Merge Normalised Data from Multiple Plates

This function merges normalised data from a list of Plate objects into a single data.frame. It supports different normalisation types and handles column mismatches based on the specified strategy.

Usage

merge_plate_outputs(
  plates,
  normalisation_type,
  column_collision_strategy = "intersection",
  verbose = TRUE,
  ...
)

Arguments

plates

A named list of Plate objects, typically returned by process_dir() with parameter return_plates = TRUE.

normalisation_type

(character(1)) The type of normalisation to merge. Options: "MFI", "RAU", "nMFI".

column_collision_strategy

(character(1), default = "intersection")

• Determines how to handle mismatched columns across plates.

• Options: "intersection" (only shared columns), "union" (include all columns).

verbose

(logical(1), default = TRUE) Whether to print verbose output.

...

Additional arguments passed to process_plate(), such as sample_type_filter = "TEST" to include only certain sample types in the merged result.

Value

A merged data.frame containing normalised data across all plates.

Examples

# creating temporary directory for the example
output_dir <- tempdir(check = TRUE)

dir_with_luminex_files <- system.file("extdata", "multiplate_reallife_reduced",
  package = "SerolyzeR", mustWork = TRUE
)
list_of_plates <- process_dir(dir_with_luminex_files,
  return_plates = TRUE, format = "xPONENT", output_dir = output_dir
)
#> Reading Luminex data from: /home/runner/work/_temp/Library/SerolyzeR/extdata/multiplate_reallife_reduced/IGG_CO_1_xponent.csv
#> using format xPONENT
#> Failed to extract from BatchMetadata: BatchStartTime not found in BatchMetadata.
#> Failed to extract from raw header: BatchStartTime not found in raw header.
#> Fallback datetime successfully extracted from ProgramMetadata.
#> Could not parse datetime string using default datetime format. Trying other possibilies.
#> Successfully parsed datetime string using order: mdY IM p
#> 
#> New plate object has been created with name: IGG_CO_1_xponent!
#> 
#> Processing plate 'IGG_CO_1_xponent'
#> Reading Luminex data from: /home/runner/work/_temp/Library/SerolyzeR/extdata/multiplate_reallife_reduced/IGG_CO_2_xponent.csv
#> using format xPONENT
#> Failed to extract from BatchMetadata: BatchStartTime not found in BatchMetadata.
#> BatchStartTime successfully extracted from the header.
#> Could not parse datetime string using default datetime format. Trying other possibilies.
#> Successfully parsed datetime string using order: mdY IMS p
#> 
#> New plate object has been created with name: IGG_CO_2_xponent!
#> 
#> Processing plate 'IGG_CO_2_xponent'
#> Reading Luminex data from: /home/runner/work/_temp/Library/SerolyzeR/extdata/multiplate_reallife_reduced/IGG_CO_3_xponent.csv
#> using format xPONENT
#> Failed to extract from BatchMetadata: BatchStartTime not found in BatchMetadata.
#> BatchStartTime successfully extracted from the header.
#> Could not parse datetime string using default datetime format. Trying other possibilies.
#> Successfully parsed datetime string using order: mdY IMS p
#> 
#> New plate object has been created with name: IGG_CO_3_xponent!
#> 
#> Processing plate 'IGG_CO_3_xponent'
#> Extracting the raw MFI to the output dataframe
#> Extracting the raw MFI to the output dataframe
#> Extracting the raw MFI to the output dataframe
#> Merged output saved to: /tmp/RtmpRMSIkp/merged_MFI_20250718_180154.csv
#> Fitting the models and predicting RAU for each analyte
#> Fitting the models and predicting RAU for each analyte
#> Fitting the models and predicting RAU for each analyte
#> Merged output saved to: /tmp/RtmpRMSIkp/merged_RAU_20250718_180154.csv
#> Computing nMFI values for each analyte
#> Computing nMFI values for each analyte
#> Computing nMFI values for each analyte
#> Merged output saved to: /tmp/RtmpRMSIkp/merged_nMFI_20250718_180154.csv

df <- merge_plate_outputs(list_of_plates, "RAU", sample_type_filter = c("TEST", "STANDARD CURVE"))
#> Fitting the models and predicting RAU for each analyte
#> Fitting the models and predicting RAU for each analyte
#> Fitting the models and predicting RAU for each analyte