSOURCEDATE: 2023-May10
## | ENSOURCENAME: Ensembl
## | ENSOURCEURL: ftp://ftp.ensembl.org/pub/current_fasta
## | UPSOURCENAME: Uniprot
## | UPSOURCEURL: http://www.UniProt.org/
## | UPSOURCEDATE: Mon Sep 18 16:12:39 2023##
## Please see: help('select') for usage informationThe same information can be retrieved as a data frame by calling
metadata(SomaScan.db).
Moving forward, this database object (SomaScan.db) will be used throughout
the vignette to retrieve SomaScan annotations and map between identifiers.
For reference, the species information for the database can directly be retrieved with the following methods:
species(SomaScan.db)## [1] "Homo sapiens"taxonomyId(SomaScan.db)## [1] 9606It’s also possible to pull a more detailed summary of annotations and resource
identifiers (aka keys) by calling the package as a function, with the .db
extension removed:
SomaScan()## Quality control information for SomaScan:
##
##
## This package has the following mappings:
##
## SomaScanALIAS2PROBE has 38839 mapped keys (of 260786 keys)
## SomaScanENSEMBL has 10596 mapped keys (of 10731 keys)
## SomaScanENSEMBL2PROBE has 11012 mapped keys (of 40851 keys)
## SomaScanENTREZID has 10609 mapped keys (of 10731 keys)
## SomaScanENZYME has 1712 mapped keys (of 10731 keys)
## SomaScanENZYME2PROBE has 809 mapped keys (of 975 keys)
## SomaScanGENENAME has 10609 mapped keys (of 10731 keys)
## SomaScanGO has 10499 mapped keys (of 10731 keys)
## SomaScanGO2ALLPROBES has 20327 mapped keys (of 22561 keys)
## SomaScanGO2PROBE has 16066 mapped keys (of 18692 keys)
## SomaScanMAP has 10607 mapped keys (of 10731 keys)
## SomaScanOMIM has 9766 mapped keys (of 10731 keys)
## SomaScanPATH has 4099 mapped keys (of 10731 keys)
## SomaScanPATH2PROBE has 229 mapped keys (of 229 keys)
## SomaScanPMID has 10607 mapped keys (of 10731 keys)
## SomaScanPMID2PROBE has 605433 mapped keys (of 790941 keys)
## SomaScanREFSEQ has 10605 mapped keys (of 10731 keys)
## SomaScanSYMBOL has 10609 mapped keys (of 10731 keys)
## SomaScanUNIPROT has 10545 mapped keys (of 10731 keys)
##
##
## Additional Information about this package:
##
## DB schema: HUMANCHIP_DB
## DB schema version: 2.1
## Organism: Homo sapiens
## Date for NCBI data: 2023-Sep11
## Date for GO data: 2023-07-27
## Date for KEGG data: 2011-Mar15
## Date for Golden Path data: 2023-Aug20
## Date for Ensembl data: 2023-May10Note: Keys will be explained in greater detail later in this vignette.
The SomaScan.db package has 5 primary methods that can be used to query the
database:
keyskeytypescolumnsselectmapIdsThis vignette will describe how each of these methods can be used to obtain
annotation data from SomaScan.db.
keys methodThis annotation package is platform-based, meaning it was built around the
unique identifiers from a specific platform (in this case, SomaLogic’s
SomaScan platform). That identifier corresponds to each of the assay’s
analytes, and therefore the analyte identifiers (SeqIds) are the primary
term used to query the database (aka “key”).
All keys in the database can be retrieved using keys:
# Short list of primary keys
keys(SomaScan.db) |> head(10L)## [1] "10000-28" "10001-7" "10003-15" "10006-25" "10008-43" "10010-10"
## [7] "10011-65" "10012-5" "10014-31" "10015-119"Each key retrieved in the output above corresponds to one of the assay’s unique analytes.
keytype methodWhen querying the database, we can also specify the type of key (“keytype”)
being used. The keytype refers to the type of identifier that is used to
generate a database query. While the database is centered around the SomaLogic
SeqId, other identifiers can still be used to query the database.
