Processes a Luminex plate and computes normalised values using the specified
normalisation_type. Depending on the chosen method, the function performs
blank adjustment, fits models, and extracts values for test samples.
Optionally, the results can be saved as a CSV file.
Usage
process_plate(
plate,
filename = NULL,
output_dir = "normalised_data",
write_output = TRUE,
normalisation_type = "RAU",
data_type = "Median",
sample_type_filter = "ALL",
blank_adjustment = FALSE,
verbose = TRUE,
reference_dilution = 1/400,
...
)Arguments
- plate
A Plate object containing raw or processed Luminex data.
- filename
(
character(1), optional) Output CSV filename. IfNULL, defaults to"{plate_name}_{normalisation_type}.csv". File extension is auto-corrected to.csvif missing. If an absolute path is given,output_diris ignored.- output_dir
(
character(1), default ="normalised_data") Directory where the CSV will be saved. Will be created if it doesn't exist. IfNULL, the current working directory is used.- write_output
(
logical(1), default =TRUE) Whether to write the output to disk.- 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 normalisation (e.g.,"Median").- sample_type_filter
(
character()) The types of samples to normalise. (e.g.,"TEST","STANDARD CURVE"). It can also be a vector of sample types. In that case, dataframe with multiple sample types will be returned. By default equals to"ALL", which corresponds to processing all sample types.- blank_adjustment
(
logical(1), default =FALSE) Whether to apply blank adjustment before processing.- verbose
(
logical(1), default =TRUE) Whether to print additional information during execution.- reference_dilution
(
numeric(1)orcharacter(1), default =1/400) Target dilution used for nMFI calculation. Ignored for other types. Can be numeric (e.g.,0.0025) or string (e.g.,"1/400").- ...
Additional arguments passed to the model fitting function
create_standard_curve_model_analyte()andpredict.Model
Details
Supported normalisation types:
RAU (Relative Antibody Units): Requires model fitting. Produces estimates using a standard curve. See
create_standard_curve_model_analytefor details.nMFI (Normalised Median Fluorescence Intensity): Requires a reference dilution. See
get_nmfi.MFI (Blank-adjusted Median Fluorescence Intensity): Returns raw MFI values (adjusted for blanks, if requested).
RAU Workflow
Optionally perform blank adjustment.
Fit a model for each analyte using standard curve data.
Predict RAU values for test samples.
Aggregate and optionally save results.
nMFI Workflow
Optionally perform blank adjustment.
Compute normalised MFI using the
reference_dilution.Aggregate and optionally save results.
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 using default settings (RAU normalisation)
process_plate(plate, output_dir = example_dir)
#> Warning: The specified file /tmp/RtmpRMSIkp/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/RtmpRMSIkp/CovidOISExPONTENT_CO_reduced_RAU.csv'
#> Spike_6P_IPP ME_NA
#> B 1.851528 1.644344
#> 1/50 19996.648321 20000.000000
#> 1/100 9705.250659 9667.884259
#> 1/200 5678.426749 5426.947678
#> 1/400 2261.862587 2280.534363
#> 1/800 1330.755216 1317.032271
#> 1/1600 613.282524 613.240352
#> 1/3200 307.046789 321.809726
#> 1/6400 156.375332 154.951764
#> 1/12800 83.621645 63.012212
#> 1/25600 37.395122 57.745776
#> 1/102400 9.763882 9.141187
#> K086-LM 3292.261006 2128.609832
#> M254-VM 85.387525 5496.431779
#> M199-DS 2841.645046 7169.340839
#> M164-LM 125.426944 3560.958444
#> M265-MM 396.347695 3783.900374
#> K026-DJ 85.135062 1430.546071
#> K137-DT 3142.282230 6567.666626
#> M255-VA 38.783044 1103.720244
#> M258-PA 52.985918 2174.678628
#> M188-VC 1137.877275 1877.696204
#> M270-BF 58.801999 875.658712
#> M050-EL 100.643959 2168.088477
#> M088-GE 145.306377 2892.023989
#> M259-PM 1082.028926 8614.096696
#> K101-PA 73.845028 1777.766318
#> M189-VY 511.614786 2562.331565
#> K018-FC 882.552656 2250.682762
#> M142-RA 54.433682 966.198980
#> K100-CC 42.985631 984.937963
#> K107-RP 32.368412 1138.149335
#> M240-HS 31.465941 1028.676624
#> K019-FM 83.621645 1589.089997
#> M148-PS 61.978638 1216.497661
#> M089-HG 4677.618770 7712.754643
#> K136-DA 20000.000000 17238.789725
