Example ADAT Workflow Using SomaScan.db
24 April 2024
Package
SomaScan.db 0.99.10
1 Introduction
This vignette will illustrate how the SomaScan.db package can be used to
extend the results of basic bioinformatic analyses performed on SomaScan data.
In this vignette, we will use tools from SomaDataIO, in
conjunction with SomaScan.db, to identify and annotate proteins
(and ultimately, genes and their biological pathways) that are associated
with a clinical variable of interest; e.g. Age.
This workflow assumes the user has a basic knowledge of the R programming language. In addition, it is assumed that the user is familiar with the ADAT format, as well as the documentation in SomaDataIO and the SomaLogic-Data Github repository. For more information about the ADAT format, as well as a detailed description of the fields found in an ADAT, please reference the SomaLogic-Data repository README.
library(dplyr)
library(GO.db)
library(SomaDataIO)
library(SomaScan.db)2 Reading an ADAT
The ADAT file format is a SomaLogic-specific, tab-delimited text file designed
to store SomaScan study data. The example ADAT file used for this walkthrough,
example_data10.adat, contains a SomaScan 5k study from a set of 10 human
samples. The measurements and identifiers have been altered to protect
personally identifiable information (PII), while retaining the underlying
biological signal as much as possible.
To begin, we will utilize SomaDataIO::read_adat() to load the example
ADAT file:
# Retrieve the example ADAT file from the SomaDataIO package
file <- system.file("extdata", "example_data10.adat",
package = "SomaDataIO", mustWork = TRUE
)
# Read in the file
adat <- SomaDataIO::read_adat(file)
adat## ══ SomaScan Data ═══════════════════════════════════════════════════════════════
## SomaScan version V4 (5k)
## Signal Space 5k
## Attributes intact ✓
## Rows 10
## Columns 5318
## Clinical Data 34
## Features 5284
## ── Column Meta ─────────────────────────────────────────────────────────────────
## ℹ SeqId, SeqIdVersion, SomaId, TargetFullName, Target, UniProt, EntrezGeneID,
## ℹ EntrezGeneSymbol, Organism, Units, Type, Dilution, PlateScale_Reference,
## ℹ CalReference, Cal_Example_Adat_Set001, ColCheck,
## ℹ CalQcRatio_Example_Adat_Set001_170255, QcReference_170255,
## ℹ Cal_Example_Adat_Set002, CalQcRatio_Example_Adat_Set002_170255, Dilution2
## ── Tibble ──────────────────────────────────────────────────────────────────────
## # A tibble: 10 × 5,319
## row_names PlateId PlateRunDate ScannerID PlatePosition SlideId Subarray
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 258495800012_3 Example… 2020-06-18 SG152144… H9 2.58e11 3
## 2 258495800004_7 Example… 2020-06-18 SG152144… H8 2.58e11 7
## 3 258495800010_8 Example… 2020-06-18 SG152144… H7 2.58e11 8
## 4 258495800003_4 Example… 2020-06-18 SG152144… H6 2.58e11 4
## 5 258495800009_4 Example… 2020-06-18 SG152144… H5 2.58e11 4
## 6 258495800012_8 Example… 2020-06-18 SG152144… H4 2.58e11 8
## 7 258495800001_3 Example… 2020-06-18 SG152144… H3 2.58e11 3
## 8 258495800004_8 Example… 2020-06-18 SG152144… H2 2.58e11 8
## 9 258495800001_8 Example… 2020-06-18 SG152144… H12 2.58e11 8
## 10 258495800004_3 Example… 2020-06-18 SG152144… H11 2.58e11 3
## # ℹ 5,312 more variables: SampleId <chr>, SampleType <chr>,
## # PercentDilution <int>, SampleMatrix <chr>, Barcode <lgl>, Barcode2d <chr>,
## # SampleName <lgl>, SampleNotes <lgl>, AliquotingNotes <lgl>,
## # SampleDescription <chr>, …
## ════════════════════════════════════════════════════════════════════════════════2.1 Data Prep and Exploration
Next, we will perform some preliminary data exploration of our variable
of interest (Age):
summary(adat$Age)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 36.00 41.00 55.00 55.78 71.00 77.00 1hist(adat$Age,
xlab = "Age (years)",
main = "Age Distribution"
)
The subjects in this example data set are all adults, with age ranging from 36 to 77 years.
However, as suggested by the summary table above, there is 1 patient that is missing age information:
dplyr::filter(adat, is.na(Sex)) %>%
dplyr::select(SlideId, Age)## ══ SomaScan Data ═══════════════════════════════════════════════════════════════
## SomaScan version V4 (5k)
## Signal Space 5k
## Attributes intact ✓
## Rows 1
## Columns 2
## Clinical Data 2
## Features 0
## ── Column Meta ─────────────────────────────────────────────────────────────────
## ℹ SeqId, SeqIdVersion, SomaId, TargetFullName, Target, UniProt, EntrezGeneID,
## ℹ EntrezGeneSymbol, Organism, Units, Type, Dilution, PlateScale_Reference,
## ℹ CalReference, Cal_Example_Adat_Set001, ColCheck,
## ℹ CalQcRatio_Example_Adat_Set001_170255, QcReference_170255,
## ℹ Cal_Example_Adat_Set002, CalQcRatio_Example_Adat_Set002_170255, Dilution2
## ── Tibble ──────────────────────────────────────────────────────────────────────
## # A tibble: 1 × 3
## row_names SlideId Age
## <chr> <dbl> <int>
## 1 258495800004_3 258495800004 NA
## ════════════════════════════════════════════════════════════════════════════════Note: You may notice that the select function above has the namespace
(dplyr) specified; this is to distinguish between
dplyr::select() and SomaScan.db::select(), both of which will be used in
this vignette. A more thorough explanation of why this is important can be
found in the “select namespace collision” section.
