This function processes a plate and computes normalised values based on the specified normalisation_type.
The function supports three types of normalisation:
RAU (Relative Antibody Units) - Default normalisation type.
nMFI (Normalised Median Fluorescence Intensity).
MFI (Median Fluorescence Intensity, blank-adjusted raw MFI values).
Depending on the chosen normalisation type, the function:
Adjusts for blanks (if
blank_adjustment = TRUE).Computes the relevant normalised values for each analyte in the plate.
Aggregates computed values into a single data frame.
Optionally writes the results to a CSV file.
RAU Normalisation Workflow:
Blank adjustment (if enabled).
Fit standard curve models for each analyte.
Compute RAU values based on the fitted models.
Aggregate RAU values into a data frame.
Save the results to a CSV file.
nMFI Normalisation Workflow:
Blank adjustment (if enabled).
Compute nMFI values using the target dilution.
Aggregate nMFI values into a data frame.
Save the results to a CSV file.
MFI Normalisation Workflow:
Blank adjustment (if enabled).
Save the adjusted MFI values to a CSV file.
If the plate is already blank-adjusted, setting blank_adjustment = TRUE has no effect.
More details on the normalisation methods can be found in:
nMFI: get_nmfi.
Usage
process_plate(
plate,
filename = NULL,
output_dir = "normalised_data",
write_output = TRUE,
normalisation_type = "RAU",
data_type = "Median",
blank_adjustment = FALSE,
verbose = TRUE,
reference_dilution = 1/400,
...
)Arguments
- plate
(
Plate) A plate object containing fluorescence intensity data.- filename
(
character(1), optional) Output CSV filename. Defaults to{plate_name}_{normalisation_type}.csv.If omitted (
NULL), the filename is auto-generated.If the filename lacks
.csv, the extension is automatically added.If
output_diris specified, it is combined withfilenameunlessfilenameis an absolute path.If a file already exists at the path, it will be overwritten.
- output_dir
(
character(1), default ='normalised_data') Directory for saving the output file.If the directory does not exist, it will be created.
If
NULL, the current working directory is used.
- write_output
(
logical(1), default =TRUE) Whether to save the output to a CSV file.- normalisation_type
(
character(1), default ='RAU') The normalisation method to apply.Allowed values:
c(MFI, RAU, nMFI).
- data_type
(
character(1), default ='Median') The data type to use for calculations.- blank_adjustment
(
logical(1), default =FALSE) Whether to perform blank adjustment before computing values.- verbose
(
logical(1), default =TRUE) Whether to print additional information during execution.- reference_dilution
(
numeric(1)orcharacter(1), default =1/400)The target dilution used as a reference for nMFI normalisation.
Ignored for RAU normalisation.
Can be numeric (e.g.,
0.0025) or a string ('1/400').
- ...
Additional arguments passed to the model fitting function (
create_standard_curve_model_analyte).
Examples
plate_file <- system.file("extdata", "CovidOISExPONTENT_CO_reduced.csv", package = "SerolyzeR")
layout_file <- system.file("extdata", "CovidOISExPONTENT_CO_layout.xlsx", package = "SerolyzeR")
plate <- read_luminex_data(plate_file, layout_file, verbose = FALSE)
#> Could not parse datetime string using default datetime format. Trying other possibilies.
#> Successfully parsed datetime string using order: mdY IM p
example_dir <- tempdir(check = TRUE)
# Process plate with default settings (RAU normalisation)
process_plate(plate, output_dir = example_dir)
#> Warning: The specified file /tmp/Rtmpl5e8H2/CovidOISExPONTENT_CO_reduced_RAU.csv already exists. Overwriting it.
