SomaScan.db 0.99.10
This vignette is a follow up to the “Introduction to SomaScan.db” vignette,
and will introduce more advanced capabilities of the SomaScan.db
package. Below we illustrate how SomaScan.db can be used to deeply
explore the SomaScan menu and execute complex annotation functions, outside of
the basic use of select outlined in the introductory vignette. Knowledge of
SQL is not required, but a familiarity with R and SomaScan data is highly
suggested. For an introduction to the SomaScan.db package and its
methods, please see vignette("SomaScan-db", "SomaScan.db").
Please note that this vignette will require the installation and usage of three additional Bioconductor R packages: GO.db, EnsDb.Hsapiens.v75, and KEGGREST. Please see the linked pages to find installation instructions for these packages.
library(GO.db)
library(KEGGREST)
library(org.Hs.eg.db)
library(SomaScan.db)
library(withr)The SomaScan.db package allows a user to retrieve Gene Ontology (GO)
identifiers associated with a particular SomaScan SeqId (or set of
SeqIds). However, the available GO annotations in SomaScan.db are
limited; only the GO ID, evidence code, and ontology category are currently
available. This helps prevent the package from accumulating an overwhelming
number of annotation elements, but limits the ability to extract detailed GO
information.
To illustrate this limitation, below we will display the GO terms associated with the gene “IL31”:
il31_go <- select(SomaScan.db, keys = "IL31", keytype = "SYMBOL",
columns = c("PROBEID", "GO"))## 'select()' returned 1:many mapping between keys and columnsil31_go## SYMBOL PROBEID GO EVIDENCE ONTOLOGY
## 1 IL31 10455-196 GO:0002376 IEA BP
## 2 IL31 10455-196 GO:0005125 IBA MF
## 3 IL31 10455-196 GO:0005126 IBA MF
## 4 IL31 10455-196 GO:0005515 IPI MF
## 5 IL31 10455-196 GO:0005576 TAS CC
## 6 IL31 10455-196 GO:0005615 IBA CC
## 7 IL31 10455-196 GO:0005615 IDA CC
## 8 IL31 10455-196 GO:0007165 IEA BPIn this data frame, IL31 maps to one single SeqId (“10455-196”), indicated
by the “PROBEID” column. This SeqId and gene are associated with seven
unique GO IDs (in the “GO” column). The GO knowledgebase is vast, however,
and these identifiers are not particularly informative for anyone who
hasn’t memorized their more descriptive term names. Additional details for
each ID would make this table more informative and interpretable. Luckily,
there are two options for retrieving such data:
Term, Ontology, Definition, and
Synonym)SomaScan.db with another Bioconductor tool, like
the GO.db annotation packageEach of these techniques have their own special utility. Below, we will work
through examples of how the techniques described above can be used to link
GO information with the annotations from SomaScan.db.
The Term, Ontology, Definition, and Synonym methods are GO-specific
methods imported from the AnnotationDbi package. They are designed to
retrieve a single piece of information, indicated by the method name, that
corresponds to a set of GO identifiers (note: we will skip Ontology in this
vignette, as the GO Ontology is already retrievable with SomaScan.db).
The Term method retrieves a character string defining the role of the gene
product that corresponds to provided GO ID(s). In the example below, we will
retrieve the GO terms for each of the GO IDs in the select results generated
previously:
Term(il31_go$GO)## GO:0002376 GO:0005125
## "immune system process" "cytokine activity"
## GO:0005126 GO:0005515
## "cytokine receptor binding" "protein binding"
## GO:0005576 GO:0005615
## "extracellular region" "extracellular space"
## GO:0005615 GO:0007165
## "extracellular space" "signal transduction"The Definition method retrieves a more detailed and extended definition of
the ontology for the input GO IDs:
Definition(il31_go$GO)## GO:0002376
## "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats."
## GO:0005125
## "The activity of a soluble extracellular gene product that interacts with a receptor to effect a change in the activity of the receptor to control the survival, growth, differentiation and effector function of tissues and cells."
## GO:0005126
## "Binding to a cytokine receptor."
## GO:0005515
## "Binding to a protein."
## GO:0005576
## "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite."
## GO:0005615
## "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid."
## GO:0005615
## "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid."
## GO:0007165
## "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell."And finally, the Synonym method can be used to retrieve other ontology terms
that are considered to be synonymous to the primary term attached to the GO
ID. For example, “type I programmed cell death” is considered synonymous with
“apoptosis”. It’s worth noting that Synonym can return a large set
of results, so we caution against providing a large set of GO IDs to Synonym:
Synonym(il31_go$GO)## $<NA>
## NULL
##
## $`GO:0005125`
## [1] "autocrine activity" "paracrine activity"
##
## $`GO:0005126`
## [1] "hematopoietin/interferon-class (D200-domain) cytokine receptor binding"
## [2] "hematopoietin/interferon-class (D200-domain) cytokine receptor ligand"
##
## $`GO:0005515`
## [1] "GO:0001948" "GO:0045308"
## [3] "protein amino acid binding" "glycoprotein binding"
##
## $`GO:0005576`
## [1] "extracellular"
##
## $`GO:0005615`
## [1] "intercellular space"
##
## $`GO:0005615`
## [1] "intercellular space"
##
## $`GO:0007165`
## [1] "GO:0023014"
## [2] "GO:0023015"
## [3] "GO:0023016"
## [4] "GO:0023033"
## [5] "GO:0023045"
## [6] "signaling pathway"
## [7] "signalling pathway"
## [8] "signal transduction by cis-phosphorylation"
## [9] "signal transduction by conformational transition"
## [10] "signal transduction by protein phosphorylation"
## [11] "signal transduction by trans-phosphorylation"
## [12] "signaling cascade"
## [13] "signalling cascade"A GO synonym was not found for the first identifier in the provided vector,
so an NA was returned.
These functions are useful for quickly retrieving information for a given GO
ID, but you’ll notice that the results are returned as a vector or list,
rather than a data frame. Depending on the application, this may be
useful - for example, these methods are handy for on-the-fly GO
term or definition lookups, but their format can be cumbersome to incorporate
into a data frame created by select.
Let’s return to the il31_go data frame we generated previously. How can we
incorporate the additional information obtained by Term, Definition,
and Synonym into this object? Assuming the output is the same length as
the number of rows in il31_go, the character vector obtained by Term,
Definition, or Synonym, can be easily appended as a new column in the
il31_go data frame:
trms <- Term(il31_go$GO)
class(trms)## [1] "character"length(trms) == length(il31_go$GO)## [1] TRUEil31_go$TERM <- trms
il31_go## SYMBOL PROBEID GO EVIDENCE ONTOLOGY TERM
## 1 IL31 10455-196 GO:0002376 IEA BP immune system process
## 2 IL31 10455-196 GO:0005125 IBA MF cytokine activity
## 3 IL31 10455-196 GO:0005126 IBA MF cytokine receptor binding
## 4 IL31 10455-196 GO:0005515 IPI MF protein binding
## 5 IL31 10455-196 GO:0005576 TAS CC extracellular region
## 6 IL31 10455-196 GO:0005615 IBA CC extracellular space
## 7 IL31 10455-196 GO:0005615 IDA CC extracellular space
## 8 IL31 10455-196 GO:0007165 IEA BP signal transductionThe same can be done with the output of Definition:
defs <- Definition(il31_go$GO)
class(defs)## [1] "character"length(defs) == length(il31_go$GO)## [1] TRUEil31_go$DEFINITION <- defs
