In SomaLogic/SomaDataIO: Input/Output 'SomaScan' Data

options(width = 80)
#Sys.setlocale("LC_COLLATE", "C")
Sys.setlocale("LC_COLLATE", "en_US.UTF-8") # ensure common sorting envir
library(SomaDataIO)
knitr::opts_chunk$set(
  echo = TRUE,
  collapse = TRUE,
  comment = "#>",
  fig.path = "figures/wrangling-"
)

Loading an ADAT

Load an ADAT text file into R memory with:

# path to *.adat file
# replace with your file path
adat_path <- system.file("extdata", "example_data10.adat",
                         package = "SomaDataIO", mustWork = TRUE)
adat_path

my_adat <- read_adat(adat_path)

# class test
is.soma_adat(my_adat)

# S3 print method forwards -> tibble
my_adat

print(my_adat, show_header = TRUE)  # if simply wish to see Header info

# S3 summary method
# View Target and summary statistics
seqs <- tail(names(my_adat), 3L)
summary(my_adat[, seqs])

# Summarize by Sex
my_adat[, seqs] |>
  split(my_adat$Sex) |>
  lapply(summary)

Debugging

Occasionally "problematic" ADATs can be difficult to parse. For this purpose a convenient debug = TRUE argument to read_adat() allows you to inspect the file specifications that R thinks exist in the file. This can be useful in identifying where/why/how a parse failure has occurred. It is recommended to view this output and compare to the physical text file itself to identify any misidentified or mismatched landmarks:

read_adat(adat_path, debug = TRUE)

---

Wrangling

Attributes Contain File and Feature Information

names(attributes(my_adat))

# The `Col.Meta` attribute contains
# target annotation information
attr(my_adat, "Col.Meta")

Analyte Features (seq.xxxx.xx)

getAnalytes(my_adat) |> head(20L)    # first 20 analytes; see AptName above
getAnalytes(my_adat) |> length()     # how many analytes
getAnalytes(my_adat, n = TRUE)       # the `n` argument; no. analytes

Feature Data

The getAnalyteInfo() function creates a lookup table that links analyte feature names in the soma_adat object to the annotation data in ?Col.Meta via the common index-key, AptName, in column 1:

getAnalyteInfo(my_adat)

Clinical Data

getMeta(my_adat)             # clinical meta data for each sample
getMeta(my_adat, n = TRUE)   # also an `n` argument

ADAT structure

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.

---

Group Generics

You may perform basic mathematical transformations on the feature data only with special soma_adat S3 methods (see ?groupGenerics):

head(my_adat$seq.2429.27)

logData <- log10(my_adat)    # a typical log10() transform
head(logData$seq.2429.27)

roundData <- round(my_adat)
head(roundData$seq.2429.27)

sqData <- sqrt(my_adat)
head(sqData$seq.2429.27)

antilog(1:4)

sum(my_adat < 100)  # low signalling values

all.equal(my_adat, sqrt(my_adat^2))

all.equal(my_adat, antilog(log10(my_adat)))

Math Generics

getGroupMembers("Math")

getGroupMembers("Compare")

getGroupMembers("Arith")

getGroupMembers("Summary")

Full Complement of dplyr S3 Methods

The soma_adat also comes with numerous class specific methods to the most popular dplyr generics that make working with soma_adat objects simpler for those familiar with this standard toolkit:

dim(my_adat)
males <- dplyr::filter(my_adat, Sex == "M")
dim(males)

males |>
  dplyr::select(SampleType, SampleMatrix, starts_with("NormScale"))

Available S3 Methods soma_adat

# see full complement of `soma_adat` methods
methods(class = "soma_adat")

---

Writing a soma_adat

is_intact_attr(my_adat)   # MUST have intact attrs

write_adat(my_adat, file = tempfile("my-adat-", fileext = ".adat"))

SomaLogic/SomaDataIO documentation built on Feb. 8, 2025, 12:19 p.m.