SomaDataIO (version 6.2.0)

getAnalytes: Get Analytes

Description

Return the feature names (i.e. the column names for SOMAmer reagent analytes) from a soma_adat. S3 methods also exist for these classes:

#> [1] getAnalytes.character  getAnalytes.data.frame getAnalytes.default   
#> [4] getAnalytes.list       getAnalytes.matrix     getAnalytes.recipe    
#> [7] getAnalytes.soma_adat 
#> see '?methods' for accessing help and source code

getMeta() returns the inverse, a character vector of string names of non-analyte feature columns/variables, which typically correspond to the clinical ("meta") data variables. S3 methods exist for these classes:

#> [1] getMeta.character  getMeta.data.frame getMeta.default    getMeta.list      
#> [5] getMeta.matrix     getMeta.soma_adat 
#> see '?methods' for accessing help and source code

Usage

getAnalytes(x, n = FALSE, rm.controls = FALSE)
getMeta(x, n = FALSE)
getFeatures(x, n = FALSE, rm.controls = FALSE)

Value

getAnalytes(): a character vector of ADAT feature ("analyte") names.

getMeta(): a character vector of ADAT clinical ("meta") data names.

For both, if n = TRUE, an integer corresponding to the length of the character vector.

Arguments

x

Typically a soma_adat class object created using read_adat().

n

Logical. Return an integer corresponding to the length of the features?

rm.controls

Logical. Should all control and non-human analytes (e.g. HybControls, Non-Human, Non-Biotin, Spuriomer) be removed from the returned value?

Functions

• getFeatures(): . Please now use getAnalytes().

Author

Stu Field

See Also

is.apt()

Examples

# RFU feature variables
apts <- getAnalytes(example_data)
head(apts)
getAnalytes(example_data, n = TRUE)

# vector string
bb <- getAnalytes(names(example_data))
all.equal(apts, bb)

# create some control sequences
# ~~~~~~~~~ Spuriomer ~~~ HybControl ~~~
apts2 <- c("seq.2053.2", "seq.2171.12", head(apts))
apts2
no_crtl <- getAnalytes(apts2, rm.controls = TRUE)
no_crtl
setdiff(apts2, no_crtl)

# clinical variables
mvec <- getMeta(example_data)
head(mvec, 10)
getMeta(example_data, n = TRUE)

# test 'data.frame' and 'character' S3 methods are identical
identical(getMeta(example_data), getMeta(names(example_data))) # TRUE