il31_go[ ,c("SYMBOL", "PROBEID", "GO", "TERM", "DEFINITION")]## SYMBOL PROBEID GO TERM
## 1 IL31 10455-196 GO:0002376 immune system process
## 2 IL31 10455-196 GO:0005125 cytokine activity
## 3 IL31 10455-196 GO:0005126 cytokine receptor binding
## 4 IL31 10455-196 GO:0005515 protein binding
## 5 IL31 10455-196 GO:0005576 extracellular region
## 6 IL31 10455-196 GO:0005615 extracellular space
## 7 IL31 10455-196 GO:0005615 extracellular space
## 8 IL31 10455-196 GO:0007165 signal transduction
## DEFINITION
## 1 Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats.
## 2 The activity of a soluble extracellular gene product that interacts with a receptor to effect a change in the activity of the receptor to control the survival, growth, differentiation and effector function of tissues and cells.
## 3 Binding to a cytokine receptor.
## 4 Binding to a protein.
## 5 The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite.
## 6 That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid.
## 7 That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid.
## 8 The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell.However, this only works cleanly when the output is a character
vector with the same order and number of elements as the input vector.
With the list output of Synonym, the process is a little less
straightforward. In addition, it takes multiple steps to generate these
additional annotations and combine them with a select data frame.
Instead of performing so many steps, we can utilize another
Bionconductor annotation resource called GO.db to retrieve GO
annotation elements in a convenient data frame format.
The GO.db R package contains annotations describing the entire
Gene Ontology knowledgebase, assembled using data directly from the
GO website. GO.db provides a method
to easily retrieve the latest version of the Gene Ontology knowledgebase into
an R session. Like SomaScan.db, GO.db is an annotation
package that can be queried using the same five methods (select, keys,
keytypes, columns, and mapIds). By utilizing both SomaScan.db and
GO.db, it is possible to connect SeqIds to GO IDs, then add
additional GO annotations that are not available within SomaScan.db.
Let’s walk through an example. First, select a key (and corresponding GO ID) to use as a starting point:
go_ids <- select(SomaScan.db, "IL3RA", keytype = "SYMBOL",
columns = c("GO", "SYMBOL"))## 'select()' returned 1:many mapping between keys and columnsgo_ids## SYMBOL GO EVIDENCE ONTOLOGY
## 1 IL3RA GO:0004896 IBA MF
## 2 IL3RA GO:0004912 IDA MF
## 3 IL3RA GO:0005515 IPI MF
## 4 IL3RA GO:0005886 NAS CC
## 5 IL3RA GO:0005886 TAS CC
## 6 IL3RA GO:0009897 IBA CC
## 7 IL3RA GO:0019221 IBA BP
## 8 IL3RA GO:0019955 IBA MF
## 9 IL3RA GO:0036016 IEA BP
## 10 IL3RA GO:0038156 IDA BP
## 11 IL3RA GO:0043235 IBA CCAs shown previously, the GO ID, EVIDENCE code, and ONTOLOGY comprise the
extent of GO information contained in SomaScan.db. However, we can use the
GO ID (in the GO column) to connect these values to the annotations in
GO.db:
columns(GO.db)## [1] "DEFINITION" "GOID" "ONTOLOGY" "TERM"go_defs <- select(GO.db, keys = go_ids$GO,
columns = c("GOID", "TERM", "DEFINITION"))## 'select()' returned many:1 mapping between keys and columnsgo_defs## GOID TERM
## 1 GO:0004896 cytokine receptor activity
## 2 GO:0004912 interleukin-3 receptor activity
## 3 GO:0005515 protein binding
## 4 GO:0005886 plasma membrane
## 5 GO:0005886 plasma membrane
## 6 GO:0009897 external side of plasma membrane
## 7 GO:0019221 cytokine-mediated signaling pathway
## 8 GO:0019955 cytokine binding
## 9 GO:0036016 cellular response to interleukin-3
## 10 GO:0038156 interleukin-3-mediated signaling pathway
## 11 GO:0043235 receptor complex
## DEFINITION
## 1 Combining with a cytokine and transmitting the signal from one side of the membrane to the other to initiate a change in cell activity.
## 2 Combining with interleukin-3 and transmitting the signal from one side of the membrane to the other to initiate a change in cell activity.
## 3 Binding to a protein.
## 4 The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins.
## 5 The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins.
## 6 The leaflet of the plasma membrane that faces away from the cytoplasm and any proteins embedded or anchored in it or attached to its surface.
## 7 The series of molecular signals initiated by the binding of a cytokine to a receptor on the surface of a cell, and ending with the regulation of a downstream cellular process, e.g. transcription.
## 8 Binding to a cytokine, any of a group of proteins that function to control the survival, growth and differentiation of tissues and cells, and which have autocrine and paracrine activity.
## 9 Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of an interleukin-3 stimulus.
## 10 The series of molecular signals initiated by interleukin-3 binding to its receptor on the surface of a target cell, and ending with the regulation of a downstream cellular process, e.g. transcription.
## 11 Any protein complex that undergoes combination with a hormone, neurotransmitter, drug or intracellular messenger to initiate a change in cell function.merge(go_ids, go_defs, by.x = "GO", by.y = "GOID")## GO SYMBOL EVIDENCE ONTOLOGY TERM
## 1 GO:0004896 IL3RA IBA MF cytokine receptor activity
## 2 GO:0004912 IL3RA IDA MF interleukin-3 receptor activity
## 3 GO:0005515 IL3RA IPI MF protein binding
## 4 GO:0005886 IL3RA NAS CC plasma membrane
## 5 GO:0005886 IL3RA NAS CC plasma membrane
## 6 GO:0005886 IL3RA TAS CC plasma membrane
## 7 GO:0005886 IL3RA TAS CC plasma membrane
## 8 GO:0009897 IL3RA IBA CC external side of plasma membrane
## 9 GO:0019221 IL3RA IBA BP cytokine-mediated signaling pathway
## 10 GO:0019955 IL3RA IBA MF cytokine binding
## 11 GO:0036016 IL3RA IEA BP cellular response to interleukin-3
## 12 GO:0038156 IL3RA IDA BP interleukin-3-mediated signaling pathway
## 13 GO:0043235 IL3RA IBA CC receptor complex
## DEFINITION
## 1 Combining with a cytokine and transmitting the signal from one side of the membrane to the other to initiate a change in cell activity.
## 2 Combining with interleukin-3 and transmitting the signal from one side of the membrane to the other to initiate a change in cell activity.
## 3 Binding to a protein.
## 4 The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins.
## 5 The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins.
## 6 The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins.
## 7 The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins.
## 8 The leaflet of the plasma membrane that faces away from the cytoplasm and any proteins embedded or anchored in it or attached to its surface.
## 9 The series of molecular signals initiated by the binding of a cytokine to a receptor on the surface of a cell, and ending with the regulation of a downstream cellular process, e.g. transcription.
## 10 Binding to a cytokine, any of a group of proteins that function to control the survival, growth and differentiation of tissues and cells, and which have autocrine and paracrine activity.
## 11 Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of an interleukin-3 stimulus.
## 12 The series of molecular signals initiated by interleukin-3 binding to its receptor on the surface of a target cell, and ending with the regulation of a downstream cellular process, e.g. transcription.
## 13 Any protein complex that undergoes combination with a hormone, neurotransmitter, drug or intracellular messenger to initiate a change in cell function.Using this workflow, in just two steps we can link annotation information
between annotation package resources (i.e SomaScan.db <–> GO.db).
Note that the same workflow cannot be performed for KEGG pathways,
due to KEGG’s data sharing policy. Instead, the package
KEGGREST must be used. Rather than an annotation database-style
package (like SomaScan.db and GO.db), KEGGREST is a package
that provides a client interface in R to the KEGG REST
(REpresentational State Transfer) server. For reference,
REST is an interface that two computer
systems can use to securely exchange information over the internet. Queries
made with the KEGGREST package retrieve information
directly from the online KEGG database.
Let’s take the same select query as we used for GO, but modify it to obtain
KEGG pathway identifiers instead:
kegg_sel <- select(SomaScan.db, keys = "CD86", keytype = "SYMBOL",
columns = c("PROBEID", "PATH"))## 'select()' returned 1:many mapping between keys and columnskegg_sel## SYMBOL PROBEID PATH
## 1 CD86 5337-64 04514
## 2 CD86 5337-64 04620
## 3 CD86 5337-64 04672
## 4 CD86 5337-64 04940
## 5 CD86 5337-64 05320
## 6 CD86 5337-64 05322
## 7 CD86 5337-64 05323
## 8 CD86 5337-64 05330
## 9 CD86 5337-64 05332
## 10 CD86 5337-64 05416
## 11 CD86 6232-54 04514
## 12 CD86 6232-54 04620
## 13 CD86 6232-54 04672
## 14 CD86 6232-54 04940
## 15 CD86 6232-54 05320
## 16 CD86 6232-54 05322
## 17 CD86 6232-54 05323
## 18 CD86 6232-54 05330
## 19 CD86 6232-54 05332
## 20 CD86 6232-54 05416We can use the identifiers in the “PATH” column to query the KEGG database
using KEGGREST::keggGet():
# Add prefix indicating species (hsa = Homo sapiens)
hsa_names <- paste0("hsa", kegg_sel$PATH)
kegg_res <- keggGet(dbentries = hsa_names) |>
setNames(hsa_names[1:10L]) # Setting names for results list## Warning in keggGet(dbentries = hsa_names): More than 10 inputs supplied, only
## the first 10 results will be returned.Because so much information is returned by keggGet(), a maximum number of 10
entries are allowed. Input exceeding 10 entries will be truncated, and only
the first 10 results will be returned (as indicated in the warning message
above). Let’s take a look at what was returned for each KEGG pathway:
str(kegg_res$hsa04514)## List of 12
## $ ENTRY : Named chr "hsa04514"
## ..- attr(*, "names")= chr "Pathway"
## $ NAME : chr "Cell adhesion molecules - Homo sapiens (human)"
## $ DESCRIPTION: chr "Cell adhesion molecules (CAMs) are (glyco)proteins expressed on the cell surface and play a critical role in a "| __truncated__
## $ CLASS : chr "Environmental Information Processing; Signaling molecules and interaction"
## $ PATHWAY_MAP: Named chr "Cell adhesion molecules"
## ..- attr(*, "names")= chr "hsa04514"
## $ DRUG : chr [1:228] "D02800" "Alefacept (USAN/INN)" "D02811" "Alicaforsen sodium (USAN)" ...
## $ DBLINKS : chr "GO: 0050839"
## $ ORGANISM : Named chr "NA Homo sapiens (human) [GN:hsa]"
## ..- attr(*, "names")= chr "Homo sapiens (human) [GN:hsa]"
## $ GENE : chr [1:314] "965" "CD58; CD58 molecule [KO:K06492]" "914" "CD2; CD2 molecule [KO:K06449]" ...
## $ REL_PATHWAY: Named chr [1:5] "Adherens junction" "Tight junction" "Complement and coagulation cascades" "T cell receptor signaling pathway" ...
## ..- attr(*, "names")= chr [1:5] "hsa04520" "hsa04530" "hsa04610" "hsa04660" ...
## $ KO_PATHWAY : chr "ko04514"
## $ REFERENCE :List of 25
## ..$ :List of 4
## .. ..$ REFERENCE: chr "PMID:14690046"
## .. ..$ AUTHORS : chr "Barclay AN."
## .. ..$ TITLE : chr "Membrane proteins with immunoglobulin-like domains--a master superfamily of interaction molecules."
## .. ..$ JOURNAL : chr [1:2] "Semin Immunol 15:215-23 (2003)" "DOI:10.1016/S1044-5323(03)00047-2"
## ..$ :List of 4
## .. ..$ REFERENCE: chr "PMID:11910893"
## .. ..$ AUTHORS : chr "Sharpe AH, Freeman GJ."
## .. ..$ TITLE : chr "The B7-CD28 superfamily."
## .. ..$ JOURNAL : chr [1:2] "Nat Rev Immunol 2:116-26 (2002)" "DOI:10.1038/nri727"
## ..$ :List of 4
## .. ..$ REFERENCE: chr "PMID:9597126"
## .. ..$ AUTHORS : chr "Grewal IS, Flavell RA."
## .. ..$ TITLE : chr "CD40 and CD154 in cell-mediated immunity."
## .. ..$ JOURNAL : chr [1:2] "Annu Rev Immunol 16:111-35 (1998)" "DOI:10.1146/annurev.immunol.16.1.111"
## ..$ :List of 4
## .. ..$ REFERENCE: chr "PMID:16034094"
## .. ..$ AUTHORS : chr "Dardalhon V, Schubart AS, Reddy J, Meyers JH, Monney L, Sabatos CA, Ahuja R, Nguyen K, Freeman GJ, Greenfield E"| __truncated__
## .. ..$ TITLE : chr "CD226 is specifically expressed on the surface of Th1 cells and regulates their expansion and effector functions."
