Get Analytes

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)

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?

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.

Functions

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

See also

is.apt()

Author

Stu Field

Examples

# RFU feature variables
apts <- getAnalytes(example_data)
head(apts)
#> [1] "seq.10000.28" "seq.10001.7"  "seq.10003.15" "seq.10006.25"
#> [5] "seq.10008.43" "seq.10011.65"
getAnalytes(example_data, n = TRUE)
#> [1] 5284

# vector string
bb <- getAnalytes(names(example_data))
all.equal(apts, bb)
#> [1] TRUE

# create some control sequences
# ~~~~~~~~~ Spuriomer ~~~ HybControl ~~~
apts2 <- c("seq.2053.2", "seq.2171.12", head(apts))
apts2
#> [1] "seq.2053.2"   "seq.2171.12"  "seq.10000.28" "seq.10001.7" 
#> [5] "seq.10003.15" "seq.10006.25" "seq.10008.43" "seq.10011.65"
no_crtl <- getAnalytes(apts2, rm.controls = TRUE)
no_crtl
#> [1] "seq.10000.28" "seq.10001.7"  "seq.10003.15" "seq.10006.25"
#> [5] "seq.10008.43" "seq.10011.65"
setdiff(apts2, no_crtl)
#> [1] "seq.2053.2"  "seq.2171.12"

# clinical variables
mvec <- getMeta(example_data)
head(mvec, 10)
#>  [1] "PlateId"         "PlateRunDate"    "ScannerID"      
#>  [4] "PlatePosition"   "SlideId"         "Subarray"       
#>  [7] "SampleId"        "SampleType"      "PercentDilution"
#> [10] "SampleMatrix"   
getMeta(example_data, n = TRUE)
#> [1] 34

# test 'data.frame' and 'character' S3 methods are identical
identical(getMeta(example_data), getMeta(names(example_data))) # TRUE
#> [1] TRUE