A named list of simulated multi-plate ELISA data for a single analyte (alpha), spanning 6 plates. Plates 1-3 use a 5-parameter logistic standard curve; plates 4-6 use Gompertz, reflecting realistic between-plate variability in curve shape.
Format
A named list with six elements:
- standards
Data frame (60 rows x 8 cols). Standard curve data with columns:
curve_id,stype,sampleid,well,dilution,od,assay_response_variable,assay_independent_variable.- blanks
Data frame (24 rows x 7 cols). Blank well measurements.
- samples
Data frame (120 rows x 12 cols). Patient samples at fixed serum dilution 1:400.
- curve_id_lookup
Data frame (6 rows x 5 cols). Maps
curve_idtoantigen,study_accession,experiment_accession,plate.- response_var
Character:
"od".- indep_var
Character:
"concentration".
Source
Synthetic data with biologically plausible parameters and proportional CV matching typical ELISA plate-reader behaviour.
Examples
data(elisa_assay_example)
str(elisa_assay_example, max.level = 1)
#> List of 6
#> $ standards :'data.frame': 60 obs. of 8 variables:
#> $ blanks :'data.frame': 24 obs. of 7 variables:
#> $ samples :'data.frame': 120 obs. of 12 variables:
#> $ curve_id_lookup:'data.frame': 6 obs. of 5 variables:
#> $ response_var : chr "od"
#> $ indep_var : chr "concentration"
head(elisa_assay_example$standards)
#> curve_id stype sampleid well dilution od assay_response_variable
#> 1 1 S STD_01 A1 1000.000000 0.0955 od
#> 2 1 S STD_02 B1 333.333333 0.0865 od
#> 3 1 S STD_03 C1 100.000000 0.0672 od
#> 4 1 S STD_04 D1 33.333333 0.0937 od
#> 5 1 S STD_05 E1 10.000000 0.1885 od
#> 6 1 S STD_06 F1 3.333333 0.7303 od
#> assay_independent_variable
#> 1 concentration
#> 2 concentration
#> 3 concentration
#> 4 concentration
#> 5 concentration
#> 6 concentration