## .. ..$ JOURNAL : chr [1:2] "J Immunol 175:1558-65 (2005)" "DOI:10.4049/jimmunol.175.3.1558"
## ..$ :List of 4
## .. ..$ REFERENCE: chr "PMID:12234363"
## .. ..$ AUTHORS : chr "Montoya MC, Sancho D, Vicente-Manzanares M, Sanchez-Madrid F."
## .. ..$ TITLE : chr "Cell adhesion and polarity during immune interactions."
## .. ..$ JOURNAL : chr [1:2] "Immunol Rev 186:68-82 (2002)" "DOI:10.1034/j.1600-065X.2002.18607.x"
## ..$ :List of 4
## .. ..$ REFERENCE: chr "PMID:15071551"
## .. ..$ AUTHORS : chr "Dejana E."
## .. ..$ TITLE : chr "Endothelial cell-cell junctions: happy together."
## .. ..$ JOURNAL : chr [1:2] "Nat Rev Mol Cell Biol 5:261-70 (2004)" "DOI:10.1038/nrm1357"
## ..$ :List of 4
## .. ..$ REFERENCE: chr "PMID:14519386"
## .. ..$ AUTHORS : chr "Bazzoni G."
## .. ..$ TITLE : chr "The JAM family of junctional adhesion molecules."
## .. ..$ JOURNAL : chr [1:2] "Curr Opin Cell Biol 15:525-30 (2003)" "DOI:10.1016/S0955-0674(03)00104-2"
## ..$ :List of 4
## .. ..$ REFERENCE: chr "PMID:10798271"
## .. ..$ AUTHORS : chr "Becker BF, Heindl B, Kupatt C, Zahler S."
## .. ..$ TITLE : chr "Endothelial function and hemostasis."
## .. ..$ JOURNAL : chr [1:2] "Z Kardiol 89:160-7 (2000)" "DOI:10.1007/PL00007320"
## ..$ :List of 4
## .. ..$ REFERENCE: chr "PMID:9150551"
## .. ..$ AUTHORS : chr "Elangbam CS, Qualls CW Jr, Dahlgren RR."
## .. ..$ TITLE : chr "Cell adhesion molecules--update."
## .. ..$ JOURNAL : chr [1:2] "Vet Pathol 34:61-73 (1997)" "DOI:10.1177/030098589703400113"
## ..$ :List of 4
## .. ..$ REFERENCE: chr "PMID:12810109"
## .. ..$ AUTHORS : chr "Muller WA."
## .. ..$ TITLE : chr "Leukocyte-endothelial-cell interactions in leukocyte transmigration and the inflammatory response."
## .. ..$ JOURNAL : chr [1:2] "Trends Immunol 24:327-34 (2003)" "DOI:10.1016/S1471-4906(03)00117-0"
## ..$ :List of 4
## .. ..$ REFERENCE: chr "PMID:14519398"
## .. ..$ AUTHORS : chr "Yamagata M, Sanes JR, Weiner JA."
## .. ..$ TITLE : chr "Synaptic adhesion molecules."
## .. ..$ JOURNAL : chr [1:2] "Curr Opin Cell Biol 15:621-32 (2003)" "DOI:10.1016/S0955-0674(03)00107-8"
## ..$ :List of 4
## .. ..$ REFERENCE: chr "PMID:15882774"
## .. ..$ AUTHORS : chr "Ethell IM, Pasquale EB."
## .. ..$ TITLE : chr "Molecular mechanisms of dendritic spine development and remodeling."
## .. ..$ JOURNAL : chr [1:2] "Prog Neurobiol 75:161-205 (2005)" "DOI:10.1016/j.pneurobio.2005.02.003"
## ..$ :List of 4
## .. ..$ REFERENCE: chr "PMID:11050419"
## .. ..$ AUTHORS : chr "Benson DL, Schnapp LM, Shapiro L, Huntley GW."
## .. ..$ TITLE : chr "Making memories stick: cell-adhesion molecules in synaptic plasticity."
## .. ..$ JOURNAL : chr [1:2] "Trends Cell Biol 10:473-82 (2000)" "DOI:10.1016/S0962-8924(00)01838-9"
## ..$ :List of 4
## .. ..$ REFERENCE: chr "PMID:11860281"
## .. ..$ AUTHORS : chr "Rosdahl JA, Mourton TL, Brady-Kalnay SM."
## .. ..$ TITLE : chr "Protein kinase C delta (PKCdelta) is required for protein tyrosine phosphatase mu (PTPmu)-dependent neurite outgrowth."
## .. ..$ JOURNAL : chr [1:2] "Mol Cell Neurosci 19:292-306 (2002)" "DOI:10.1006/mcne.2001.1071"
## ..$ :List of 4
## .. ..$ REFERENCE: chr "PMID:10964748"
## .. ..$ AUTHORS : chr "Dunican DJ, Doherty P."
## .. ..$ TITLE : chr "The generation of localized calcium rises mediated by cell adhesion molecules and their role in neuronal growth cone motility."
## .. ..$ JOURNAL : chr [1:2] "Mol Cell Biol Res Commun 3:255-63 (2000)" "DOI:10.1006/mcbr.2000.0225"
## ..$ :List of 4
## .. ..$ REFERENCE: chr "PMID:12367625"
## .. ..$ AUTHORS : chr "Girault JA, Peles E."
## .. ..$ TITLE : chr "Development of nodes of Ranvier."
## .. ..$ JOURNAL : chr [1:2] "Curr Opin Neurobiol 12:476-85 (2002)" "DOI:10.1016/S0959-4388(02)00370-7"
## ..$ :List of 4
## .. ..$ REFERENCE: chr "PMID:10664064"
## .. ..$ AUTHORS : chr "Arroyo EJ, Scherer SS."
## .. ..$ TITLE : chr "On the molecular architecture of myelinated fibers."
## .. ..$ JOURNAL : chr [1:2] "Histochem Cell Biol 113:1-18 (2000)" "DOI:10.1007/s004180050001"
## ..$ :List of 4
## .. ..$ REFERENCE: chr "PMID:14556710"
## .. ..$ AUTHORS : chr "Salzer JL."
## .. ..$ TITLE : chr "Polarized domains of myelinated axons."
## .. ..$ JOURNAL : chr [1:2] "Neuron 40:297-318 (2003)" "DOI:10.1016/S0896-6273(03)00628-7"
## ..$ :List of 4
## .. ..$ REFERENCE: chr "PMID:15561584"
## .. ..$ AUTHORS : chr "Irie K, Shimizu K, Sakisaka T, Ikeda W, Takai Y."
## .. ..$ TITLE : chr "Roles and modes of action of nectins in cell-cell adhesion."
## .. ..$ JOURNAL : chr [1:2] "Semin Cell Dev Biol 15:643-56 (2004)" "DOI:10.1016/j.semcdb.2004.09.002"
## ..$ :List of 4
## .. ..$ REFERENCE: chr "PMID:15551862"
## .. ..$ AUTHORS : chr "Nakanishi H, Takai Y."
## .. ..$ TITLE : chr "Roles of nectins in cell adhesion, migration and polarization."
## .. ..$ JOURNAL : chr [1:2] "Biol Chem 385:885-92 (2004)" "DOI:10.1515/BC.2004.116"
## ..$ :List of 4
## .. ..$ REFERENCE: chr "PMID:15115723"
## .. ..$ AUTHORS : chr "Siu MK, Cheng CY."
## .. ..$ TITLE : chr "Extracellular matrix: recent advances on its role in junction dynamics in the seminiferous epithelium during spermatogenesis."
## .. ..$ JOURNAL : chr [1:2] "Biol Reprod 71:375-91 (2004)" "DOI:10.1095/biolreprod.104.028225"
## ..$ :List of 4
## .. ..$ REFERENCE: chr "PMID:15056568"
## .. ..$ AUTHORS : chr "Lee NP, Cheng CY."
## .. ..$ TITLE : chr "Adaptors, junction dynamics, and spermatogenesis."
## .. ..$ JOURNAL : chr [1:2] "Biol Reprod 71:392-404 (2004)" "DOI:10.1095/biolreprod.104.027268"
## ..$ :List of 4
## .. ..$ REFERENCE: chr "PMID:15728677"
## .. ..$ AUTHORS : chr "Inagaki M, Irie K, Ishizaki H, Tanaka-Okamoto M, Morimoto K, Inoue E, Ohtsuka T, Miyoshi J, Takai Y."
## .. ..$ TITLE : chr "Roles of cell-adhesion molecules nectin 1 and nectin 3 in ciliary body development."
## .. ..$ JOURNAL : chr [1:2] "Development 132:1525-37 (2005)" "DOI:10.1242/dev.01697"
## ..$ :List of 4
## .. ..$ REFERENCE: chr "PMID:12500939"
## .. ..$ AUTHORS : chr "Marthiens V, Gavard J, Lambert M, Mege RM."
## .. ..$ TITLE : chr "Cadherin-based cell adhesion in neuromuscular development."
## .. ..$ JOURNAL : chr [1:2] "Biol Cell 94:315-26 (2002)" "DOI:10.1016/S0248-4900(02)00005-9"
## ..$ :List of 4
## .. ..$ REFERENCE: chr "PMID:15923648"
## .. ..$ AUTHORS : chr "Krauss RS, Cole F, Gaio U, Takaesu G, Zhang W, Kang JS."
## .. ..$ TITLE : chr "Close encounters: regulation of vertebrate skeletal myogenesis by cell-cell contact."
## .. ..$ JOURNAL : chr [1:2] "J Cell Sci 118:2355-62 (2005)" "DOI:10.1242/jcs.02397"Some additional data manipulation will be required to extract the desired
information from the results of keggGet(). Let’s just extract the pathway
name (NAME):
kegg_names <- vapply(kegg_res, `[[`, i = "NAME", "", USE.NAMES = FALSE)
kegg_names## [1] "Cell adhesion molecules - Homo sapiens (human)"
## [2] "Toll-like receptor signaling pathway - Homo sapiens (human)"
## [3] "Intestinal immune network for IgA production - Homo sapiens (human)"
## [4] "Type I diabetes mellitus - Homo sapiens (human)"
## [5] "Autoimmune thyroid disease - Homo sapiens (human)"
## [6] "Systemic lupus erythematosus - Homo sapiens (human)"
## [7] "Rheumatoid arthritis - Homo sapiens (human)"
## [8] "Allograft rejection - Homo sapiens (human)"
## [9] "Graft-versus-host disease - Homo sapiens (human)"
## [10] "Viral myocarditis - Homo sapiens (human)"Now we can append this vector to our original results from select:
kegg_sel$PATHNAME <- kegg_names
kegg_sel## SYMBOL PROBEID PATH
## 1 CD86 5337-64 04514
## 2 CD86 5337-64 04620
## 3 CD86 5337-64 04672
## 4 CD86 5337-64 04940
## 5 CD86 5337-64 05320
## 6 CD86 5337-64 05322
## 7 CD86 5337-64 05323
## 8 CD86 5337-64 05330
## 9 CD86 5337-64 05332
## 10 CD86 5337-64 05416
## 11 CD86 6232-54 04514
## 12 CD86 6232-54 04620
## 13 CD86 6232-54 04672
## 14 CD86 6232-54 04940
## 15 CD86 6232-54 05320
## 16 CD86 6232-54 05322
## 17 CD86 6232-54 05323
## 18 CD86 6232-54 05330
## 19 CD86 6232-54 05332
## 20 CD86 6232-54 05416
## PATHNAME
## 1 Cell adhesion molecules - Homo sapiens (human)
## 2 Toll-like receptor signaling pathway - Homo sapiens (human)
## 3 Intestinal immune network for IgA production - Homo sapiens (human)
## 4 Type I diabetes mellitus - Homo sapiens (human)
## 5 Autoimmune thyroid disease - Homo sapiens (human)
## 6 Systemic lupus erythematosus - Homo sapiens (human)
## 7 Rheumatoid arthritis - Homo sapiens (human)
## 8 Allograft rejection - Homo sapiens (human)
## 9 Graft-versus-host disease - Homo sapiens (human)
## 10 Viral myocarditis - Homo sapiens (human)
## 11 Cell adhesion molecules - Homo sapiens (human)
## 12 Toll-like receptor signaling pathway - Homo sapiens (human)
## 13 Intestinal immune network for IgA production - Homo sapiens (human)
## 14 Type I diabetes mellitus - Homo sapiens (human)
## 15 Autoimmune thyroid disease - Homo sapiens (human)
## 16 Systemic lupus erythematosus - Homo sapiens (human)
## 17 Rheumatoid arthritis - Homo sapiens (human)
## 18 Allograft rejection - Homo sapiens (human)
## 19 Graft-versus-host disease - Homo sapiens (human)
## 20 Viral myocarditis - Homo sapiens (human)Other pieces of information can be extracted to the list and reduced to a character vector or used to build a data frame, which can then be appended to or merged similar to the pathway name in the code chunks above. For more details about what can be done with the package, see KEGGREST.
Similar to the extended GO annotation in the previous section, positional
annotation cannot currently be performed within SomaScan.db.
SomaScan.db is a platform-centric annotation package, built around the
probes of the SomaScan protein assay, and positional annotation is not
within its scope. However, it is possible to retrieve positional
annotations by linking to other Bioconductor annotation resources, which can
then be combined with SomaScan.db in a two-step process (similar to above).
The first step uses SomaScan.db to retrieve gene-level information
corresponding to SomaScan analytes; the second requires a human transcriptome
or organism-centric annotation package to retrieve the desired chromosomal
locations.
We will provide a brief example of this using the popular organism-centric
package, EnsDb.Hsapiens.v75, which contains a database of human
annotations derived from Ensembl release 75. However, this procedure can
also be performed using transcriptome-centric annotation packages like
TxDb.Hsapiens.UCSC.hg19.knownGene.
Let’s say we are interested in collecting position information
associated with the protein target corresponding to SeqId = 11138-16.
First, we must determine which gene this SeqId maps to:
pos_sel <- select(SomaScan.db, "11138-16", columns = c("SYMBOL", "GENENAME",
"ENTREZID", "ENSEMBL"))## 'select()' returned 1:1 mapping between keys and columnspos_sel## PROBEID SYMBOL GENENAME ENTREZID ENSEMBL
## 1 11138-16 RUNX3 RUNX family transcription factor 3 864 ENSG00000020633We now know this probe targets protein encoded by the RUNX3
gene. We can use EnsDb.Hsapiens.v75 to retrieve positional
information about RUNX3, like which chromosome the
RUNX3 is on, its start and stop position, and how many exons it
has (at the time of Ensembl’s v75 release):
# Install package from Bioconductor, if not already installed
if (!require("EnsDb.Hsapiens.v75", quietly = TRUE)) {
BiocManager::install("EnsDb.Hsapiens.v75")
}
# The central keys of the organism-level database are the Ensembl gene ID
keys(EnsDb.Hsapiens.v75)[1:10L]
# Also contains the Ensembl gene ID, so this column can be used for merging
grep("ENSEMBL", columns(SomaScan.db), value = TRUE)
# These columns will inform us as to what positional information we can
# retrieve from the organism-level database
columns(EnsDb.Hsapiens.v75)
# Build a query to retrieve the prot IDs and start/stop pos of protein domains
pos_res <- select(EnsDb.Hsapiens.v75, keys = "ENSG00000020633",
columns = c("GENEBIOTYPE", "SEQCOORDSYSTEM", "GENEID",
"PROTEINID", "PROTDOMSTART", "PROTDOMEND"))
# Merge back into `pos_sel` using the "GENEID" column
merge(pos_sel, pos_res, by.x = "ENSEMBL", by.y = "GENEID")As mentioned in the Introductory Vignette (vignette("SomaScan-db", package = "SomaScan.db")),
the SomaScan.db annotation database can be queried using values other than
the central database key, the SeqId (i.e. the “PROBEID” column). This
section will describe additional methods of retrieving information from the
database without using the SeqId.