We can list all available datatypes that can be used as query keys using
keytypes():
## List all of the supported key types.
keytypes(SomaScan.db)## [1] "ACCNUM" "ALIAS" "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS"
## [6] "ENTREZID" "ENZYME" "EVIDENCE" "EVIDENCEALL" "GENENAME"
## [11] "GENETYPE" "GO" "GOALL" "IPI" "MAP"
## [16] "OMIM" "ONTOLOGY" "ONTOLOGYALL" "PATH" "PFAM"
## [21] "PMID" "PROBEID" "PROSITE" "REFSEQ" "SYMBOL"
## [26] "UCSCKG" "UNIPROT"Note: the SomaScan assay analyte identifiers (SeqIds) are stored as the
“PROBEID” keytype.
keytypes can also be used in conjunction with keys to retrieve all
identifiers associated with the specified keytype. The example below will
retrieve all UniProt IDs in SomaScan.db:
keys(SomaScan.db, keytype = "UNIPROT") |> head(20L)## [1] "A8K052" "P04217" "Q68CK0" "Q8IYJ6" "Q96P39" "V9HWD8" "P01023" "Q13677"
## [9] "Q59F47" "Q5QTS0" "Q68DN2" "Q6PIY3" "Q6PN97" "A8K4E7" "O15159" "O15300"
## [17] "P18440" "Q546N1" "Q96TE9" "Q9HAQ5"columns methodAll available external annotations, corresponding to “columns” of the
database, can be listed using columns():
columns(SomaScan.db)## [1] "ACCNUM" "ALIAS" "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS"
## [6] "ENTREZID" "ENZYME" "EVIDENCE" "EVIDENCEALL" "GENENAME"
## [11] "GENETYPE" "GO" "GOALL" "IPI" "MAP"
## [16] "OMIM" "ONTOLOGY" "ONTOLOGYALL" "PATH" "PFAM"
## [21] "PMID" "PROBEID" "PROSITE" "REFSEQ" "SYMBOL"
## [26] "UCSCKG" "UNIPROT"Note: the SomaScan assay analyte identifiers (SeqIds) are stored in the
“PROBEID” column.
This list may look very similar (or even identical) to the columns output.
If identical, all columns can be used as query keys. For a more in-depth
explanation of what each of these columns contains, consult the manual:
help("OMIM") # Example help callEach columns entry also has a mapping object that contains the information
connecting SeqIds → the annotation’s identifiers. To read further
documentation about the object and the resource used to make it, check out the
manual page for the mapping itself:
?SomaScanOMIMselect methodThe list of columns returned by columns informs us as to what types of data
are available; therefore, the column values can be used to retrieve specific
pieces of information from the database. You can think of keys and columns as:
SeqIds/probe IDs), aka rows
of the databaseThe SomaScan.db database can be queried via select, using both the keys
and columns.
When selecting columns and keys using the select method, the keys are
returned in the left-most column of the output, in the PROBEID column. The
results will be in the exact same order as the input keys:
# Randomly select a set of keys
example_keys <- withr::with_seed(101L, sample(keys(SomaScan.db),
size = 5L,
replace = FALSE
))
# Query keys in the database
select(SomaScan.db,
keys = example_keys,
columns = c("ENTREZID", "SYMBOL", "GENENAME")
)## 'select()' returned 1:1 mapping between keys and columns## PROBEID ENTREZID SYMBOL GENENAME
## 1 34799-31 81563 C1orf21 chromosome 1 open reading frame 21
## 2 20520-14 10447 FAM3C FAM3 metabolism regulating signaling molecule C
## 3 9123-18 255057 CBARP CACN subunit beta associated regulatory protein
## 4 21707-15 389941 C1QL3 complement C1q like 3
## 5 29325-43 11102 RPP14 ribonuclease P/MRP subunit p14Note: The message above (‘select()’ returned 1:1 mapping between keys and columns) will be described in detail in the next section of this vignette.
The data that is returned will always be in the same order as the provided
keys. If select cannot find a mapping for a specific key, an NA value will
be returned to retain the original query order.
