Group Generics for soma_adat Class Objects — groupGenerics • SomaDataIO

S3 group generic methods to apply group specific prototype functions to the RFU data only of soma_adat objects. The clinical meta data are not transformed and remain unmodified in the returned object (Math() and Ops()) or are ignored for the Summary() group. See groupGeneric().

Usage

# S3 method for soma_adat
Math(x, ...)

antilog(x, base = 10)

# S3 method for soma_adat
Ops(e1, e2 = NULL)

# S3 method for soma_adat
Summary(..., na.rm = FALSE)

# S3 method for soma_adat
==(e1, e2)

Arguments

x: The soma_adat class object to perform the transformation.
...: Additional arguments passed to the various group generics as appropriate.
base: A positive or complex number: the base with respect to which logarithms are computed.
e1, e2: Objects.
na.rm: Logical. Should missing values be removed?

Value

A soma_adat object with the same dimensions of the input object with the feature columns transformed by the specified generic.

Functions

antilog(): performs the inverse or anti-log transform for a numeric vector of soma_adat object. note: default is base = 10, which differs from the log() default base e.
Ops(soma_adat): performs binary mathematical operations on class soma_adat. See Ops().
Summary(soma_adat): performs summary calculations on class soma_adat. See Summary().
== : compares left- and right-hand sides of the operator unless the RHS is also a soma_adat, in which case diffAdats() is invoked.

Math

Group members:

#>  [1] "abs"      "acos"     "acosh"    "asin"     "asinh"    "atan"    
#>  [7] "atanh"    "ceiling"  "cos"      "cosh"     "cospi"    "cummax"  
#> [13] "cummin"   "cumprod"  "cumsum"   "digamma"  "exp"      "expm1"   
#> [19] "floor"    "gamma"    "lgamma"   "log"      "log10"    "log1p"   
#> [25] "log2"     "sign"     "sin"      "sinh"     "sinpi"    "sqrt"    
#> [31] "tan"      "tanh"     "tanpi"    "trigamma" "trunc"

Commonly used generics of this group include:

log(), log10(), log2(), antilog(), abs(), sign(), floor(), sqrt(), exp()

Ops

Group members:

#>  [1] "+"   "-"   "*"   "^"   "%%"  "%/%" "/"   "=="  ">"   "<"   "!="  "<=" 
#> [13] ">="

Note that for the `==` method if the RHS is also a soma_adat, diffAdats() is invoked which compares LHS vs. RHS. Commonly used generics of this group include:

+, -, *, /, ^, ==, >, <

Summary

Group members:

#> [1] "all"   "any"   "max"   "min"   "prod"  "range" "sum"

Commonly used generics of this group include:

max(), min(), range(), sum(), any()

Author

Stu Field

Examples

# subset `example_data` for speed
# all SeqIds from 2000 -> 2999
seqs <- grep("^seq\\.2[0-9]{3}", names(example_data), value = TRUE)
ex_data_small <- head(example_data[, c(getMeta(example_data), seqs)], 10L)
dim(ex_data_small)
#> [1]  10 264

ex_data_small$seq.2991.9
#>  [1] 4921.8 4392.2 4791.5 6017.2 5711.3 5092.4 4417.9 5394.3 6111.4 6045.2

# Math Generics:
# -------------
# log-transformation
a <- log(ex_data_small)
a$seq.2991.9
#>  [1] 8.501430 8.387586 8.474599 8.702377 8.650202 8.535505 8.393420
#>  [8] 8.593098 8.717911 8.707020

b <- log10(ex_data_small)
b$seq.2991.9
#>  [1] 3.692124 3.642682 3.680471 3.779394 3.756735 3.706923 3.645216
#>  [8] 3.731935 3.786141 3.781411
isTRUE(all.equal(b, log(ex_data_small, base = 10)))
#> [1] TRUE

# floor
c <- floor(ex_data_small)
c$seq.2991.9
#>  [1] 4921 4392 4791 6017 5711 5092 4417 5394 6111 6045

# square-root
d <- sqrt(ex_data_small)
d$seq.2991.9
#>  [1] 70.15554 66.27368 69.22066 77.57061 75.57314 71.36105 66.46729
#>  [8] 73.44590 78.17544 77.75088

# rounding
e <- round(ex_data_small)
e$seq.2991.9
#>  [1] 4922 4392 4792 6017 5711 5092 4418 5394 6111 6045

# inverse log
antilog(1:4)
#> [1]    10   100  1000 10000

alog <- antilog(b)
all.equal(ex_data_small, alog)    # return `b` -> linear space
#> [1] TRUE

# Ops Generics:
# -------------
plus1 <- ex_data_small + 1
times2 <- ex_data_small * 2

sq <- ex_data_small^2
all.equal(sqrt(sq), ex_data_small)
#> [1] TRUE

gt100k <- ex_data_small > 100000
gt100k
#> ══ SomaScan Data ═════════════════════════════════════════════════════════
#>      SomaScan version     V4 (5k)
#>      Signal Space         5k
#>      Attributes intact    ✓
#>      Rows                 10
#>      Columns              264
#>      Clinical Data        34
#>      Features             230
#> ── Column Meta ───────────────────────────────────────────────────────────
#> ℹ SeqId, SeqIdVersion, SomaId, TargetFullName, Target, UniProt,
#> ℹ EntrezGeneID, EntrezGeneSymbol, Organism, Units, Type,
#> ℹ Dilution, PlateScale_Reference, CalReference,
#> ℹ Cal_Example_Adat_Set001, ColCheck,
#> ℹ CalQcRatio_Example_Adat_Set001_170255, QcReference_170255,
#> ℹ Cal_Example_Adat_Set002, CalQcRatio_Example_Adat_Set002_170255,
#> ℹ Dilution2
#> ── Tibble ────────────────────────────────────────────────────────────────
#> # A tibble: 10 × 265
#>    row_names PlateId PlateRunDate ScannerID PlatePosition SlideId Subarray
#>    <chr>     <chr>   <chr>        <chr>     <chr>           <dbl>    <dbl>
#>  1 25849580… Exampl… 2020-06-18   SG152144… H9            2.58e11        3
#>  2 25849580… Exampl… 2020-06-18   SG152144… H8            2.58e11        7
#>  3 25849580… Exampl… 2020-06-18   SG152144… H7            2.58e11        8
#>  4 25849580… Exampl… 2020-06-18   SG152144… H6            2.58e11        4
#>  5 25849580… Exampl… 2020-06-18   SG152144… H5            2.58e11        4
#>  6 25849580… Exampl… 2020-06-18   SG152144… H4            2.58e11        8
#>  7 25849580… Exampl… 2020-06-18   SG152144… H3            2.58e11        3
#>  8 25849580… Exampl… 2020-06-18   SG152144… H2            2.58e11        8
#>  9 25849580… Exampl… 2020-06-18   SG152144… H12           2.58e11        8
#> 10 25849580… Exampl… 2020-06-18   SG152144… H11           2.58e11        3
#> # ℹ 258 more variables: SampleId <chr>, SampleType <chr>,
#> #   PercentDilution <int>, SampleMatrix <chr>, Barcode <lgl>,
#> #   Barcode2d <chr>, SampleName <lgl>, SampleNotes <lgl>,
#> #   AliquotingNotes <lgl>, SampleDescription <chr>, …
#> ══════════════════════════════════════════════════════════════════════════

ex_data_small == ex_data_small   # invokes diffAdats()
#> ══ Checking ADAT attributes & characteristics ════════════════════════════
#> • Attribute names are identical       ✓
#> • Attributes are identical            ✓
#> • ADAT dimensions are identical       ✓
#> • ADAT row names are identical        ✓
#> • ADATs contain identical Features    ✓
#> • ADATs contain same Meta Fields      ✓
#> ── Checking the data matrix ──────────────────────────────────────────────
#> • All Clinical data is identical      ✓
#> • All Feature data is identical       ✓
#> ══════════════════════════════════════════════════════════════════════════
#> NULL

# Summary Generics:
# -------------
sum(ex_data_small)
#> [1] 24319326

any(ex_data_small < 100)  # low RFU analytes
#> [1] TRUE

sum(ex_data_small < 100)  # how many
#> [1] 67

min(ex_data_small)
#> [1] 9.9

min(ex_data_small, 0)
#> [1] 0

max(ex_data_small)
#> [1] 213074.3

max(ex_data_small, 1e+7)
#> [1] 1e+07

range(ex_data_small)
#> [1]      9.9 213074.3

Group Generics for `soma_adat` Class Objects

Usage

Arguments

Value

Functions

Math

Ops

Summary

See also

Author

Examples