Samples corresponding to this patient will need to be removed before proceeding:
adat <- dplyr::filter(adat, !is.na(Age))As a quick sanity check: this ADAT was generated using the 5k SomaScan assay menu, which indicates that there should be RFU values for approximately 5,000 analytes.
analytes <- SomaDataIO::getAnalytes(adat)
head(analytes)## [1] "seq.10000.28" "seq.10001.7" "seq.10003.15" "seq.10006.25" "seq.10008.43"
## [6] "seq.10011.65"length(analytes)## [1] 5284Indeed, there are 5284 analytes in this example ADAT file, consistent with the assay version.
2.2 Note on SeqId format
The values obtained in analytes above contains the same information as a
SomaLogic SeqId, just converted to a more R-friendly format (i.e. the
seq-prefix and "." delimiters). This format of identifier is
used throughout SomaDataIO, and is also compatible with SomaScan.db.
Values obtained from SomaDataIO (with the seq-prefix) are
converted to SeqIds automatically when used by methods in SomaScan.db.
3 Identifying Associations
We can now examine the data to see if any analytes are correlated with the
variable Age. First, however, the data be be pre-processed. SomaLogic
generally recommends to log-transform the RFU values in an ADAT prior to
analysis.
log10_adat <- log10(adat)After log-transformation, we can examine the correlations and identify
analytes that are positively correlated with Age. Here, correlations will be
calculated using stats::cor.test():
# Calculate correlations for each analyte
cors_df <- lapply(analytes, function(x) {
results <- stats::cor.test(log10_adat$Age, log10_adat[[x]],
method = "spearman", exact = FALSE)
results <- results[c("estimate", "p.value")]
unlist(results)
}) %>% setNames(analytes)
# Bind results together into a single dataframe
cors_df <- dplyr::bind_rows(cors_df, .id = "analytes")
# Isolate top positive correlations
top_posCor <- cors_df %>%
dplyr::filter(p.value < 0.05) %>% # Retain significant cors only
dplyr::filter(estimate.rho >= 0.75) %>% # Retain strong correlations
arrange(desc(estimate.rho))
nrow(top_posCor)## [1] 98head(top_posCor, 20L)## # A tibble: 20 × 3
## analytes estimate.rho p.value
## <chr> <dbl> <dbl>
## 1 seq.7655.11 0.95 0.0000876
## 2 seq.9942.2 0.95 0.0000876
## 3 seq.15326.64 0.933 0.000236
## 4 seq.6444.15 0.933 0.000236
## 5 seq.3362.61 0.917 0.000507
## 6 seq.12988.49 0.9 0.000943
## 7 seq.15640.54 0.9 0.000943
## 8 seq.16890.37 0.9 0.000943
## 9 seq.9266.1 0.9 0.000943
## 10 seq.16785.45 0.883 0.00159
## 11 seq.3336.50 0.883 0.00159
## 12 seq.4666.199 0.883 0.00159
## 13 seq.8304.50 0.883 0.00159
## 14 seq.2944.66 0.867 0.00250
## 15 seq.3434.34 0.867 0.00250
## 16 seq.3435.53 0.867 0.00250
## 17 seq.3440.7 0.867 0.00250
## 18 seq.4131.72 0.867 0.00250
## 19 seq.4907.56 0.867 0.00250
## 20 seq.4914.10 0.867 0.00250The table above contains analytes that have a strong positive
correlation with Age, but this information alone may not enough to derive
meaningful biological insights. Which proteins and genes do these identifiers
correspond to? Are the most correlated proteins functionally related in some
way, perhaps as part of the same biological pathway? These are the types of
questions that SomaScan.db is designed to address.
In the next section, we will annotate these data by querying the above
SeqIds in SomaScan.db to retrieve additional information about their
corresponding genes and gene types.
4 Annotating Results
To obtain a list of available annotations, use the columns method:
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 that the “PROBEID” column corresponds to the SomaScan SeqId, the
central probe for the platform. This identifier ties the other available
annotations (listed in the columns output above) to the data found in an
ADAT file. For more information on the ADAT file format and an explanation of
its contents, please reference the
SomaLogic-Data GitHub repository.
For this example, we will retrieve Gene Ontology (GO) identifiers associated
with the SeqIds that are positively correlated with Age. This will be the
first step in identifying biological processes associated with the protein
targets of these SeqIds.
grep("GO|ONTOLOGY", columns(SomaScan.db), value = TRUE)## [1] "GO" "GOALL" "ONTOLOGY" "ONTOLOGYALL"The GO and GOALL columns both contain GO pathway identifiers, while
ONTOLOGY and ONTOLOGYALL contain additional metadata about the
identifiers. For additional information about the values returned by columns,
run help("{COLUMN NAME}") to get a more detailed description of the listed
options.
4.1 Retrieving Annotations
The select method can be used to query the annotations to retrieve
information corresponding to SomaScan analytes of interest. But, before we
proceed, a note about the select method.
4.1.1 select Namespace Collisions
The select method is unique to Bioconductor’s annotation packages (of which
SomaScan.db is one), and should not be confused with the similarly-named
dplyr::select(). These two functions perform very different actions on
different classes of objects. If you have the dplyr package
(dplyr) loaded at the same time as
SomaScan.db (as we do in this vignette), you may encounter an error like the
one below when using select:
Error in UseMethod("select") :
no applicable method for 'select' applied to an object of class "c('SomaDb',
'ChipDb', 'AnnotationDb', 'envRefClass', '.environment', 'refClass',
'environment', 'refObject', 'AssayData')"This error indicates that it is unclear which select you are attempting to
use (the dplyr version or the SomaScan.db version). To remedy this, it
can be helpful to use the :: operator to specify the package namespace
when calling select, as seen in the code chunk below.
4.1.2 Gene Name Annotation
Continuing on with the example, we will retrieve gene names (as symbols) that
correspond to the top 10 analytes associated with Age.
top10_analytes <- head(top_posCor$analytes, 10L)
anno <- SomaScan.db::select(SomaScan.db,
keys = top10_analytes,
columns = c("SYMBOL", "GENENAME", "GENETYPE"))## 'select()' returned 1:1 mapping between keys and columnsanno## PROBEID SYMBOL GENENAME
## 1 7655-11 NPPB natriuretic peptide B
## 2 9942-2 NXT1 nuclear transport factor 2 like export factor 1
## 3 15326-64 GBP1 guanylate binding protein 1
## 4 6444-15 PSG3 pregnancy specific beta-1-glycoprotein 3
## 5 3362-61 CHRDL1 chordin like 1
## 6 12988-49 EWSR1 EWS RNA binding protein 1
## 7 15640-54 TAGLN transgelin
## 8 16890-37 ADAMTSL1 ADAMTS like 1
## 9 9266-1 TREM1 triggering receptor expressed on myeloid cells 1
## 10 16785-45 DEFA5 defensin alpha 5
## GENETYPE
## 1 protein-coding
## 2 protein-coding
## 3 protein-coding
## 4 protein-coding
## 5 protein-coding
## 6 protein-coding
## 7 protein-coding
## 8 protein-coding
## 9 protein-coding
## 10 protein-codingRemember that select retains the order of the original query, so these genes
are still listed in order of most highly associated with Age.
4.1.3 GO Annotation
Which pathways or biological processes are these genes associated with? Could
that information help explain why they positively correlated with age?
SomaScan.db enables mapping between SeqIds and GO identifiers, so let’s
take the top 3 genes and add GO annotations to the data frame. But why only 3
genes? We are likely to receive a lot of additional
information if we query the database for GO identifiers, so it can be easier
and cleaner to begin with a small example data set when retrieving additional
annotations.
go_anno <- SomaScan.db::select(SomaScan.db,
keys = anno$PROBEID[1:3L],
columns = c("SYMBOL", "GENENAME", "GENETYPE",
"GO", "ONTOLOGY")) %>%
dplyr::filter(ONTOLOGY == "BP")## 'select()' returned 1:many mapping between keys and columnsgo_anno## PROBEID SYMBOL GENENAME
## 1 7655-11 NPPB natriuretic peptide B
## 2 7655-11 NPPB natriuretic peptide B
## 3 7655-11 NPPB natriuretic peptide B
## 4 7655-11 NPPB natriuretic peptide B
## 5 7655-11 NPPB natriuretic peptide B
## 6 7655-11 NPPB natriuretic peptide B
## 7 7655-11 NPPB natriuretic peptide B
## 8 7655-11 NPPB natriuretic peptide B
## 9 7655-11 NPPB natriuretic peptide B
## 10 7655-11 NPPB natriuretic peptide B
## 11 7655-11 NPPB natriuretic peptide B
## 12 7655-11 NPPB natriuretic peptide B
## 13 7655-11 NPPB natriuretic peptide B
## 14 7655-11 NPPB natriuretic peptide B
## 15 7655-11 NPPB natriuretic peptide B
## 16 7655-11 NPPB natriuretic peptide B
## 17 7655-11 NPPB natriuretic peptide B
## 18 7655-11 NPPB natriuretic peptide B
## 19 7655-11 NPPB natriuretic peptide B
## 20 9942-2 NXT1 nuclear transport factor 