The annotations in SomaScan.db can be used to answer general questions about
SomaScan, without the need for a SomaScan dataset/ADAT file as a starting
point. For example, if one were interested in proteins involved in cancer
progression and metastasis (and therefore cell adhesion), is the SomaScan
menu capable of measuring proteins involved in cell adhesion? If so, how
many of these proteins can be measured with SomaScan?
We can answer this by examining the coverage of the GO term
“cell adhesion” in both the 5k and 7k SomaScan menus. We don’t need the
GO identifier to get started, as that information can be retrieved from
GO.db using the name of the term as the key:
select(GO.db, keys = "cell adhesion", keytype = "TERM",
columns = c("GOID", "TERM"))## 'select()' returned 1:1 mapping between keys and columns## TERM GOID
## 1 cell adhesion GO:0007155Now that we have the GO ID, we can search in SomaScan.db to determine
how many SeqIds are associated with cell adhesion.
cellAd_ids <- select(SomaScan.db, keys = "GO:0007155", keytype = "GO",
columns = "PROBEID", "UNIPROTID")## The 'menu' argument can only be used when 'keytype = 'PROBEID''. The results of this query will contain all analytes from the 11k menu.## 'select()' returned 1:many mapping between keys and columnshead(cellAd_ids, n = 10L)## GO PROBEID EVIDENCE ONTOLOGY
## 1 GO:0007155 10037-98 IBA BP
## 2 GO:0007155 10511-10 IEA BP
## 3 GO:0007155 10521-10 IEA BP
## 4 GO:0007155 10539-30 IEA BP
## 5 GO:0007155 10558-26 IBA BP
## 6 GO:0007155 10702-1 IBA BP
## 7 GO:0007155 10748-216 IBA BP
## 8 GO:0007155 10907-116 IEA BP
## 9 GO:0007155 10980-11 IDA BP
## 10 GO:0007155 11067-13 NAS BP# Total number of SeqIds associated with cell adhesion
unique(cellAd_ids$PROBEID) |> length()## [1] 451There are 451 unique SeqIds associated
with the “cell adhesion” GO term (unique is important here because the data
frame above may contain multiple entries per SeqId, due to the “EVIDENCE”
column). There are 10731 total SeqIds in the
SomaScan.db database, so
4.2%
of keys in the database are associated with cell adhesion.
How many of the total proteins in the cell adhesion GO term are covered by
the SomaScan menu? To answer this question, we first must use another
annotation package, org.Hs.eg.db, to retrieve a list of all human
UniProt IDs associated with the “cell adhesion” GO term.
cellAd_prots <- select(org.Hs.eg.db,
keys = "GO:0007155",
keytype = "GO",
columns = "UNIPROT")## 'select()' returned 1:many mapping between keys and columns# Again, we take the unique set of proteins
length(unique(cellAd_prots$UNIPROT))## [1] 3294The GO term GO:0007155 (cell adhesion) contains a total of
3294 unique human UniProt IDs. Now we
can check to see how many of these are covered by the SomaScan menu by
searching for the proteins in SomaScan.db with select:
cellAd_covProts <- select(SomaScan.db, keys = unique(cellAd_prots$UNIPROT),
keytype = "UNIPROT", columns = "PROBEID")## 'select()' returned 1:many mapping between keys and columnshead(cellAd_covProts, n = 20L)## UNIPROT PROBEID
## 1 P42684 3342-76
## 2 P42684 5261-13
## 3 A0M8X0 3342-76
## 4 A0M8X0 5261-13
## 5 B7UEF2 3342-76
## 6 B7UEF2 5261-13
## 7 B7UEF3 3342-76
## 8 B7UEF3 5261-13
## 9 B7UEF4 3342-76
## 10 B7UEF4 5261-13
## 11 B7UEF5 3342-76
## 12 B7UEF5 5261-13
## 13 Q5T0X6 3342-76
## 14 Q5T0X6 5261-13
## 15 Q5W0C5 3342-76
## 16 Q5W0C5 5261-13
## 17 Q6NZY6 3342-76
## 18 Q6NZY6 5261-13
## 19 Q7Z301 3342-76
## 20 Q7Z301 5261-13select will return an NA value if a key is not found in the database. As
seen above, some proteins in GO:0007155 do not map to a SeqId in
SomaScan.db. To get an accurate count of the proteins that do map to a
SeqId, we must remove the unmapped proteins by filtering out rows with NA
values:
cellAd_covProts <- cellAd_covProts[!is.na(cellAd_covProts$PROBEID),]
cellAd_covIDs <- unique(cellAd_covProts$UNIPROT)
length(cellAd_covIDs)## [1] 2482We removed duplicates from the list of proteins provided as keys, to get a final count of 2482 proteins (75.35%) from the “cell adhesion” GO term that are covered by the SomaScan menu.
Does this number differ between versions of the SomaScan Menu? Remember that
the 7k menu contains all of the SeqIds in the 5k menu, so what this really
tells us is: were analytes targeting cell adhesion-related proteins added in
the 7k menu?
cellAd_menu <- lapply(c("5k", "7k"), function(x) {
df <- select(SomaScan.db, keys = unique(cellAd_prots$UNIPROT),
keytype = "UNIPROT", columns = "PROBEID",
menu = x)
# Again, removing probes that do not map to a cell adhesion protein
df <- df[!is.na(df$PROBEID),]
}) |> setNames(c("somascan_5k", "somascan_7k"))## The 'menu' argument can only be used when 'keytype = 'PROBEID''. The results of this query will contain all analytes from the 11k menu.## 'select()' returned 1:many mapping between keys and columns## The 'menu' argument can only be used when 'keytype = 'PROBEID''. The results of this query will contain all analytes from the 11k menu.## 'select()' returned 1:many mapping between keys and columnsidentical(cellAd_menu$somascan_5k, cellAd_menu$somascan_7k)## [1] TRUEIn this example, the number of SeqIds associated with cell adhesion does
not differ between SomaScan menu versions (the list of SeqIds is
identical). The differences between menu versions can be explored with the
menu argument of select, or via the somascan_menu data object (this is
explained in the Introductory Vignette).
A number of gene families are targeted by reagents in the SomaScan assay. How
can these be interrogated using SomaScan.db? Is the package capable of
searching for/within specific gene families? The answer is yes, but
a specific function does not exist for analyzing gene families as a whole.
Instead, by using features of select and keys, SomaScan.db can
be queried for common features connecting gene families of interest - more
specifically, the match= argument of select and the pattern= argument of
keys can be used to retrieve gene family members that contain a common
pattern in their name.
The keys method is capable of using regular expressions (“regex”) to search
for keys in the database that contain a specific pattern of characters. This
feature is especially useful when looking for annotations for a gene family.
