This function runs the classification algorithm for all possible sensitivity and specificity options.
Examples
# \donttest{
# Step 0: Load in Raw Data
your_raw_data <- c(
system.file("extdata", "example_MAGPIX_plate1.csv", package = "SeroTrackR"),
system.file("extdata", "example_MAGPIX_plate2.csv", package = "SeroTrackR")
)
your_plate_layout <- system.file("extdata", "example_platelayout_1.xlsx", package = "SeroTrackR")
# Step 1: Reading in Raw Data
sero_data <- readSeroData(raw_data = your_raw_data, "magpix")
#> PASS: File example_magpix_plate1.csv successfully validated.
#> PASS: File example_magpix_plate2.csv successfully validated.
plate_list <- readPlateLayout(
plate_layout = your_plate_layout,
sero_data = sero_data
)
#> Plate layouts correctly identified!
# Step 2: Quality Control and MFI to RAU
qc_results <- runQC(sero_data, plate_list)
# Step 4: Run MFI to RAU (e.g., using ETH beads)
mfi_to_rau_output <- MFItoRAU_Adj(sero_data, plate_list, qc_results)
#> Joining with `by = join_by(antigen)`
#> Joining with `by = join_by(antigen)`
#> Joining with `by = join_by(antigen)`
#> Joining with `by = join_by(antigen)`
#> Joining with `by = join_by(antigen)`
#> Joining with `by = join_by(antigen)`
# Step 5: Render classification table
renderClassificationTable(
mfi_to_rau_output = mfi_to_rau_output,
algorithm_type = "antibody_model",
qc_results = qc_results
)
#> # A tibble: 2 × 3
#> # Groups: Sensitivity/Specificity [2]
#> `Sensitivity/Specificity` Seropositive Seronegative
#> <chr> <int> <int>
#> 1 90% specificity 92 76
#> 2 balanced 150 18
# }