2 like export factor 1
## 21 9942-2 NXT1 nuclear transport factor 2 like export factor 1
## 22 9942-2 NXT1 nuclear transport factor 2 like export factor 1
## 23 9942-2 NXT1 nuclear transport factor 2 like export factor 1
## 24 15326-64 GBP1 guanylate binding protein 1
## 25 15326-64 GBP1 guanylate binding protein 1
## 26 15326-64 GBP1 guanylate binding protein 1
## 27 15326-64 GBP1 guanylate binding protein 1
## 28 15326-64 GBP1 guanylate binding protein 1
## 29 15326-64 GBP1 guanylate binding protein 1
## 30 15326-64 GBP1 guanylate binding protein 1
## 31 15326-64 GBP1 guanylate binding protein 1
## 32 15326-64 GBP1 guanylate binding protein 1
## 33 15326-64 GBP1 guanylate binding protein 1
## 34 15326-64 GBP1 guanylate binding protein 1
## 35 15326-64 GBP1 guanylate binding protein 1
## 36 15326-64 GBP1 guanylate binding protein 1
## 37 15326-64 GBP1 guanylate binding protein 1
## 38 15326-64 GBP1 guanylate binding protein 1
## 39 15326-64 GBP1 guanylate binding protein 1
## 40 15326-64 GBP1 guanylate binding protein 1
## 41 15326-64 GBP1 guanylate binding protein 1
## 42 15326-64 GBP1 guanylate binding protein 1
## GENETYPE GO EVIDENCE ONTOLOGY
## 1 protein-coding GO:0003085 IBA BP
## 2 protein-coding GO:0003161 NAS BP
## 3 protein-coding GO:0006182 IBA BP
## 4 protein-coding GO:0006182 IDA BP
## 5 protein-coding GO:0006457 IDA BP
## 6 protein-coding GO:0007166 NAS BP
## 7 protein-coding GO:0007168 IBA BP
## 8 protein-coding GO:0007168 IDA BP
## 9 protein-coding GO:0007218 IBA BP
## 10 protein-coding GO:0007589 TAS BP
## 11 protein-coding GO:0008217 NAS BP
## 12 protein-coding GO:0016525 TAS BP
## 13 protein-coding GO:0019934 IBA BP
## 14 protein-coding GO:0030308 NAS BP
## 15 protein-coding GO:0035810 TAS BP
## 16 protein-coding GO:0035815 TAS BP
## 17 protein-coding GO:0042311 IEA BP
## 18 protein-coding GO:0043114 TAS BP
## 19 protein-coding GO:0097746 NAS BP
## 20 protein-coding GO:0006406 IDA BP
## 21 protein-coding GO:0006406 ISS BP
## 22 protein-coding GO:0006611 IEA BP
## 23 protein-coding GO:0016973 IBA BP
## 24 protein-coding GO:0032703 IMP BP
## 25 protein-coding GO:0042742 ISS BP
## 26 protein-coding GO:0042832 IDA BP
## 27 protein-coding GO:0045087 IDA BP
## 28 protein-coding GO:0050848 IMP BP
## 29 protein-coding GO:0050860 IMP BP
## 30 protein-coding GO:0051607 IEA BP
## 31 protein-coding GO:0051715 IDA BP
## 32 protein-coding GO:0070373 IMP BP
## 33 protein-coding GO:0071346 IBA BP
## 34 protein-coding GO:0071346 IDA BP
## 35 protein-coding GO:0071347 IEP BP
## 36 protein-coding GO:0071356 IEP BP
## 37 protein-coding GO:0072665 IDA BP
## 38 protein-coding GO:0140639 IDA BP
## 39 protein-coding GO:0160075 IDA BP
## 40 protein-coding GO:1900025 IMP BP
## 41 protein-coding GO:1903076 IGI BP
## 42 protein-coding GO:1903077 IMP BPAs expected when working with GO terms, there were quite a few rows retrieved
by that select query (42 rows of information for only 3
genes). Filtering the results to the biological process (“BP”) ontology only
can help reduce the number of GO identifiers that are returned in the query.
This leaves us with a list of GO identifiers, but those identifiers do not adequately explain much about the term itself. How can we get more “human-readable” information out of GO?
Luckily, this is easy to do with other annotation tools from Bioconductor. The
GO.db annotation package contains annotations describing the
entire Gene Ontology knowledgebase, assembled using data directly from the
GO website. We can use the GO IDs identified by
SomaScan.db to connect with GO.db and retrieve more
information about each GO ID.
go_terms <- AnnotationDbi::select(GO.db,
keys = go_anno$GO,
columns = c("GOID", "TERM", "DEFINITION"))## 'select()' returned many:1 mapping between keys and columnsgo_terms## GOID