For example, a regex pattern can be used to retrieve a list of all IL17
receptor family genes in the database:
il17_family <- keys(SomaScan.db, keytype = "SYMBOL", pattern = "IL17")Those keys can then be used to query the database with select:
select(SomaScan.db, keys = il17_family, keytype = "SYMBOL",
columns = c("PROBEID", "UNIPROT", "GENENAME"))## 'select()' returned 1:many mapping between keys and columns## SYMBOL PROBEID UNIPROT GENENAME
## 1 IL17A 13718-1 Q16552 interleukin 17A
## 2 IL17A 13718-1 Q5T2P0 interleukin 17A
## 3 IL17A 13718-1 Q6NZ94 interleukin 17A
## 4 IL17A 21897-4 Q16552 interleukin 17A
## 5 IL17A 21897-4 Q5T2P0 interleukin 17A
## 6 IL17A 21897-4 Q6NZ94 interleukin 17A
## 7 IL17A 31553-5 Q16552 interleukin 17A
## 8 IL17A 31553-5 Q5T2P0 interleukin 17A
## 9 IL17A 31553-5 Q6NZ94 interleukin 17A
## 10 IL17A 3498-53 Q16552 interleukin 17A
## 11 IL17A 3498-53 Q5T2P0 interleukin 17A
## 12 IL17A 3498-53 Q6NZ94 interleukin 17A
## 13 IL17A 9170-24 Q16552 interleukin 17A
## 14 IL17A 9170-24 Q5T2P0 interleukin 17A
## 15 IL17A 9170-24 Q6NZ94 interleukin 17A
## 16 IL17RA 2992-59 Q96F46 interleukin 17 receptor A
## 17 IL17RA 2992-59 O43844 interleukin 17 receptor A
## 18 IL17RA 2992-59 Q20WK1 interleukin 17 receptor A
## 19 IL17C 9255-13 Q3MIG8 interleukin 17C
## 20 IL17C 9255-13 Q9HC75 interleukin 17C
## 21 IL17C 9255-13 Q9P0M4 interleukin 17C
## 22 IL17C 9255-5 Q3MIG8 interleukin 17C
## 23 IL17C 9255-5 Q9HC75 interleukin 17C
## 24 IL17C 9255-5 Q9P0M4 interleukin 17C
## 25 IL17B 14022-17 Q9UHF5 interleukin 17B
## 26 IL17B 14022-17 Q14CE5 interleukin 17B
## 27 IL17B 14022-17 Q6IAG3 interleukin 17B
## 28 IL17B 3499-77 Q9UHF5 interleukin 17B
## 29 IL17B 3499-77 Q14CE5 interleukin 17B
## 30 IL17B 3499-77 Q6IAG3 interleukin 17B
## 31 IL17D 4136-40 B1AM69 interleukin 17D
## 32 IL17D 4136-40 Q8TAD2 interleukin 17D
## 33 IL17RD 3376-49 B4DXM5 interleukin 17 receptor D
## 34 IL17RD 3376-49 Q2NKP7 interleukin 17 receptor D
## 35 IL17RD 3376-49 Q58EZ7 interleukin 17 receptor D
## 36 IL17RD 3376-49 Q6RVF4 interleukin 17 receptor D
## 37 IL17RD 3376-49 Q6UWI5 interleukin 17 receptor D
## 38 IL17RD 3376-49 Q8N113 interleukin 17 receptor D
## 39 IL17RD 3376-49 Q8NFM7 interleukin 17 receptor D
## 40 IL17RD 3376-49 Q8NFS0 interleukin 17 receptor D
## 41 IL17RD 3376-49 Q9UFA0 interleukin 17 receptor D
## 42 IL17RB 35707-93 Q9BPZ0 interleukin 17 receptor B
## 43 IL17RB 35707-93 Q9NRL4 interleukin 17 receptor B
## 44 IL17RB 35707-93 Q9NRM5 interleukin 17 receptor B
## 45 IL17RB 35707-93 Q9NRM6 interleukin 17 receptor B
## 46 IL17RB 5084-154 Q9BPZ0 interleukin 17 receptor B
## 47 IL17RB 5084-154 Q9NRL4 interleukin 17 receptor B
## 48 IL17RB 5084-154 Q9NRM5 interleukin 17 receptor B
## 49 IL17RB 5084-154 Q9NRM6 interleukin 17 receptor B
## 50 IL17RB 6262-14 Q9BPZ0 interleukin 17 receptor B
## 51 IL17RB 6262-14 Q9NRL4 interleukin 17 receptor B
## 52 IL17RB 6262-14 Q9NRM5 interleukin 17 receptor B
## 53 IL17RB 6262-14 Q9NRM6 interleukin 17 receptor B
## 54 IL17RC 5468-67 A0A8Q3SJ19 interleukin 17 receptor C
## 55 IL17RC 5468-67 Q8NAC3 interleukin 17 receptor C
## 56 IL17RC 5468-67 A0A8Q3SIU5 interleukin 17 receptor C
## 57 IL17RC 5468-67 A0A8Q3SIV5 interleukin 17 receptor C
## 58 IL17RC 5468-67 A0A8Q3SJJ9 interleukin 17 receptor C
## 59 IL17RC 5468-67 A0A8Q3WM30 interleukin 17 receptor C
## 60 IL17RC 5468-67 A0A8Q3SJ01 interleukin 17 receptor C
## 61 IL17RC 5468-67 A8BWC1 interleukin 17 receptor C
## 62 IL17RC 5468-67 A8BWC9 interleukin 17 receptor C
## 63 IL17RC 5468-67 A8BWD5 interleukin 17 receptor C
## 64 IL17RC 5468-67 E9PHG1 interleukin 17 receptor C
## 65 IL17RC 5468-67 E9PHJ6 interleukin 17 receptor C
## 66 IL17RC 5468-67 Q6UVY3 interleukin 17 receptor C
## 67 IL17RC 5468-67 Q6UWD4 interleukin 17 receptor C
## 68 IL17RC 5468-67 Q8NFS1 interleukin 17 receptor C
## 69 IL17RC 5468-67 Q9BR97 interleukin 17 receptor C
## 70 IL17RC 5468-67 C9JSZ3 interleukin 17 receptor C
## 71 IL17F 14026-24 F1JZ09 interleukin 17F
## 72 IL17F 14026-24 Q6NSI0 interleukin 17F
## 73 IL17F 14026-24 Q7Z6P4 interleukin 17F
## 74 IL17F 14026-24 Q96PD4 interleukin 17F
## 75 IL17F 14026-24 Q96PI8 interleukin 17F
## 76 IL17F 14026-24 Q9NUE6 interleukin 17F
## 77 IL17F 21897-4 F1JZ09 interleukin 17F
## 78 IL17F 21897-4 Q6NSI0 interleukin 17F
## 79 IL17F 21897-4 Q7Z6P4 interleukin 17F
## 80 IL17F 21897-4 Q96PD4 interleukin 17F
## 81 IL17F 21897-4 Q96PI8 interleukin 17F
## 82 IL17F 21897-4 Q9NUE6 interleukin 17F
## 83 IL17F 2775-54 F1JZ09 interleukin 17F
## 84 IL17F 2775-54 Q6NSI0 interleukin 17F
## 85 IL17F 2775-54 Q7Z6P4 interleukin 17F
## 86 IL17F 2775-54 Q96PD4 interleukin 17F
## 87 IL17F 2775-54 Q96PI8 interleukin 17F
## 88 IL17F 2775-54 Q9NUE6 interleukin 17F
## 89 IL17F 31553-5 F1JZ09 interleukin 17F
## 90 IL17F 31553-5 Q6NSI0 interleukin 17F
## 91 IL17F 31553-5 Q7Z6P4 interleukin 17F
## 92 IL17F 31553-5 Q96PD4 interleukin 17F
## 93 IL17F 31553-5 Q96PI8 interleukin 17F
## 94 IL17F 31553-5 Q9NUE6 interleukin 17F
## 95 IL17RE 20535-68 B4DMZ3 interleukin 17 receptor E
## 96 IL17RE 20535-68 B2RB34 interleukin 17 receptor E
## 97 IL17RE 20535-68 B2RNR1 interleukin 17 receptor E
## 98 IL17RE 20535-68 B9EH65 interleukin 17 receptor E
## 99 IL17RE 20535-68 J3KQN7 interleukin 17 receptor E
## 100 IL17RE 20535-68 Q6P532 interleukin 17 receptor E
## 101 IL17RE 20535-68 Q8N8H7 interleukin 17 receptor E
## 102 IL17RE 20535-68 Q8N8H8 interleukin 17 receptor E
## 103 IL17RE 20535-68 Q8NFR9 interleukin 17 receptor E
## 104 IL17RE 20535-68 Q8TEC2 interleukin 17 receptor E
## 105 IL17REL <NA> <NA> <NA>If multiple gene families are of interest, the keys argument of select
(in combination with match=TRUE) can support a regex pattern, and will
accomplish both of the previous steps in a single call:
select(SomaScan.db, keys = "NOTCH|ZF", keytype = "SYMBOL",
columns = c("PROBEID", "SYMBOL", "GENENAME"), match = TRUE)## 'select()' returned 1:many mapping between keys and columns## SYMBOL PROBEID GENENAME
## 1 NOTCH2 11297-54 notch receptor 2
## 2 NOTCH2 5106-52 notch receptor 2
## 3 NOTCH2 8407-84 notch receptor 2
## 4 ZFYVE27 13432-9 zinc finger FYVE-type containing 27
## 5 ZFYVE27 9102-28 zinc finger FYVE-type containing 27
## 6 ZFP91 13651-54 ZFP91 zinc finger protein, atypical E3 ubiquitin ligase
## 7 MZF1 14662-6 myeloid zinc finger 1
## 8 ZFAND5 18317-111 zinc finger AN1-type containing 5
## 9 ZFAND1 19173-5 zinc finger AN1-type containing 1
## 10 CREBZF 21134-9 CREB/ATF bZIP transcription factor
## 11 ZFAND3 21875-31 zinc finger AN1-type containing 3
## 12 ZFP42 22038-30 ZFP42 zinc finger protein
## 13 ZFP36 22395-7 ZFP36 ring finger protein
## 14 ZFAND2B 23319-6 zinc finger AN1-type containing 2B
## 15 ZFYVE19 26166-2 zinc finger FYVE-type containing 19
## 16 NOTCH4 26329-69 notch receptor 4
## 17 FEZF2 29288-262 FEZ family zinc finger 2
## 18 ZFP37 29349-78 ZFP37 zinc finger protein
## 19 VEZF1 29356-246 vascular endothelial zinc finger 1
## 20 ZFP69B 29397-101 ZFP69 zinc finger protein B
## 21 IKZF3 32719-135 IKAROS family zinc finger 3
## 22 NOTCH2NLB 33381-1 notch 2 N-terminal like B
## 23 ZFP57 33456-11 ZFP57 zinc finger protein
## 24 ZFYVE21 33555-5 zinc finger FYVE-type containing 21
## 25 ZFAND6 35258-74 zinc finger AN1-type containing 6
## 26 NOTCH1 5107-7 notch receptor 1
## 27 NOTCH3 5108-72 notch receptor 3The GENENAME column can also support a regex pattern, and can be used to
search for keywords that are associated with specific gene families (and
not just the gene symbols themselves). Examples include “homeobox”,
“zinc finger”, “notch”, etc.
select(SomaScan.db, keys = "homeobox", keytype = "GENENAME",
columns = c("PROBEID", "SYMBOL"), match = TRUE)## 'select()' returned 1:1 mapping between keys and columns## GENENAME PROBEID SYMBOL
## 1 homeobox A11 22375-15 HOXA11
## 2 homeobox A5 22376-95 HOXA5
## 3 homeobox C11 22474-28 HOXC11
## 4 homeobox D4 22476-115 HOXD4
## 5 homeobox C9 28147-8 HOXC9
## 6 homeobox A10 28162-49 HOXA10
## 7 homeobox B6 28467-4 HOXB6
## 8 homeobox A9 28495-10 HOXA9
## 9 homeobox C6 28769-70 HOXC6
## 10 homeobox B7 29263-40 HOXB7
## 11 homeobox C5 29420-65 HOXC5
## 12 homeobox A7 30504-3 HOXA7
## 13 homeobox D10 33419-10 HOXD10
## 14 homeobox A6 33426-10 HOXA6
## 15 homeobox B8 33444-17 HOXB8
## 16 homeobox C8 33624-3 HOXC8
## 17 homeobox B1 34260-43 HOXB1
## 18 homeobox B4 34757-64 HOXB4sessionInfo()## 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
##
## 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] org.Hs.eg.db_3.19.1 KEGGREST_1.43.0 GO.db_3.19.1
## [4] tibble_3.2.1 SomaScan.db_0.99.10 AnnotationDbi_1.65.2
## [7] IRanges_2.37.1 S4Vectors_0.41.7 Biobase_2.63.1
## [10] BiocGenerics_0.49.1 withr_3.0.0 BiocStyle_2.31.0
##
## loaded via a namespace (and not attached):
## [1] sass_0.4.9 utf8_1.2.4 RSQLite_2.3.6
## [4] digest_0.6.35 magrittr_2.0.3 evaluate_0.23
## [7] bookdown_0.39 fastmap_1.1.1 blob_1.2.4
## [10] jsonlite_1.8.8 GenomeInfoDb_1.39.14 DBI_1.2.2
## [13] BiocManager_1.30.22 httr_1.4.7 fansi_1.0.6
## [16] UCSC.utils_0.99.7 Biostrings_2.71.6 jquerylib_0.1.4
## [19] cli_3.6.2 rlang_1.1.3 crayon_1.5.2
## [22] XVector_0.43.1 bit64_4.0.5 cachem_1.0.8
## [25] yaml_2.3.8 tools_4.4.0 memoise_2.0.1
## [28] GenomeInfoDbData_1.2.12 curl_5.2.1 vctrs_0.6.5
## [31] R6_2.5.1 png_0.1-8 lifecycle_1.0.4
## [34] zlibbioc_1.49.3 bit_4.0.5 pkgconfig_2.0.3
## [37] bslib_0.7.0 pillar_1.9.0 glue_1.7.0
## [40] xfun_0.43 knitr_1.46 htmltools_0.5.8.1
## [43] rmarkdown_2.26 compiler_4.4.0