#> Fitting the models and predicting RAU for each analyte
#> Saving the computed RAU values to a CSV file located in: '/tmp/Rtmpl5e8H2/CovidOISExPONTENT_CO_reduced_RAU.csv'
#> Spike_6P_IPP ME_NA
#> K086-LM 3292.26101 2128.6098
#> M254-VM 85.38753 5496.4318
#> M199-DS 2841.64505 7169.3408
#> M164-LM 125.42694 3560.9584
#> M265-MM 396.34769 3783.9004
#> K026-DJ 85.13506 1430.5461
#> K137-DT 3142.28223 6567.6666
#> M255-VA 38.78304 1103.7202
#> M258-PA 52.98592 2174.6786
#> M188-VC 1137.87727 1877.6962
#> M270-BF 58.80200 875.6587
#> M050-EL 100.64396 2168.0885
#> M088-GE 145.30638 2892.0240
#> M259-PM 1082.02893 8614.0967
#> K101-PA 73.84503 1777.7663
#> M189-VY 511.61479 2562.3316
#> K018-FC 882.55266 2250.6828
#> M142-RA 54.43368 966.1990
#> K100-CC 42.98563 984.9380
#> K107-RP 32.36841 1138.1493
#> M240-HS 31.46594 1028.6766
#> K019-FM 83.62164 1589.0900
#> M148-PS 61.97864 1216.4977
#> M089-HG 4677.61877 7712.7546
#> K136-DA 20000.00000 17238.7897
#> M241-HR 5532.92911 1279.2952
#> K020-NA 12680.55047 9631.3814
#> M239-HJ 40.41069 2654.1971
#> M092-LS 164.84476 3102.6112
#> M162-PE 4972.48078 5738.5101
#> M260-PM 20000.00000 2370.4746
#> K021-FS 28.78211 635.1388
#> M253-VM 80.35078 1235.3249
#> M198-CN 60.26598 2056.5039
#> M163-PD 3780.09488 6970.3730
#> M264-MA 20000.00000 2197.7678
#> K024-DT 981.88707 2867.6895
# Process plate without blank adjustment, custom filename
process_plate(plate,
filename = "plate_no_blank_adjustment.csv",
output_dir = example_dir, blank_adjustment = FALSE
)
#> Fitting the models and predicting RAU for each analyte
#> Saving the computed RAU values to a CSV file located in: '/tmp/Rtmpl5e8H2/plate_no_blank_adjustment.csv'
#> Spike_6P_IPP ME_NA
#> K086-LM 3292.26101 2128.6098
#> M254-VM 85.38753 5496.4318
#> M199-DS 2841.64505 7169.3408
#> M164-LM 125.42694 3560.9584
#> M265-MM 396.34769 3783.9004
#> K026-DJ 85.13506 1430.5461
#> K137-DT 3142.28223 6567.6666
#> M255-VA 38.78304 1103.7202
#> M258-PA 52.98592 2174.6786
#> M188-VC 1137.87727 1877.6962
#> M270-BF 58.80200 875.6587
#> M050-EL 100.64396 2168.0885
#> M088-GE 145.30638 2892.0240
#> M259-PM 1082.02893 8614.0967
#> K101-PA 73.84503 1777.7663
#> M189-VY 511.61479 2562.3316
#> K018-FC 882.55266 2250.6828
#> M142-RA 54.43368 966.1990
#> K100-CC 42.98563 984.9380
#> K107-RP 32.36841 1138.1493
#> M240-HS 31.46594 1028.6766
#> K019-FM 83.62164 1589.0900
#> M148-PS 61.97864 1216.4977
#> M089-HG 4677.61877 7712.7546
#> K136-DA 20000.00000 17238.7897
#> M241-HR 5532.92911 1279.2952
#> K020-NA 12680.55047 9631.3814
#> M239-HJ 40.41069 2654.1971
#> M092-LS 164.84476 3102.6112
#> M162-PE 4972.48078 5738.5101
#> M260-PM 20000.00000 2370.4746
#> K021-FS 28.78211 635.1388
#> M253-VM 80.35078 1235.3249
#> M198-CN 60.26598 2056.5039
#> M163-PD 3780.09488 6970.3730
#> M264-MA 20000.00000 2197.7678
#> K024-DT 981.88707 2867.6895
# Process plate with nMFI normalisation
process_plate(plate,
output_dir = example_dir, normalisation_type = "nMFI",
reference_dilution = 1 / 400
)
#> Warning: The specified file /tmp/Rtmpl5e8H2/CovidOISExPONTENT_CO_reduced_nMFI.csv already exists. Overwriting it.
#> Computing nMFI values for each analyte
#> Saving the computed nMFI values to a CSV file located in: '/tmp/Rtmpl5e8H2/CovidOISExPONTENT_CO_reduced_nMFI.csv'
#> Spike_6P_IPP ME_NA
#> K086-LM 1.32720798 0.9407979
#> M254-VM 0.05968661 2.0952381
#> M199-DS 1.19088319 2.5559846
#> M164-LM 0.08190883 1.4723295
#> M265-MM 0.22336182 1.5495495
#> K026-DJ 0.05954416 0.6615187
#> K137-DT 1.28290598 2.3976834
#> M255-VA 0.03247863 0.5276705
#> M258-PA 0.04102564 0.9588160
#> M188-VC 0.56766382 0.8416988
#> M270-BF 0.04444444 0.4337194
#> M050-EL 0.06823362 0.9562420
#> M088-GE 0.09273504 1.2316602
#> M259-PM 0.54358974 2.9060489
#> K101-PA 0.05313390 0.8018018
#> M189-VY 0.28062678 1.1081081
#> K018-FC 0.45527066 0.9884170
#> M142-RA 0.04188034 0.4710425
#> K100-CC 0.03504274 0.4787645
#> K107-RP 0.02849003 0.5418275
#> M240-HS 0.02792023 0.4967825
#> K019-FM 0.05868946 0.7258687
#> M148-PS 0.04629630 0.5740026
#> M089-HG 1.69344729 2.6924067
#> K136-DA 4.10655271 4.3912484
#> M241-HR 1.88774929 0.5997426
#> K020-NA 2.98148148 3.1299871
#> M239-HJ 0.03347578 1.1428571
#> M092-LS 0.10327635 1.3088803
#> M162-PE 1.76282051 2.1660232
#> M260-PM 4.17592593 1.0347490
#> K021-FS 0.02621083 0.3346203
#> M253-VM 0.05683761 0.5817246
#> M198-CN 0.04529915 0.9124839
#> M163-PD 1.46452991 2.5045045
#> M264-MA 5.67492877 0.9678250
#> K024-DT 0.49971510 1.2226512