# Inserting a new key that won't be found in the annotations ("TEST")
test_keys <- c(example_keys[1], "TEST")
select(SomaScan.db, keys = test_keys, columns = c("PROBEID", "ENTREZID"))## 'select()' returned 1:1 mapping between keys and columns## PROBEID ENTREZID
## 1 34799-31 81563
## 2 <NA> <NA>In the example above, a “PROBEID” and “ENTREZID” value couldn’t be found for
the character string “TEST”, so an NA was returned in its place.
When using select, a message indicating the relationship between query keys
and column data will be displayed along with the query results.
This message will describe one of the three relationships below:
1:1 mapping between keys and columns1:many mapping between keys and columnsmany:many mapping between keys and columnsThese messages describe the very real possibility that there are multiple
identifiers associated with each key in a query. This can cause the number of
rows returned by select() to exceed the number of keys used to retrieve the
data; this is what is meant by the message “‘select()’ returned 1:many mapping
between keys and columns”.
In these cases, you will still see concordance between the order of the
provided keys and outputted results rows, but you should be aware that new
rows were inserted into the results. This message is not an error, merely an
informative notification to the user making it clear that more output rows
than input items should be expected. Importantly, this message also does not
relay information about the SomaScan menu itself or advice on how to handle
many-to-one relationships between SomaScan reagents and their corresponding
protein targets; rather, the message is directly related to this package’s
select method and how it retrieves information from the database.
Because some columns may have a many-to-many relationship to each key, it is generally best practice to retrieve the minimum number of columns needed for a query. Additionally, when retrieving a column that is known to have a many-to-one relationship to each key, like GO terms, it’s best to request that information in its own query, like so:
# Good
select(SomaScan.db, keys = example_keys[3L], columns = "GO")## 'select()' returned 1:many mapping between keys and columns## PROBEID GO EVIDENCE ONTOLOGY
## 1 9123-18 GO:0005886 IBA CC
## 2 9123-18 GO:0005886 ISS CC
## 3 9123-18 GO:0030141 IBA CC
## 4 9123-18 GO:0030141 ISS CC
## 5 9123-18 GO:0030426 ISS CC
## 6 9123-18 GO:0030672 IEA CC
## 7 9123-18 GO:0044325 IBA MF
## 8 9123-18 GO:0044325 ISS MF
## 9 9123-18 GO:0045955 IBA BP
## 10 9123-18 GO:0045955 ISS BP
## 11 9123-18 GO:1901386 IBA BP
## 12 9123-18 GO:1901386 ISS BP
## 13 9123-18 GO:1903170 ISS BP# Bad
select(SomaScan.db,
keys = example_keys[3L],
columns = c("UNIPROT", "ENSEMBL", "GO", "PATH", "IPI")
) |> tibble::as_tibble()## Warning: You have selected the following columns that can have a many to one
## relationship with the primary key: UNIPROT, ENSEMBL, GO, PATH, IPI .
## Because you have selected more than a few such columns there is a
## risk that this selection may balloon up into a very large result as
## the number of rows returned multiplies accordingly. To experience
## smaller/more manageable results and faster retrieval times, you might
## want to consider selecting these columns separately.## 'select()' returned 1:many mapping between keys and columns## # A tibble: 104 × 8
## PROBEID UNIPROT ENSEMBL GO EVIDENCE ONTOLOGY PATH IPI
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 9123-18 A0A140LJL2 ENSG00000099625 GO:0005886 IBA CC <NA> IPI001…
## 2 9123-18 A0A140LJL2 ENSG00000099625 GO:0005886 IBA CC <NA> IPI004…
## 3 9123-18 A0A140LJL2 ENSG00000099625 GO:0005886 ISS CC <NA> IPI001…
## 4 9123-18 A0A140LJL2 ENSG00000099625 GO:0005886 ISS CC <NA> IPI004…
## 5 9123-18 A0A140LJL2 ENSG00000099625 GO:0030141 IBA CC <NA> IPI001…
## 6 9123-18 A0A140LJL2 ENSG00000099625 GO:0030141 IBA CC <NA> IPI004…
## 7 9123-18 A0A140LJL2 ENSG00000099625 GO:0030141 ISS CC <NA> IPI001…
## 8 9123-18 A0A140LJL2 ENSG00000099625 GO:0030141 ISS CC <NA> IPI004…
## 9 9123-18 A0A140LJL2 ENSG00000099625 GO:0030426 ISS CC <NA> IPI001…
## 10 9123-18 A0A140LJL2 ENSG00000099625 GO:0030426 ISS CC <NA> IPI004…
## # ℹ 94 more rowsThe example above illustrates why it is preferred to request as few columns as possible, especially when working with GO terms.
