Pre-Process an ADAT Object for Analysis

Pre-process an ADAT file containing raw analyte RFU values in preparation for analysis. For more details please refer to the pre-processing how-to article

Usage

preProcessAdat(
  adat,
  filter.features = TRUE,
  filter.controls = TRUE,
  filter.qc = TRUE,
  filter.outliers = FALSE,
  data.qc = NULL,
  log.10 = FALSE,
  center.scale = FALSE
)

Arguments

adat

A soma_adat object created using read_adat(), including SeqId columns (seq.xxxxx.xx) containing raw RFU values.

filter.features

Logical. Should non-human protein features (SeqIds) be dropped? Default is TRUE.

filter.controls

Logical. Should SomaScan technical control samples be dropped? If TRUE, this retains all samples where SampleType = "Sample" (study samples) and discards all others including buffer, calibrator, and QC control samples. Default is TRUE.

filter.qc

Logical. If TRUE only samples that pass default normalization acceptance criteria will be retained. Default is TRUE.

filter.outliers

Logical. Should the adat object drop outlier samples? An outlier sample is defined by >= 5% of filtered SeqIds exceeding +/- 6 MAD and 5x fold-change from the median signal. This filter is typically appropriate for studies on plasma, serum, and other biological matrices generally exhibiting homeostatic characteristics. For studies on matrices such as tissue homogenate, cell culture, or study designs containing client-provided background lysis buffer controls (or similar), this filter will likely not be appropriate. Default is FALSE. If set to TRUE it is highly recommended that filter.controls is also set to TRUE

data.qc

Character. Character vector of variable names for which data QC plots are desired. Default is NULL, which does not generate any QC plots. Note: These plots are for visual inspection only, no samples or features are dropped from the output soma_adat object.

log.10

Logical. Should the RFU values be log10 transformed? Default is FALSE.

center.scale

Logical. Should the RFU values be Z-transformed (centered and scaled)? Default is FALSE. If set to set to TRUE it is highly recommended that log.10 is also set to TRUE

Value

A soma_adat object.

Details

The soma_adat object is pre-processed with the following steps:

1. Filter features -> down to human protein analytes

2. Filter samples -> by the following order and criteria: a) Retain study samples only (dropping buffer, calibrator, and QC samples) b) Only those that pass default normalization acceptance criteria c) Those not identified as outliers.

3. Data QC -> plots of normalization scale factors by clinical covariates

4. Transformations -> log10, center, and scale analyte RFU values

Author

Caleb Scheidel

Examples

preProcessAdat(example_data, data.qc = c("Age", "Sex"))
#> ✔ 305 non-human protein features were removed.
#> → 214 human proteins did not pass standard QC
#> acceptance criteria and were flagged in `ColCheck`.  These features
#> were not removed, as they still may yield useful information in an
#> analysis, but further evaluation may be needed.
#> ✔ 6 buffer samples were removed.
#> ✔ 10 calibrator samples were removed.
#> ✔ 6 QC samples were removed.
#> ✔ 2 samples flagged in `RowCheck` did not
#> pass standard normalization acceptance criteria (0.4 <= x <= 2.5)
#> and were removed.
#> → Data QC plots were generated:
#> $Age

#> 
#> $Sex

#> 
#> ══ SomaScan Data ══════════════════════════════════════════════════════
#>      SomaScan version     V4 (5k)
#>      Signal Space         5k
#>      Attributes intact    ✓
#>      Rows                 168
#>      Columns              5013
#>      Clinical Data        34
#>      Features             4979
#> ── Column Meta ────────────────────────────────────────────────────────
#> ℹ SeqId, SeqIdVersion, SomaId, TargetFullName, Target,
#> ℹ UniProt, EntrezGeneID, EntrezGeneSymbol, Organism, Units,
#> ℹ Type, Dilution, PlateScale_Reference, CalReference,
#> ℹ Cal_Example_Adat_Set001, ColCheck,
#> ℹ CalQcRatio_Example_Adat_Set001_170255, QcReference_170255,
#> ℹ Cal_Example_Adat_Set002,
#> ℹ CalQcRatio_Example_Adat_Set002_170255, Dilution2
#> ── Tibble ─────────────────────────────────────────────────────────────
#> # A tibble: 168 × 5,014
#>    row_names      PlateId  PlateRunDate ScannerID PlatePosition SlideId
#>    <chr>          <chr>    <chr>        <chr>     <chr>           <dbl>
#>  1 258495800012_3 Example… 2020-06-18   SG152144… H9            2.58e11
#>  2 258495800004_7 Example… 2020-06-18   SG152144… H8            2.58e11
#>  3 258495800010_8 Example… 2020-06-18   SG152144… H7            2.58e11
#>  4 258495800003_4 Example… 2020-06-18   SG152144… H6            2.58e11
#>  5 258495800009_4 Example… 2020-06-18   SG152144… H5            2.58e11
#>  6 258495800012_8 Example… 2020-06-18   SG152144… H4            2.58e11
#>  7 258495800001_3 Example… 2020-06-18   SG152144… H3            2.58e11
#>  8 258495800004_8 Example… 2020-06-18   SG152144… H2            2.58e11
#>  9 258495800001_8 Example… 2020-06-18   SG152144… H12           2.58e11
#> 10 258495800009_8 Example… 2020-06-18   SG152144… H10           2.58e11
#> # ℹ 158 more rows
#> # ℹ 5,008 more variables: Subarray <dbl>, SampleId <chr>,
#> #   SampleType <chr>, PercentDilution <int>, SampleMatrix <chr>,
#> #   Barcode <lgl>, Barcode2d <chr>, SampleName <lgl>,
#> #   SampleNotes <lgl>, AliquotingNotes <lgl>, …
#> ═══════════════════════════════════════════════════════════════════════