## 1 GO:0003085
## 2 GO:0003161
## 3 GO:0006182
## 4 GO:0006182
## 5 GO:0006457
## 6 GO:0007166
## 7 GO:0007168
## 8 GO:0007168
## 9 GO:0007218
## 10 GO:0007589
## 11 GO:0008217
## 12 GO:0016525
## 13 GO:0019934
## 14 GO:0030308
## 15 GO:0035810
## 16 GO:0035815
## 17 GO:0042311
## 18 GO:0043114
## 19 GO:0097746
## 20 GO:0006406
## 21 GO:0006406
## 22 GO:0006611
## 23 GO:0016973
## 24 GO:0032703
## 25 GO:0042742
## 26 GO:0042832
## 27 GO:0045087
## 28 GO:0050848
## 29 GO:0050860
## 30 GO:0051607
## 31 GO:0051715
## 32 GO:0070373
## 33 GO:0071346
## 34 GO:0071346
## 35 GO:0071347
## 36 GO:0071356
## 37 GO:0072665
## 38 GO:0140639
## 39 GO:0160075
## 40 GO:1900025
## 41 GO:1903076
## 42 GO:1903077
## TERM
## 1 negative regulation of systemic arterial blood pressure
## 2 cardiac conduction system development
## 3 cGMP biosynthetic process
## 4 cGMP biosynthetic process
## 5 protein folding
## 6 cell surface receptor signaling pathway
## 7 receptor guanylyl cyclase signaling pathway
## 8 receptor guanylyl cyclase signaling pathway
## 9 neuropeptide signaling pathway
## 10 body fluid secretion
## 11 regulation of blood pressure
## 12 negative regulation of angiogenesis
## 13 cGMP-mediated signaling
## 14 negative regulation of cell growth
## 15 positive regulation of urine volume
## 16 positive regulation of renal sodium excretion
## 17 vasodilation
## 18 regulation of vascular permeability
## 19 blood vessel diameter maintenance
## 20 mRNA export from nucleus
## 21 mRNA export from nucleus
## 22 protein export from nucleus
## 23 poly(A)+ mRNA export from nucleus
## 24 negative regulation of interleukin-2 production
## 25 defense response to bacterium
## 26 defense response to protozoan
## 27 innate immune response
## 28 regulation of calcium-mediated signaling
## 29 negative regulation of T cell receptor signaling pathway
## 30 defense response to virus
## 31 cytolysis in another organism
## 32 negative regulation of ERK1 and ERK2 cascade
## 33 cellular response to type II interferon
## 34 cellular response to type II interferon
## 35 cellular response to interleukin-1
## 36 cellular response to tumor necrosis factor
## 37 protein localization to vacuole
## 38 positive regulation of pyroptosis
## 39 non-canonical inflammasome complex assembly
## 40 negative regulation of substrate adhesion-dependent cell spreading
## 41 regulation of protein localization to plasma membrane
## 42 negative regulation of protein localization to plasma membrane
## DEFINITION
## 1 The process that reduces the force with which blood travels through the systemic arterial circulatory system.
## 2 The process whose specific outcome is the progression of the cardiac conduction system over time, from its formation to the mature structure. The cardiac conduction system consists of specialized cardiomyocytes that regulate the frequency of heart beat.
## 3 The chemical reactions and pathways resulting in the formation of cyclic GMP, guanosine 3',5'-phosphate.
## 4 The chemical reactions and pathways resulting in the formation of cyclic GMP, guanosine 3',5'-phosphate.
## 5 The process of assisting in the covalent and noncovalent assembly of single chain polypeptides or multisubunit complexes into the correct tertiary structure.
## 6 The series of molecular signals initiated by activation of a receptor on the surface of a cell. The pathway begins with binding of an extracellular ligand to a cell surface receptor, or for receptors that signal in the absence of a ligand, by ligand-withdrawal or the activity of a constitutively active receptor. The pathway ends with regulation of a downstream cellular process, e.g. transcription.
## 7 The series of molecular signals initiated by an extracellular ligand binding to a receptor on the surface of the target cell where the receptor possesses guanylyl cyclase activity, and ending with the regulation of a downstream cellular process, e.g. transcription.
## 8 The series of molecular signals initiated by an extracellular ligand binding to a receptor on the surface of the target cell where the receptor possesses guanylyl cyclase activity, and ending with the regulation of a downstream cellular process, e.g. transcription.
## 9 A G protein-coupled receptor signaling pathway initiated by a neuropeptide binding to its receptor on the surface of a target cell, and ending with the regulation of a downstream cellular process.
## 10 The controlled release of a fluid by a cell or tissue in an animal.
## 11 Any process that modulates the force with which blood travels through the circulatory system. The process is controlled by a balance of processes that increase pressure and decrease pressure.
## 12 Any process that stops, prevents, or reduces the frequency, rate or extent of angiogenesis.
## 13 An intracellular signaling cassette that starts with production of cyclic GMP (cGMP), and ends with activation of downstream effectors that further transmit the signal within the cell.
## 14 Any process that stops, prevents, or reduces the frequency, rate, extent or direction of cell growth.
## 15 Any process that increases the amount of urine excreted from the body over a unit of time.
## 16 Any process that increases the amount of sodium excreted in urine over a unit of time.
## 17 An increase in the internal diameter of blood vessels, especially arterioles or capillaries, due to relaxation of smooth muscle cells that line the vessels, and usually resulting in a decrease in blood pressure.
## 18 Any process that modulates the extent to which blood vessels can be pervaded by fluid.
## 19 Any process that modulates the diameter of blood vessels.
## 20 The directed movement of mRNA from the nucleus to the cytoplasm.
## 21 The directed movement of mRNA from the nucleus to the cytoplasm.
## 22 The directed movement of a protein from the nucleus into the cytoplasm.
## 23 The directed movement of poly(A)+ mRNA out of the nucleus into the cytoplasm.
## 24 Any process that stops, prevents, or reduces the frequency, rate, or extent of interleukin-2 production.
## 25 Reactions triggered in response to the presence of a bacterium that act to protect the cell or organism.
## 26 Reactions triggered in response to the presence of a protozoan that act to protect the cell or organism.
## 27 Innate immune responses are defense responses mediated by germline encoded components that directly recognize components of potential pathogens.
## 28 Any process that modulates the frequency, rate or extent of calcium-mediated signaling, the process in which a cell uses calcium ions to convert an extracellular signal into a response.
## 29 Any process that stops, prevents, or reduces the frequency, rate or extent of signaling pathways initiated by the cross-linking of an antigen receptor on a T cell.
## 30 Reactions triggered in response to the presence of a virus that act to protect the cell or organism.
## 31 The killing by an organism of a cell in another organism by means of the rupture of cell membranes and the loss of cytoplasm.
## 32 Any process that stops, prevents, or reduces the frequency, rate or extent of signal transduction mediated by the ERK1 and ERK2 cascade.
## 33 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 interferon-gamma stimulus. Interferon gamma is the only member of the type II interferon found so far.
## 34 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 interferon-gamma stimulus. Interferon gamma is the only member of the type II interferon found so far.
## 35 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-1 stimulus.
## 36 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 a tumor necrosis factor stimulus.
## 37 A process in which a protein is transported to, or maintained at, a location in a vacuole.
## 38 <NA>
## 39 <NA>
## 40 <NA>
## 41 <NA>
## 42 <NA>Now we have much more information about each GO ID! Using these IDs, we can
merge this information back into our select results from SomaScan.db:
final_df <- left_join(go_anno, go_terms, by = c("GO" = "GOID"))## Warning in left_join(go_anno, go_terms, by = c(GO = "GOID")): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 3 of `x` matches multiple rows in `y`.
