Binary Classification

Classification via Logistic Regression

Although targeted statistical analyses are beyond the scope of the SomaDataIO package, below is an example analysis that typical users/customers would perform on ‘SomaScan’ data.

It is not intended to be a definitive guide in statistical analysis and existing packages do exist in the R ecosystem that perform parts or extensions of these techniques. Many variations of the workflow below exist, however the framework highlights how one could perform standard preliminary analyses on ‘SomaScan’ data.

Data Preparation

# the `example_data` .adat object
# download from `SomaLogic-Data` repo or directly via bash command:
# `wget https://raw.githubusercontent.com/SomaLogic/SomaLogic-Data/main/example_data.adat`
# then read in to R with:
# example_data <- read_adat("example_data.adat")
dim(example_data)
#> [1]  192 5318

table(example_data$SampleType)
#> 
#>     Buffer Calibrator         QC     Sample 
#>          6         10          6        170

# prepare data set for analysis using `preProcessAdat()`
cleanData <- example_data |>
  preProcessAdat(
    filter.features = TRUE,            # rm non-human protein features
    filter.controls = TRUE,            # rm control samples
    filter.qc       = TRUE,            # rm non-passing qc samples
    log.10          = TRUE,            # log10 transform
    center.scale    = TRUE             # center/scale analytes
  )
#> ✔ 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.
#> ✔ RFU features were log-10 transformed.
#> ✔ RFU features were centered and scaled.

# drop any missing values in Sex, and convert to binary 0/1 variable
cleanData <- cleanData |> 
  drop_na(Sex) |>                              # rm NAs if present
  mutate(Group = as.numeric(factor(Sex)) - 1)  # map Sex -> 0/1

table(cleanData$Sex)
#> 
#>  F  M 
#> 85 83

table(cleanData$Group)    # F = 0; M = 1
#> 
#>  0  1 
#> 85 83

Set up Train/Test Data

# idx = hold-out 
# seed resulting in 50/50 class balance
idx   <- withr::with_seed(3, sample(1:nrow(cleanData), size = nrow(cleanData) - 50))
train <- cleanData[idx, ]
test  <- cleanData[-idx, ]

# assert no overlap
isTRUE(
  all.equal(intersect(rownames(train), rownames(test)), character(0))
)
#> [1] TRUE

Logistic Regression

We use the cleanData, train, and test data objects from above.

Predict Sex

LR_tbl <- getAnalyteInfo(train) |>
  select(AptName, SeqId, Target = TargetFullName, EntrezGeneSymbol, UniProt) |>
  mutate(
    formula  = map(AptName, ~ as.formula(paste("Group ~", .x))),  # create formula
    model    = map(formula, ~ stats::glm(.x, data = train, family = "binomial", model = FALSE)),  # fit glm()
    beta_hat = map(model, coef) |> map_dbl(2L),     # pull out coef Beta
    p.value  = map2_dbl(model, AptName, ~ {
      summary(.x)$coefficients[.y, "Pr(>|z|)"] }),  # pull out p-values
    fdr      = p.adjust(p.value, method = "BH")     # FDR correction multiple testing
  ) |>
  arrange(p.value) |>            # re-order by `p-value`
  mutate(rank = row_number())    # add numeric ranks

LR_tbl
#> # A tibble: 4,979 × 11
#>    AptName      SeqId   Target EntrezGeneSymbol UniProt formula   model
#>    <chr>        <chr>   <chr>  <chr>            <chr>   <list>    <lis>
#>  1 seq.6580.29  6580-29 Pregn… PZP              P20742  <formula> <glm>
#>  2 seq.5763.67  5763-67 Beta-… DEFB104A         Q8WTQ1  <formula> <glm>
#>  3 seq.7926.13  7926-13 Kunit… SPINT3           P49223  <formula> <glm>
#>  4 seq.3032.11  3032-11 Folli… CGA FSHB         P01215… <formula> <glm>
#>  5 seq.7139.14  7139-14 SLIT … SLITRK4          Q8IW52  <formula> <glm>
#>  6 seq.16892.23 16892-… Ecton… ENPP2            Q13822  <formula> <glm>
#>  7 seq.2953.31  2953-31 Lutei… CGA LHB          P01215… <formula> <glm>
#>  8 seq.9282.12  9282-12 Cyste… CRISP2           P16562  <formula> <glm>
#>  9 seq.4914.10  4914-10 Human… CGA CGB          P01215… <formula> <glm>
#> 10 seq.2474.54  2474-54 Serum… APCS             P02743  <formula> <glm>
#> # ℹ 4,969 more rows
#> # ℹ 4 more variables: beta_hat <dbl>, p.value <dbl>, fdr <dbl>,
#> #   rank <int>

Fit Model | Calculate Performance

Next, select features for the model fit. We have a good idea of reasonable Sex markers from prior knowledge (CGA*), and fortunately many of these are highly ranked in LR_tbl. Below we fit a 4-marker logistic regression model from cherry-picked gender-related features: