SomaDataIO (version 6.2.0)

getAnalyteInfo: Get Analyte Annotation Information

Description

Uses the Col.Meta attribute (analyte annotation data that appears above the protein measurements in the *.adat text file) of a soma_adat object, adds the AptName column key, conducts a few sanity checks, and generates a "lookup table" of analyte data that can be used for simple manipulation and indexing of analyte annotation information. Most importantly, the analyte column names of the soma_adat (e.g. seq.XXXX.XX) become the AptName column of the lookup table and represents the key index between the table and soma_adat from which it comes.

Usage

getAnalyteInfo(adat)
getTargetNames(tbl)
getFeatureData(adat)

Value

A tibble object with columns corresponding to the column meta data entries in the soma_adat. One row per analyte.

Arguments

adat

A soma_adat object (with intact attributes), typically created using read_adat().

tbl

A tibble object containing analyte target annotation information. This is usually the result of a call to getAnalyteInfo().

Functions

• getTargetNames(): creates a lookup table (or dictionary) as a named list object of AptNames and Target names in key-value pairs. This is a convenient tool to quickly access a TargetName given the AptName in which the key-value pairs map the seq.XXXX.XX to its corresponding TargetName in tbl. This structure which provides a convenient auto-completion mechanism at the command line or for generating plot titles.

• getFeatureData(): . Please now use getAnalyteInfo().

Author

Stu Field

See Also

getAnalytes(), is_intact_attr(), read_adat()

Examples

# Get Aptamer table
anno_tbl <- getAnalyteInfo(example_data)
anno_tbl

# Use `dplyr::group_by()`
dplyr::tally(dplyr::group_by(anno_tbl, Dilution))  # print summary by dilution

# Columns containing "Target"
anno_tbl |>
  dplyr::select(dplyr::contains("Target"))

# Rows of "Target" starting with MMP
anno_tbl |>
  dplyr::filter(grepl("^MMP", Target))

# Target names
tg <- getTargetNames(anno_tbl)

# how to use for plotting
feats <- sample(anno_tbl$AptName, 6)
op <- par(mfrow = c(2, 3))
sapply(feats, function(.x) plot(1:10, main = tg[[.x]]))
par(op)