In SomaScan.db, the default keytype for select is the probe ID. This means
that when using a SeqId (aka “PROBEID”) to retrieve annotations,
the keytype= argument does not need to be defined, and can be left
out of the select call entirely. The default (PROBEID) will be used.
select(SomaScan.db, keys = example_keys, columns = c("ENTREZID", "UNIPROT"))## 'select()' returned 1:many mapping between keys and columns## PROBEID ENTREZID UNIPROT
## 1 34799-31 81563 B2R551
## 2 34799-31 81563 Q9H246
## 3 20520-14 10447 A6NDN2
## 4 20520-14 10447 A8K3R7
## 5 20520-14 10447 Q92520
## 6 9123-18 255057 A0A140LJL2
## 7 9123-18 255057 K7EJP2
## 8 9123-18 255057 O43385
## 9 9123-18 255057 Q8N350
## 10 21707-15 389941 A0A3B0J0F3
## 11 21707-15 389941 A0PJY4
## 12 21707-15 389941 A0PJY5
## 13 21707-15 389941 Q5VWW1
## 14 29325-43 11102 O95059
## 15 29325-43 11102 Q53X97However, the database can be searched using more than just SeqIds.
For example, you may want to retrieve a list of SeqIds that are associated
with a specific gene of interest - let’s use SMAD2 as an example. You can work
“backwards” to retrieve the SeqIds associated with SMAD2 by setting the
keytype="SYMBOL":
select(SomaScan.db,
columns = c("PROBEID", "ENTREZID"),
keys = "SMAD2", keytype = "SYMBOL"
)## 'select()' returned 1:many mapping between keys and columns## SYMBOL PROBEID ENTREZID
## 1 SMAD2 10364-6 4087
## 2 SMAD2 11353-143 4087Sometimes, this may appear not to work. Let’s use CASC4 as an example:
select(SomaScan.db,
columns = c("PROBEID", "ENTREZID"),
keys = "CASC4", keytype = "SYMBOL"
)## Error in .testForValidKeys(x, keys, keytype, fks): None of the keys entered are valid keys for 'SYMBOL'. Please use the keys method to see a listing of valid arguments.The error message above implies that CASC4 is not a valid key for the
“SYMBOL” keytype, which means that no entry for CASC4 was found in the
“SYMBOL” column. However, genes can be tricky to search, and in some cases
have many common names. We can improve this using keytype="ALIAS"; this
data type contains the various aliases associated with gene names found in the
“SYMBOL” column. Using keytype="ALIAS", we can cast a wider net:
select(SomaScan.db,
columns = c("SYMBOL", "PROBEID", "ENTREZID"),
keys = "CASC4", keytype = "ALIAS"
)## 'select()' returned 1:many mapping between keys and columns## ALIAS SYMBOL PROBEID ENTREZID
## 1 CASC4 GOLM2 10613-33 113201
## 2 CASC4 GOLM2 8838-10 113201This reveals the source of our problem! CASC4 is also known as GOLM4, and this symbol is used in the annotations database. Because of this, searching for CASC4 as a symbol returns no results, but the same query is able to identify an entry when the “ALIAS” column is specified.
Additionally, we can see that CASC4/GOLM2 is associated with two SeqIds -
10613-33 and 8838-10. How is this possible, and why does this happen?
For more information, please reference the Advanced Usage Examples (
vignette("advanced_usage_examples", package = "SomaScan.db")).
mapIDs methodFor situations in which you wish only to retrieve one data type from the
database, the mapIds method may be cleaner and more streamlined than using
select, and can help avoid problems with one-to-many mapping of keys.
For example, if you are only interested in the gene symbols associated with a
set of SomaScan analytes, they can be retrieved like so:
mapIds(SomaScan.db, keys = example_keys, column = "SYMBOL")## 'select()' returned 1:1 mapping between keys and columns## 34799-31 20520-14 9123-18 21707-15 29325-43
## "C1orf21" "FAM3C" "CBARP" "C1QL3" "RPP14"mapIds will return a named vector from a single column, while select
returns a data frame and can be used to retrieve data from multiple columns.
The primary difference between mapIds and select is how the method handles
one-to-many mapping, i.e. when the chosen key maps to > 1 entry in the
selected column. When this occurs, only the first value (by default) is
returned.
Compare the output in the examples below:
# Only 1 symbol per key
mapIds(SomaScan.db, keys = example_keys[3L], column = "GO")## 'select()' returned 1:many mapping between keys and columns## 9123-18
## "GO:0005886"# All entries for chosen key
select(SomaScan.db, keys = example_keys[3L], column = "GO")## 'select()' returned 1:many mapping between keys and columns## PROBEID GO EVIDENCE ONTOLOGY
## 1 9123-18 GO:0005886 IBA CC
## 2 9123-18 GO:0005886 ISS CC
## 3 9123-18 GO:0030141 IBA CC
## 4 9123-18 GO:0030141 ISS CC
## 5 9123-18 GO:0030426 ISS CC
## 6 9123-18 GO:0030672 IEA CC
## 7 9123-18 GO:0044325 IBA MF
## 8 9123-18 GO:0044325 ISS MF
## 9 9123-18 GO:0045955 IBA BP
## 10 9123-18 GO:0045955 ISS BP
## 11 9123-18 GO:1901386 IBA BP
## 12 9123-18 GO:1901386 ISS BP
## 13 9123-18 GO:1903170 ISS BPNote that the mapIds method warning message states that it returned 1:many
mappings between keys and columns, however only one value was
returned for the desired SeqId. This is because there were more mapped
values that were discarded when the results were converted to a named vector.
This may not be a problem for some columns, like “SYMBOL” (typically there is
only one gene symbol per gene), but it may present a problem for others (like
GO terms or KEGG pathways). Think carefully when using mapIds, or consider
specifying the multiVals= argument to indicate what should be done
with multi-mapped output.
The default behavior of mapIds is to return the first available result:
# The default - returns the first available result
mapIds(SomaScan.db, keys = example_keys[3L], column = "GO",
multiVals = "first")## 'select()' returned 1:many mapping between keys and columns## 9123-18
## "GO:0005886"Again, the select message here indicates that, while only 1 value was
returned, there were many more GO term matches. All of the matches can be
viewed by specifying multiVals="list":
# Returns a list object of results, instead of only returning the first result
mapIds(SomaScan.db, keys = example_keys[3L], column = "GO",
multiVals = "list")## 'select()' returned 1:many mapping between keys and columns## $`9123-18`
## [1] "GO:0005886" "GO:0005886" "GO:0030141" "GO:0030141" "GO:0030426"
## [6] "GO:0030672" "GO:0044325" "GO:0044325" "GO:0045955" "GO:0045955"
## [11] "GO:1901386" "GO:1901386" "GO:1903170"Because the annotations in this package are compiled from public repositories,
information typically found in an ADAT may be missing. For example, in an ADAT
file, each SeqId is associated with a protein target, and
the name of that target is provided as both an abbreviated symbol (“Target”)
and full description (“Target Full Name”). The SomaScan.db
package does not contain data from a particular ADAT file; however, it does
contain a function to add the full protein target name to any data
frame obtained via select.
As an example, we will generate a data frame of Ensembl gene IDs and OMIM IDs:
ensg <- select(SomaScan.db,
keys = example_keys[1:3L],
columns = c("ENSEMBL", "OMIM")
)## 'select()' returned 1:1 mapping between keys and columnsensg## PROBEID ENSEMBL OMIM
## 1 34799-31 ENSG00000116667 <NA>
## 2 20520-14 ENSG00000196937 608618
## 3 9123-18 ENSG00000099625 <NA>We will now append the Target Full Name to this data frame:
addTargetFullName(ensg)## PROBEID TARGETFULLNAME ENSEMBL OMIM
## 1 20520-14 Protein FAM3C ENSG00000196937 608618
## 2 34799-31 Uncharacterized protein C1orf21 ENSG00000116667 <NA>
## 3 9123-18 Protein Dos ENSG00000099625 <NA>The full protein target name will be appended to the input data frame, with the Target Full Name (in the “TARGETFULLNAME” column) always added to the right of the “PROBEID” column.
In addition to the methods mentioned above, there is an R object that can be
used to retrieve SomaScan analytes from a specific menu version. The object is
a list, with each element in the list containing a character vector of
SeqIds that were available in the specified menu.
summary(somascan_menu)## Length Class Mode
## v4.0 4966 -none- character
## v4.1 7267 -none- character
## v5.0 10731 -none- characterlapply(somascan_menu, head)## $v4.0
## [1] "10000-28" "10001-7" "10003-15" "10006-25" "10008-43" "10011-65"
##
## $v4.1
## [1] "10000-28" "10001-7" "10003-15" "10006-25" "10008-43" "10010-10"
##
## $v5.0
## [1] "10000-28" "10001-7" "10003-15" "10006-25" "10008-43" "10010-10"This object also provides a quick and easy way of comparing the available SomaScan menus:
setdiff(somascan_menu$v4.1, somascan_menu$v4.0) |> head(50L)## [1] "10010-10" "10025-1" "10039-32" "10069-2" "10351-51" "10354-57"
## [7] "10379-19" "10382-1" "10398-110" "10420-30" "10439-57" "10457-3"
## [13] "10460-1" "10463-23" "10470-34" "10472-53" "10473-2" "10479-18"
## [19] "10480-33" "10505-12" "10528-2" "10576-7" "10626-116" "10631-9"
## [25] "10636-1" "10670-26" "10738-11" "10741-22" "10743-13" "10746-24"
## [31] "10780-10" "10801-11" "10819-108" "10855-55" "10870-32" "10894-25"
## [37] "10966-1" "10967-12" "10970-3" "10976-44" "10980-11" "11081-1"
## [43] "11083-23" "11121-56" "11150-3" "11159-14" "11184-51" "11200-52"
## [49] "11203-97" "11232-46"sessionInfo()## R version 4.4.0 beta (2024-04-15 r86425)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.19-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
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## 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] tibble_3.2.1 SomaScan.db_0.99.10 AnnotationDbi_1.65.2
## [4] IRanges_2.37.1 S4Vectors_0.41.7 Biobase_2.63.1
## [7] BiocGenerics_0.49.1 withr_3.0.0 BiocStyle_2.31.0
##
## loaded via a namespace (and not attached):
## [1] bit_4.0.5 jsonlite_1.8.8 compiler_4.4.0
## [4] BiocManager_1.30.22 crayon_1.5.2 blob_1.2.4
## [7] Biostrings_2.71.6 jquerylib_0.1.4 png_0.1-8
## [10] yaml_2.3.8 fastmap_1.1.1 org.Hs.eg.db_3.19.1
## [13] R6_2.5.1 XVector_0.43.1 GenomeInfoDb_1.39.14
## [16] knitr_1.46 bookdown_0.39 GenomeInfoDbData_1.2.12
## [19] DBI_1.2.2 pillar_1.9.0 bslib_0.7.0
## [22] rlang_1.1.3 utf8_1.2.4 KEGGREST_1.43.0
## [25] cachem_1.0.8 xfun_0.43 sass_0.4.9
## [28] bit64_4.0.5 RSQLite_2.3.6 memoise_2.0.1
## [31] cli_3.6.2 magrittr_2.0.3 zlibbioc_1.49.3
## [34] digest_0.6.35 lifecycle_1.0.4 vctrs_0.6.5
## [37] glue_1.7.0 evaluate_0.23 fansi_1.0.6
## [40] rmarkdown_2.26 httr_1.4.7 pkgconfig_2.0.3
## [43] tools_4.4.0 htmltools_0.5.8.1 UCSC.utils_0.99.7