The soma_adat Class and S3 Methods
Source: R/s3-soma-adat.R, R/s3-print-soma-adat.R, R/s3-summary-soma-adat.R
soma_adat.RdThe soma_adat data structure is the primary internal R representation
of SomaScan data. A soma_adat is automatically created via read_adat()
when loading a *.adat text file. It consists of a data.frame-like
object with leading columns as clinical variables and SomaScan RFU data
as the remaining variables. Two main attributes corresponding to analyte
and SomaScan run information contained in the *.adat file are added:
Header.Meta: information about the SomaScan run, seeparseHeader()orattr(x, "Header.Meta")Col.Meta: annotations information about the SomaScan reagents/analytes, seegetAnalyteInfo()orattr(x, "Col.Meta")file_specs: parsing specifications for the ingested*.adatfilerow_meta: the names of the non-RFU fields. SeegetMeta().
See groupGenerics() for a details on Math(), Ops(), and Summary()
methods that dispatch on class soma_adat.
See reexports() for a details on re-exported S3 generics from other
packages (mostly dplyr and tidyr) to enable S3 methods to be
dispatched on class soma_adat.
Below is a list of all currently available S3 methods that dispatch on
the soma_adat class:
#> [1] [ [[ [[<- [<-
#> [5] == $ $<- anti_join
#> [9] arrange count filter full_join
#> [13] getAdatVersion getAnalytes getMeta group_by
#> [17] inner_join is_seqFormat left_join Math
#> [21] median merge mutate Ops
#> [25] print rename right_join row.names<-
#> [29] sample_frac sample_n select semi_join
#> [33] separate slice_sample slice summary
#> [37] Summary transform ungroup unite
#> see '?methods' for accessing help and source codeThe S3 print() method returns summary information parsed from the object
attributes, if present, followed by a dispatch to the tibble() print method.
Rownames are printed as the first column in the print method only.
The S3 summary() method returns the following for each column of the ADAT
object containing SOMAmer data (clinical meta data is excluded):
Target (if available)
Minimum value
1st Quantile
Median
Mean
3rd Quantile
Maximum value
Standard deviation
Median absolute deviation (
mad())Interquartile range (
IQR())
The S3 Extract() method is used for sub-setting a soma_adat
object and relies heavily on the [ method that maintains the soma_adat
attributes intact and subsets the Col.Meta so that it is consistent
with the newly created object.
S3 extraction via $ is fully supported, however,
as opposed to the data.frame method, partial matching
is not allowed for class soma_adat.
S3 extraction via [[ is supported, however, we restrict
the usage of [[ for soma_adat. Use only a numeric index (e.g. 1L)
or a character identifying the column (e.g. "SampleID").
Do not use [[i,j]] syntax with [[, use [ instead.
As with $, partial matching is not allowed.
S3 assignment via [ is supported for class soma_adat.
S3 assignment via $ is fully supported for class soma_adat.
S3 assignment via [[ is supported for class soma_adat.
S3 median() is not currently supported for the soma_adat class,
however a dispatch is in place to direct users to alternatives.
Usage
# S3 method for soma_adat
print(x, show_header = FALSE, ...)
# S3 method for soma_adat
summary(object, tbl = NULL, digits = max(3L, getOption("digits") - 3L), ...)
# S3 method for soma_adat
[(x, i, j, drop = TRUE, ...)
# S3 method for soma_adat
$(x, name)
# S3 method for soma_adat
[[(x, i, j, ..., exact = TRUE)
# S3 method for soma_adat
[(x, i, j, ...) <- value
# S3 method for soma_adat
$(x, i, j, ...) <- value
# S3 method for soma_adat
[[(x, i, j, ...) <- value
# S3 method for soma_adat
median(x, na.rm = FALSE, ...)Arguments
- x, object
A
soma_adatclass object.- show_header
Logical. Should all the
Header Datainformation be displayed instead of the data frame (tibble) object?- ...
Ignored.