## ℹ Row 3 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.final_df## PROBEID SYMBOL GENENAME
## 1 7655-11 NPPB natriuretic peptide B
## 2 7655-11 NPPB natriuretic peptide B
## 3 7655-11 NPPB natriuretic peptide B
## 4 7655-11 NPPB natriuretic peptide B
## 5 7655-11 NPPB natriuretic peptide B
## 6 7655-11 NPPB natriuretic peptide B
## 7 7655-11 NPPB natriuretic peptide B
## 8 7655-11 NPPB natriuretic peptide B
## 9 7655-11 NPPB natriuretic peptide B
## 10 7655-11 NPPB natriuretic peptide B
## 11 7655-11 NPPB natriuretic peptide B
## 12 7655-11 NPPB natriuretic peptide B
## 13 7655-11 NPPB natriuretic peptide B
## 14 7655-11 NPPB natriuretic peptide B
## 15 7655-11 NPPB natriuretic peptide B
## 16 7655-11 NPPB natriuretic peptide B
## 17 7655-11 NPPB natriuretic peptide B
## 18 7655-11 NPPB natriuretic peptide B
## 19 7655-11 NPPB natriuretic peptide B
## 20 7655-11 NPPB natriuretic peptide B
## 21 7655-11 NPPB natriuretic peptide B
## 22 7655-11 NPPB natriuretic peptide B
## 23 7655-11 NPPB natriuretic peptide B
## 24 9942-2 NXT1 nuclear transport factor 2 like export factor 1
## 25 9942-2 NXT1 nuclear transport factor 2 like export factor 1
## 26 9942-2 NXT1 nuclear transport factor 2 like export factor 1
## 27 9942-2 NXT1 nuclear transport factor 2 like export factor 1
## 28 9942-2 NXT1 nuclear transport factor 2 like export factor 1
## 29 9942-2 NXT1 nuclear transport factor 2 like export factor 1
## 30 15326-64 GBP1 guanylate binding protein 1
## 31 15326-64 GBP1 guanylate binding protein 1
## 32 15326-64 GBP1 guanylate binding protein 1
## 33 15326-64 GBP1 guanylate binding protein 1
## 34 15326-64 GBP1 guanylate binding protein 1
## 35 15326-64 GBP1 guanylate binding protein 1
## 36 15326-64 GBP1 guanylate binding protein 1
## 37 15326-64 GBP1 guanylate binding protein 1
## 38 15326-64 GBP1 guanylate binding protein 1
## 39 15326-64 GBP1 guanylate binding protein 1
## 40 15326-64 GBP1 guanylate binding protein 1
## 41 15326-64 GBP1 guanylate binding protein 1
## 42 15326-64 GBP1 guanylate binding protein 1
## 43 15326-64 GBP1 guanylate binding protein 1
## 44 15326-64 GBP1 guanylate binding protein 1
## 45 15326-64 GBP1 guanylate binding protein 1
## 46 15326-64 GBP1 guanylate binding protein 1
## 47 15326-64 GBP1 guanylate binding protein 1
## 48 15326-64 GBP1 guanylate binding protein 1
## 49 15326-64 GBP1 guanylate binding protein 1
## 50 15326-64 GBP1 guanylate binding protein 1
## GENETYPE GO EVIDENCE ONTOLOGY
## 1 protein-coding GO:0003085 IBA BP
## 2 protein-coding GO:0003161 NAS BP
## 3 protein-coding GO:0006182 IBA BP
## 4 protein-coding GO:0006182 IBA BP
## 5 protein-coding GO:0006182 IDA BP
## 6 protein-coding GO:0006182 IDA BP
## 7 protein-coding GO:0006457 IDA BP
## 8 protein-coding GO:0007166 NAS BP
## 9 protein-coding GO:0007168 IBA BP
## 10 protein-coding GO:0007168 IBA BP
## 11 protein-coding GO:0007168 IDA BP
## 12 protein-coding GO:0007168 IDA BP
## 13 protein-coding GO:0007218 IBA BP
## 14 protein-coding GO:0007589 TAS BP
## 15 protein-coding GO:0008217 NAS BP
## 16 protein-coding GO:0016525 TAS BP
## 17 protein-coding GO:0019934 IBA BP
## 18 protein-coding GO:0030308 NAS BP
## 19 protein-coding GO:0035810 TAS BP
## 20 protein-coding GO:0035815 TAS BP
## 21 protein-coding GO:0042311 IEA BP
## 22 protein-coding GO:0043114 TAS BP
## 23 protein-coding GO:0097746 NAS BP
## 24 protein-coding GO:0006406 IDA BP
## 25 protein-coding GO:0006406 IDA BP
## 26 protein-coding GO:0006406 ISS BP
## 27 protein-coding GO:0006406 ISS BP
## 28 protein-coding GO:0006611 IEA BP
## 29 protein-coding GO:0016973 IBA BP
## 30 protein-coding GO:0032703 IMP BP
## 31 protein-coding GO:0042742 ISS BP
## 32 protein-coding GO:0042832 IDA BP
## 33 protein-coding GO:0045087 IDA BP
## 34 protein-coding GO:0050848 IMP BP
## 35 protein-coding GO:0050860 IMP BP
## 36 protein-coding GO:0051607 IEA BP
## 37 protein-coding GO:0051715 IDA BP
## 38 protein-coding GO:0070373 IMP BP
## 39 protein-coding GO:0071346 IBA BP
## 40 protein-coding GO:0071346 IBA BP
## 41 protein-coding GO:0071346 IDA BP
## 42 protein-coding GO:0071346 IDA BP
## 43 protein-coding GO:0071347 IEP BP
## 44 protein-coding GO:0071356 IEP BP
## 45 protein-coding GO:0072665 IDA BP
## 46 protein-coding GO:0140639 IDA BP
## 47 protein-coding GO:0160075 IDA BP
## 48 protein-coding GO:1900025 IMP BP
## 49 protein-coding GO:1903076 IGI BP
## 50 protein-coding GO:1903077 IMP BP
## TERM
## 1 negative regulation of systemic arterial blood pressure
## 2 cardiac conduction system development
## 3 cGMP biosynthetic process
## 4 cGMP biosynthetic process
## 5 cGMP biosynthetic process
## 6 cGMP biosynthetic process
## 7 protein folding
## 8 cell surface receptor signaling pathway
## 9 receptor guanylyl cyclase signaling pathway
## 10 receptor guanylyl cyclase signaling pathway
## 11 receptor guanylyl cyclase signaling pathway
## 12 receptor guanylyl cyclase signaling pathway
## 13 neuropeptide signaling pathway
## 14 body fluid secretion
## 15 regulation of blood pressure
## 16 negative regulation of angiogenesis
## 17 cGMP-mediated signaling
## 18 negative regulation of cell growth
## 19 positive regulation of urine volume
## 20 positive regulation of renal sodium excretion
## 21 vasodilation
## 22 regulation of vascular permeability
## 23 blood vessel diameter maintenance
## 24 mRNA export from nucleus
## 25 mRNA export from nucleus
## 26 mRNA export from nucleus
## 27 mRNA export from nucleus
## 28 protein export from nucleus
## 29 poly(A)+ mRNA export from nucleus
## 30 negative regulation of interleukin-2 production
## 31 defense response to bacterium
## 32 defense response to protozoan
## 33 innate immune response
## 34 regulation of calcium-mediated signaling
## 35 negative regulation of T cell receptor signaling pathway
## 36 defense response to virus
## 37 cytolysis in another organism
## 38 negative regulation of ERK1 and ERK2 cascade
## 39 cellular response to type II interferon
## 40 cellular response to type II interferon
## 41 cellular response to type II interferon
## 42 cellular response to type II interferon
## 43 cellular response to interleukin-1
## 44 cellular response to tumor necrosis factor
## 45 protein localization to vacuole
## 46 positive regulation of pyroptosis
## 47 non-canonical inflammasome complex assembly
## 48 negative regulation of substrate adhesion-dependent cell spreading
## 49 regulation of protein localization to plasma membrane
## 50 negative regulation of protein localization to plasma membrane
## DEFINITION
## 1 The process that reduces the force with which blood travels through the systemic arterial circulatory system.
## 2 The process whose specific outcome is the progression of the cardiac conduction system over time, from its formation to the mature structure. The cardiac conduction system consists of specialized cardiomyocytes that regulate the frequency of heart beat.
## 3 The chemical reactions and pathways resulting in the formation of cyclic GMP, guanosine 3',5'-phosphate.
## 4 The chemical reactions and pathways resulting in the formation of cyclic GMP, guanosine 3',5'-phosphate.
## 5 The chemical reactions and pathways resulting in the formation of cyclic GMP, guanosine 3',5'-phosphate.
## 6 The chemical reactions and pathways resulting in the formation of cyclic GMP, guanosine 3',5'-phosphate.
## 7 The process of assisting in the covalent and noncovalent assembly of single chain polypeptides or multisubunit complexes into the correct tertiary structure.
## 8 The series of molecular signals initiated by activation of a receptor on the surface of a cell. The pathway begins with binding of an extracellular ligand to a cell surface receptor, or for receptors that signal in the absence of a ligand, by ligand-withdrawal or the activity of a constitutively active receptor. The pathway ends with regulation of a downstream cellular process, e.g. transcription.
## 9 The series of molecular signals initiated by an extracellular ligand binding to a receptor on the surface of the target cell where the receptor possesses guanylyl cyclase activity, and ending with the regulation of a downstream cellular process, e.g. transcription.
## 10 The series of molecular signals initiated by an extracellular ligand binding to a receptor on the surface of the target cell where the receptor possesses guanylyl cyclase activity, and ending with the regulation of a downstream cellular process, e.g. transcription.
## 11 The series of molecular signals initiated by an extracellular ligand binding to a receptor on the surface of the target cell where the receptor possesses guanylyl cyclase activity, and ending with the regulation of a downstream cellular process, e.g. transcription.
## 12 The series of molecular signals initiated by an extracellular ligand binding to a receptor on the surface of the target cell where the receptor possesses guanylyl cyclase activity, and ending with the regulation of a downstream cellular process, e.g. transcription.
## 13 A G protein-coupled receptor signaling pathway initiated by a neuropeptide binding to its receptor on the surface of a target cell, and ending with the regulation of a downstream cellular process.
## 14 The controlled release of a fluid by a cell or tissue in an animal.
## 15 Any process that modulates the force with which blood travels through the circulatory system. The process is controlled by a balance of processes that increase pressure and decrease pressure.
## 16 Any process that stops, prevents, or reduces the frequency, rate or extent of angiogenesis.
## 17 An intracellular signaling cassette that starts with production of cyclic GMP (cGMP), and ends with activation of downstream effectors that further transmit the signal within the cell.
## 18 Any process that stops, prevents, or reduces the frequency, rate, extent or direction of cell growth.
## 19 Any process that increases the amount of urine excreted from the body over a unit of time.
## 20 Any process that increases the amount of sodium excreted in urine over a unit of time.
## 21 An increase in the internal diameter of blood vessels, especially arterioles or capillaries, due to relaxation of smooth muscle cells that line the vessels, and usually resulting in a decrease in blood pressure.
## 22 Any process that modulates the extent to which blood vessels can be pervaded by fluid.
## 23 Any process that modulates the diameter of blood vessels.
## 24 The directed movement of mRNA from the nucleus to the cytoplasm.
## 25 The directed movement of mRNA from the nucleus to the cytoplasm.
## 26 The directed movement of mRNA from the nucleus to the cytoplasm.
## 27 The directed movement of mRNA from the nucleus to the cytoplasm.
## 28 The directed movement of a protein from the nucleus into the cytoplasm.
## 29 The directed movement of poly(A)+ mRNA out of the nucleus into the cytoplasm.
## 30 Any process that stops, prevents, or reduces the frequency, rate, or extent of interleukin-2 production.
## 31 Reactions triggered in response to the presence of a bacterium that act to protect the cell or organism.
## 32 Reactions triggered in response to the presence of a protozoan that act to protect the cell or organism.
## 33 Innate immune responses are defense responses mediated by germline encoded components that directly recognize components of potential pathogens.
## 34 Any process that modulates the frequency, rate or extent of calcium-mediated signaling, the process in which a cell uses calcium ions to convert an extracellular signal into a response.
## 35 Any process that stops, prevents, or reduces the frequency, rate or extent of signaling pathways initiated by the cross-linking of an antigen receptor on a T cell.
## 36 Reactions triggered in response to the presence of a virus that act to protect the cell or organism.
## 37 The killing by an organism of a cell in another organism by means of the rupture of cell membranes and the loss of cytoplasm.
## 38 Any process that stops, prevents, or reduces the frequency, rate or extent of signal transduction mediated by the ERK1 and ERK2 cascade.
## 39 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 interferon-gamma stimulus. Interferon gamma is the only member of the type II interferon found so far.
## 40 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 interferon-gamma stimulus. Interferon gamma is the only member of the type II interferon found so far.
## 41 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 interferon-gamma stimulus. Interferon gamma is the only member of the type II interferon found so far.
## 42 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 interferon-gamma stimulus. Interferon gamma is the only member of the type II interferon found so far.
## 43 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-1 stimulus.
## 44 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 a tumor necrosis factor stimulus.
## 45 A process in which a protein is transported to, or maintained at, a location in a vacuole.
## 46 <NA>
## 47 <NA>
## 48 <NA>
## 49 <NA>
## 50 <NA>The SomaScan.db package can be used to link to numerous other
annotation resources. For a more detailed description of how this can be done
with examples of what resources are available, see the Advanced Usage Examples
vignette.
There is still room for further exploration and extension of these results;
this workflow is meant to be an introduction to how SomaScan.db can be
used to build upon and interpret information obtained from a SomaLogic ADAT.
5 Session Info
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
##
## 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] SomaDataIO_6.1.0 dplyr_1.1.4 org.Hs.eg.db_3.19.1
## [4] KEGGREST_1.43.0 GO.db_3.19.1 tibble_3.2.1
## [7] SomaScan.db_0.99.10 AnnotationDbi_1.65.2 IRanges_2.37.1
## [10] S4Vectors_0.41.7 Biobase_2.63.1 BiocGenerics_0.49.1
## [13] withr_3.0.0 BiocStyle_2.31.0
##
## loaded via a namespace (and not attached):
## [1] tidyr_1.3.1 generics_0.1.3 sass_0.4.9
## [4] utf8_1.2.4 RSQLite_2.3.6 digest_0.6.35
## [7] magrittr_2.0.3 evaluate_0.23 bookdown_0.39
## [10] fastmap_1.1.1 blob_1.2.4 cellranger_1.1.0
## [13] jsonlite_1.8.8 GenomeInfoDb_1.39.14 DBI_1.2.2
## [16] BiocManager_1.30.22 httr_1.4.7 purrr_1.0.2
## [19] fansi_1.0.6 UCSC.utils_0.99.7 Biostrings_2.71.6
## [22] jquerylib_0.1.4 cli_3.6.2 rlang_1.1.3
## [25] crayon_1.5.2 XVector_0.43.1 bit64_4.0.5
## [28] cachem_1.0.8 yaml_2.3.8 tools_4.4.0
## [31] memoise_2.0.1 GenomeInfoDbData_1.2.12 curl_5.2.1
## [34] vctrs_0.6.5 R6_2.5.1 png_0.1-8
## [37] lifecycle_1.0.4 zlibbioc_1.49.3 bit_4.0.5
## [40] pkgconfig_2.0.3 bslib_0.7.0 pillar_1.9.0
## [43] glue_1.7.0 highr_0.10 tidyselect_1.2.1
## [46] xfun_0.43 knitr_1.46 htmltools_0.5.8.1
## [49] rmarkdown_2.26 compiler_4.4.0 readxl_1.4.3