#> M241-HR 5532.929109 1279.295237
#> K020-NA 12680.550471 9631.381408
#> M239-HJ 40.410687 2654.197103
#> M092-LS 164.844759 3102.611181
#> M162-PE 4972.480781 5738.510120
#> M260-PM 20000.000000 2370.474570
#> K021-FS 28.782107 635.138793
#> M253-VM 80.350785 1235.324899
#> M198-CN 60.265975 2056.503923
#> M163-PD 3780.094882 6970.373031
#> M264-MA 20000.000000 2197.767771
#> K024-DT 981.887071 2867.689504
# Use a custom filename and skip blank adjustment
process_plate(plate,
filename = "no_blank.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/RtmpRMSIkp/no_blank.csv'
#> Spike_6P_IPP ME_NA
#> B 1.851528 1.644344
#> 1/50 19996.648321 20000.000000
#> 1/100 9705.250659 9667.884259
#> 1/200 5678.426749 5426.947678
#> 1/400 2261.862587 2280.534363
#> 1/800 1330.755216 1317.032271
#> 1/1600 613.282524 613.240352
#> 1/3200 307.046789 321.809726
#> 1/6400 156.375332 154.951764
#> 1/12800 83.621645 63.012212
#> 1/25600 37.395122 57.745776
#> 1/102400 9.763882 9.141187
#> K086-LM 3292.261006 2128.609832
#> M254-VM 85.387525 5496.431779
#> M199-DS 2841.645046 7169.340839
#> M164-LM 125.426944 3560.958444
#> M265-MM 396.347695 3783.900374
#> K026-DJ 85.135062 1430.546071
#> K137-DT 3142.282230 6567.666626
#> M255-VA 38.783044 1103.720244
#> M258-PA 52.985918 2174.678628
#> M188-VC 1137.877275 1877.696204
#> M270-BF 58.801999 875.658712
#> M050-EL 100.643959 2168.088477
#> M088-GE 145.306377 2892.023989
#> M259-PM 1082.028926 8614.096696
#> K101-PA 73.845028 1777.766318
#> M189-VY 511.614786 2562.331565
#> K018-FC 882.552656 2250.682762
#> M142-RA 54.433682 966.198980
#> K100-CC 42.985631 984.937963
#> K107-RP 32.368412 1138.149335
#> M240-HS 31.465941 1028.676624
#> K019-FM 83.621645 1589.089997
#> M148-PS 61.978638 1216.497661
#> M089-HG 4677.618770 7712.754643
#> K136-DA 20000.000000 17238.789725
#> M241-HR 5532.929109 1279.295237
#> K020-NA 12680.550471 9631.381408
#> M239-HJ 40.410687 2654.197103
#> M092-LS 164.844759 3102.611181
#> M162-PE 4972.480781 5738.510120
#> M260-PM 20000.000000 2370.474570
#> K021-FS 28.782107 635.138793
#> M253-VM 80.350785 1235.324899
#> M198-CN 60.265975 2056.503923
#> M163-PD 3780.094882 6970.373031
#> M264-MA 20000.000000 2197.767771
#> K024-DT 981.887071 2867.689504
# Use nMFI normalisation with reference dilution
process_plate(plate,
normalisation_type = "nMFI",
reference_dilution = "1/400",
output_dir = example_dir
)
#> Warning: The specified file /tmp/RtmpRMSIkp/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/RtmpRMSIkp/CovidOISExPONTENT_CO_reduced_nMFI.csv'
#> Spike_6P_IPP ME_NA
#> B 0.005413105 0.05662806
#> 1/50 3.624643875 4.73359073
#> 1/100 2.610683761 3.13770914
#> 1/200 1.918803419 2.07464607
#> 1/400 1.000000000 1.00000000
#> 1/800 0.648717949 0.61518662
#> 1/1600 0.329914530 0.32561133
#> 1/3200 0.177920228 0.20592021
#> 1/6400 0.098717949 0.13642214
#> 1/12800 0.058689459 0.09523810
#> 1/25600 0.031623932 0.09266409
#> 1/102400 0.012962963 0.06435006
#> K086-LM 1.327207977 0.94079794
#> M254-VM 0.059686610 2.09523810
#> M199-DS 1.190883191 2.55598456
#> M164-LM 0.081908832 1.47232947
#> M265-MM 0.223361823 1.54954955
#> K026-DJ 0.059544160 0.66151866
#> K137-DT 1.282905983 2.39768340
#> M255-VA 0.032478632 0.52767053
#> M258-PA 0.041025641 0.95881596
#> M188-VC 0.567663818 0.84169884
#> M270-BF 0.044444444 0.43371943
#> M050-EL 0.068233618 0.95624196
#> M088-GE 0.092735043 1.23166023
#> M259-PM 0.543589744 2.90604891
#> K101-PA 0.053133903 0.80180180
#> M189-VY 0.280626781 1.10810811
#> K018-FC 0.455270655 0.98841699
#> M142-RA 0.041880342 0.47104247
#> K100-CC 0.035042735 0.47876448
#> K107-RP 0.028490028 0.54182754
#> M240-HS 0.027920228 0.49678250
#> K019-FM 0.058689459 0.72586873
#> M148-PS 0.046296296 0.57400257
#> M089-HG 1.693447293 2.69240669
#> K136-DA 4.106552707 4.39124839
#> M241-HR 1.887749288 0.59974260
#> K020-NA 2.981481481 3.12998713
#> M239-HJ 0.033475783 1.14285714
#> M092-LS 0.103276353 1.30888031
#> M162-PE 1.762820513 2.16602317
#> M260-PM 4.175925926 1.03474903
#> K021-FS 0.026210826 0.33462033
#> M253-VM 0.056837607 0.58172458
#> M198-CN 0.045299145 0.91248391
#> M163-PD 1.464529915 2.50450450
#> M264-MA 5.674928775 0.96782497
#> K024-DT 0.499715100 1.22265122