The SomaDataIO
R package loads and exports ‘SomaScan’ data via the
SomaLogic Operating Co., Inc. structured text file called an ADAT
(*.adat
). The package also exports auxiliary functions for
manipulating, wrangling, and extracting relevant information from an
ADAT object once in memory. Basic familiarity with the R environment is
assumed, as is the ability to install contributed packages from the
Comprehensive R Archive Network (CRAN).
If you run into any issues/problems with SomaDataIO
full documentation
of the most recent
release can be found
at our website of articles and
workflows. If the issue
persists we encourage you to consult the
issues page and, if
appropriate, submit an issue and/or feature request.
The SomaDataIO
package is licensed under the
MIT
license and is intended solely for research use only (“RUO”) purposes.
The code contained herein may not be used for diagnostic, clinical,
therapeutic, or other commercial purposes.
The easiest way to install SomaDataIO
is to install directly from
CRAN:
install.packages("SomaDataIO")
Alternatively from GitHub:
remotes::install_github("SomaLogic/SomaDataIO")
which installs the most current “development” version from the
repository HEAD
. To install the most recent release, use:
remotes::install_github("SomaLogic/SomaDataIO@*release")
To install a specific tagged release, use:
remotes::install_github("SomaLogic/[email protected]")
The SomaDataIO
package was intentionally developed to contain a
limited number of dependencies from CRAN. This makes the package more
stable to external software design changes but also limits its contained
feature set. With this in mind, SomaDataIO
aims to strike a balance
providing long(er)-term stability and a limited set of features. Below
are the package dependencies (see also the
DESCRIPTION
file):
The Biobase
package is suggested, being required by only two
functions, pivotExpressionSet()
and adat2eSet()
.
Biobase
must be installed separately from
Bioconductor by entering the following
from the R
Console:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("Biobase", version = remotes::bioc_version())
Information about Bioconductor can be found here: https://bioconductor.org/install/
Upon successful installation, load SomaDataIO
as normal:
library(SomaDataIO)
For an index of available commands:
library(help = SomaDataIO)
The SomaDataIO
package comes with four (4) objects available to users
to run canned examples (or analyses). They can be accessed once
SomaDataIO
has been attached via library()
. They are:
-
example_data
: the original ‘SomaScan’ file (example_data.adat
) can be found here or downloaded directly via:wget https://raw.githubusercontent.com/SomaLogic/SomaLogic-Data/main/example_data.adat
-
within
SomaDataIO
it has been replaced by an abbreviated, light-weight version containing only the first 10 samples:dir(system.file("extdata", package = "SomaDataIO"), full.names = TRUE)
-
-
ex_analytes
: the analyte (feature) variables inexample_data
-
ex_anno_tbl
: the annotations table associated withexample_data
-
ex_target_names
: a mapping object for analyte -> target -
See also
?SomaScanObjects
- Loading data (Import)
- parse and import a
*.adat
text file into anR
session as asoma_adat
object.
- parse and import a
- Wrangling data (manipulation)
- subset, reorder, and list various fields of a
soma_adat
object. ?SeqId
analyte (feature) matching.- dplyr and
tidyr verb S3 methods for the
soma_adat
class. ?rownames
helpers that do not breaksoma_adat
attributes.- please see the article Loading and Wrangling ‘SomaScan’
- subset, reorder, and list various fields of a
- Exporting data (Output)
- write out a
soma_adat
object as a*.adat
text file.
- write out a
Loading an ADAT text file is simple using read_adat()
:
# Sample file name
f <- system.file("extdata", "example_data10.adat",
package = "SomaDataIO", mustWork = TRUE)
my_adat <- read_adat(f)
# test object class
is.soma_adat(my_adat)
#> [1] TRUE
# S3 print method (forwards -> tibble)
my_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>, …
#> ════════════════════════════════════════════════════════════════════════════════
Please see the article Loading and Wrangling SomaScan for more details and options.
The soma_adat
class comes with numerous class-specific S3 methods to
the most popular dplyr and
tidyr generics.
# see full complement of `soma_adat` methods
methods(class = "soma_adat")
#> [1] [ [[ [[<- [<- ==
#> [6] $ $<- anti_join arrange count
#> [11] filter full_join getAdatVersion getAnalytes getMeta
#> [16] group_by inner_join is_seqFormat left_join Math
#> [21] median merge mutate Ops print
#> [26] rename right_join row.names<- sample_frac sample_n
#> [31] semi_join separate slice_sample slice summary
#> [36] Summary transform ungroup unite
#> see '?methods' for accessing help and source code
Please see the article Loading and Wrangling
SomaScan
for more details about available soma_adat
methods.
The soma_adat
object also contains specific structure that are useful
to users. Please also see ?colmeta
or ?annotations
for further
details about these fields.
This section now lives in individual package articles. For further detail please see:
- Two-group comparison (e.g. differential expression) via t-test
- see
stats::t.test()
- see workflow: Two-Group Comparison
- see
- Multi-group comparison (e.g. differential expression) via ANOVA
- see
stats::aov()
- see workflow: ANOVA Three-Group Analysis
- see
- Binary classification
- see
stats::glm()
- see workflow: Binary Classification
- see
- Linear regression
- see
stats::lm()
- see workflow: Linear Regression
- see
Note that, in an effort to reduce package size and dependencies, these
articles and workflows are only accessible via the SomaDataIO
pkgdown
website, and are not included with the installed package.
- See:
- The MIT license:
- Further:
- “SomaDataIO” and “SomaLogic” are trademarks owned by SomaLogic Operating Co., Inc. No license is hereby granted to these trademarks other than for purposes of identifying the origin or source of this Software.