Anonymised observation-level data from a Luminex multiplex immunoassay measuring antibody responses across two vaccine groups, four timepoints, eleven antigens, and ten immunological features. Each observation includes the predicted concentration from a semi-supervised, best fitting calibration curve, its standard error, and the posterior coefficient of variation (pcov) that quantifies calibration curve uncertainty. All calculations were computed in I-SPI using the bayesian regression approach (https://immunoplex.org/i-spi-docs) .
Format
A data frame with 48,224 rows and 11 columns:
- plate
Character. Plate identifier (15 plates: plate_1 to plate_15).
- nominal_sample_dilution
Character. Anonymised dilution factor (8 levels: dil_1 to dil_8).
- patientid
Character. Anonymised subject identifier (150 subjects: S001 to S150).
- group_a
Character. Vaccine group (2 levels: vaccine_a, vaccine_b). This is the treatment arm used as the independent variable in arm-effect comparisons.
- group_b
Character. Timepoint (4 levels: timepoint_1 to timepoint_4, in chronological order).
- antigen
Character. Target antigen (11 levels: act, dt, fha, fim, ipv1, ipv2, ipv3, pentamer, prn, pt, tt).
- feature
Character. Immunological readout (10 levels: ADCD, ADCP, ADNP, FcgR2a, FcgR3b, IgG1, IgG2, IgG3, IgG4, Total_IgG).
- mfi
Numeric. Median fluorescence intensity (raw assay readout).
- predicted_concentration
Numeric. Concentration predicted from the 4PL calibration curve (arbitrary units).
- se_concentration
Numeric. Standard error of the predicted concentration from the calibration curve posterior.
- pcov
Numeric. Posterior coefficient of variation from the calibration curve: se_concentration / predicted_concentration for midrange samples, but correctly capturing the non-Gaussian posterior near LLOQ and ULOQ. This is the precision index used by
curveRweightsto estimate observation-level weights.
Details
The data were anonymised by replacing patient IDs with random codes (S001 to S150, shuffled), sample dilutions with generic labels (dil_1 to dil_8), and timepoint names with sequential labels (timepoint_1 to timepoint_4). All measurement values (mfi, predicted_concentration, se_concentration, pcov) are unchanged.
Typical usage selects one (antigen, feature) combination for analysis. For example, IgG1 responses to pertussis antigens (pt, fha, prn) are a natural starting point:
Examples
data(example_assay)
str(example_assay)
#> 'data.frame': 48224 obs. of 11 variables:
#> $ plate : chr "plate_1" "plate_1" "plate_1" "plate_1" ...
#> $ nominal_sample_dilution: chr "dil_6" "dil_6" "dil_6" "dil_6" ...
#> $ patientid : chr "S013" "S013" "S013" "S013" ...
#> $ group_a : chr "vaccine_b" "vaccine_b" "vaccine_b" "vaccine_b" ...
#> $ group_b : chr "timepoint_3" "timepoint_3" "timepoint_3" "timepoint_3" ...
#> $ antigen : chr "act" "dt" "ipv1" "ipv2" ...
#> $ feature : chr "IgG1" "IgG1" "IgG1" "IgG1" ...
#> $ mfi : num 43 1034 101 85 1169 ...
#> $ predicted_concentration: num 1.2 39.58 8.74 1.33 11.9 ...
#> $ se_concentration : num 0.1546 3.5301 0.5288 0.0633 1.1052 ...
#> $ pcov : num 0.1767 0.0789 0.102 0.0405 0.1409 ...
table(example_assay$antigen, example_assay$feature)
#>
#> ADCD ADCP ADNP FcgR2a FcgR3b IgG1 IgG2 IgG3 IgG4 Total_IgG
#> act 510 0 0 512 512 512 512 512 512 446
#> dt 510 502 502 512 512 512 512 512 512 446
#> fha 510 0 0 511 512 512 512 512 512 446
#> fim 510 0 0 512 512 512 512 512 512 446
#> ipv1 510 0 0 511 512 512 417 512 512 446
#> ipv2 510 0 0 512 512 512 512 512 512 446
#> ipv3 510 0 0 512 512 512 512 512 512 446
#> pentamer 510 0 0 512 512 512 512 512 512 446
#> prn 510 502 502 511 512 512 512 512 512 446
#> pt 510 502 502 511 512 512 512 512 512 446
#> tt 510 502 502 511 512 512 512 512 512 446