- tbl
An annotations table. If
NULL(default), annotation information is extracted from the object itself (if possible). Alternatively, the result of a call togetAnalyteInfo(), from which Target names can be extracted.- digits
Integer. Used for number formatting with
signif().- i, j
Row and column indices respectively. If
jis omitted,iis used as the column index.- drop
Coerce to a vector if fetching one column via
tbl[, j]. DefaultFALSE, ignored when accessing a column viatbl[j].- name
A name or a string.
- exact
Ignored with a
warning().- value
A value to store in a row, column, range or cell.
- na.rm
a logical value indicating whether
NAvalues should be stripped before the computation proceeds.
Value
The set of S3 methods above return the soma_adat object with
the corresponding S3 method applied.
See also
Other IO:
loadAdatsAsList(),
parseHeader(),
read_adat(),
write_adat()
Examples
# S3 print method
example_data
#> ══ SomaScan Data ═════════════════════════════════════════════════════════
#> SomaScan version V4 (5k)
#> Signal Space 5k
#> Attributes intact ✓
#> Rows 192
#> Columns 5318
#> Clinical Data 34
#> Features 5284
#> ── 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: 192 × 5,319
#> 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 182 more rows
#> # ℹ 5,312 more variables: SampleId <chr>, SampleType <chr>,
#> # PercentDilution <int>, SampleMatrix <chr>, Barcode <lgl>,
#> # Barcode2d <chr>, SampleName <lgl>, SampleNotes <lgl>,
#> # AliquotingNotes <lgl>, SampleDescription <chr>, …
#> ══════════════════════════════════════════════════════════════════════════
# show the header info (no RFU data)
print(example_data, show_header = TRUE)
#> ══ SomaScan Data ═════════════════════════════════════════════════════════
#> SomaScan version V4 (5k)
#> Signal Space 5k
#> Attributes intact ✓
#> Rows 192
#> Columns 5318
#> Clinical Data 34
#> Features 5284
#> ── 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
#> ── Header Data ───────────────────────────────────────────────────────────
#> # A tibble: 35 × 2
#> Key Value
#> <chr> <chr>
#> 1 AdatId GID-1234-56-789-abcdef
#> 2 Version 1.2
#> 3 AssayType PharmaServices
#> 4 AssayVersion V4
#> 5 AssayRobot Fluent 1 L-307
#> 6 Legal Experiment details and data have been processed t…
#> 7 CreatedBy PharmaServices
#> 8 CreatedDate 2020-07-24
#> 9 EnteredBy Technician1
#> 10 ExpDate 2020-06-18, 2020-07-20
#> 11 GeneratedBy Px (Build: : ), Canopy_0.1.1
#> 12 RunNotes 2 columns ('Age' and 'Sex') have been added to th…
#> 13 ProcessSteps Raw RFU, Hyb Normalization, medNormInt (SampleId)…
#> 14 ProteinEffectiveDate 2019-08-06
#> 15 StudyMatrix EDTA Plasma
#> # ℹ 20 more rows
#> ══════════════════════════════════════════════════════════════════════════
# S3 summary method
# MMP analytes (4)
mmps <- c("seq.2579.17", "seq.2788.55", "seq.2789.26", "seq.4925.54")
mmp_adat <- example_data[, c("Sex", mmps)]
summary(mmp_adat)
#> seq.2579.17 seq.2788.55 seq.2789.26
#> Target : MMP-9 Target : MMP-3 Target : MMP-7
#> Min : 59.3 Min : 29.30 Min : 24.1
#> 1Q : 9360.5 1Q : 126.67 1Q : 8182.0
#> Median : 13191.2 Median : 149.75 Median : 10947.8
#> Mean : 14372.5 Mean : 157.78 Mean : 11277.6
#> 3Q : 19387.3 3Q : 179.08 3Q : 13577.0
#> Max : 51263.0 Max : 423.20 Max : 33496.9
#> sd : 7449.9 sd : 53.57 sd : 5802.6
#> MAD : 6877.9 MAD : 38.62 MAD : 4001.2
#> IQR : 10026.8 IQR : 52.40 IQR : 5395.0
#> seq.4925.54
#> Target : MMP-13
#> Min : 25.20
#> 1Q : 328.30
#> Median : 389.70
#> Mean : 424.48
#> 3Q : 452.85
#> Max : 2883.20
#> sd : 259.01
#> MAD : 93.85
#> IQR : 124.55
# Summarize by group
mmp_adat |>
split(mmp_adat$Sex) |>
lapply(summary)
#> $F
#> seq.2579.17 seq.2788.55 seq.2789.26
#> Target : MMP-9 Target : MMP-3 Target : MMP-7
#> Min : 4484 Min : 97.10 Min : 289.7
#> 1Q : 8336 1Q : 127.70 1Q : 9410.0
#> Median : 12859 Median : 150.60 Median : 12157.2
#> Mean : 13674 Mean : 156.02 Mean : 12877.3
#> 3Q : 16148 3Q : 181.10 3Q : 15151.9
#> Max : 31879 Max : 235.40 Max : 32221.3
#> sd : 6389 sd : 34.66 sd : 5214.4
#> MAD : 6488 MAD : 38.10 MAD : 4342.7
#> IQR : 7811 IQR : 53.40 IQR : 5741.9
#> seq.4925.54
#> Target : MMP-13
#> Min : 241.80
#> 1Q : 333.80
#> Median : 388.10
#> Mean : 422.14
#> 3Q : 460.70
#> Max : 1353.80
#> sd : 171.89
#> MAD : 95.78
#> IQR : 126.90
#>
#> $M
#> seq.2579.17 seq.2788.55 seq.2789.26
#> Target : MMP-9 Target : MMP-3 Target : MMP-7
#> Min : 5894 Min : 99.90 Min : 2850
#> 1Q : 9973 1Q : 132.40 1Q : 8640
#> Median : 13645 Median : 157.40 Median : 11230
#> Mean : 15715 Mean : 173.63 Mean : 11699
#> 3Q : 20595 3Q : 196.20 3Q : 13061
#> Max : 51263 Max : 423.20 Max : 33497
#> sd : 7979 sd : 61.17 sd : 5243
#> MAD : 6344 MAD : 40.18 MAD : 3477
#> IQR : 10622 IQR : 63.80 IQR : 4421
#> seq.4925.54
#> Target : MMP-13
#> Min : 205.7
#> 1Q : 329.3
#> Median : 401.6
#> Mean : 462.7
#> 3Q : 468.9
#> Max : 2883.2
#> sd : 331.3
#> MAD : 103.8
#> IQR : 139.6
#>
# Alternatively pass annotations with Target info
anno <- getAnalyteInfo(mmp_adat)
summary(mmp_adat, tbl = anno)
#> seq.2579.17 seq.2788.55 seq.2789.26
#> Target : MMP-9 Target : MMP-3 Target : MMP-7
#> Min : 59.3 Min : 29.30 Min : 24.1
#> 1Q : 9360.5 1Q : 126.67 1Q : 8182.0
#> Median : 13191.2 Median : 149.75 Median : 10947.8
#> Mean : 14372.5 Mean : 157.78 Mean : 11277.6
#> 3Q : 19387.3 3Q : 179.08 3Q : 13577.0
#> Max : 51263.0 Max : 423.20 Max : 33496.9
#> sd : 7449.9 sd : 53.57 sd : 5802.6
#> MAD : 6877.9 MAD : 38.62 MAD : 4001.2
#> IQR : 10026.8 IQR : 52.40 IQR : 5395.0
#> seq.4925.54
#> Target : MMP-13
#> Min : 25.20
#> 1Q : 328.30
#> Median : 389.70
#> Mean : 424.48
#> 3Q : 452.85
#> Max : 2883.20
#> sd : 259.01
#> MAD : 93.85
#> IQR : 124.55