Preprocess Proteome Discoverer LFQ exports — preprocess

Converts a Proteome Discoverer LFQ protein export (wide TSV format) into the same output structure as preprocess_spectronaut() / preprocess_tmt(): three data.frames (metadata, protein_id, protein_quant) ready for the downstream pipeline (process_proteomics()).

The experimental design comes from the Abundance: <Condition>_<Replicate> column suffixes, exactly as in preprocess_tmt() and preprocess_diann(). For a report whose columns are not named that way, annot_path takes a sample sheet instead.

The intensities (Abundance: <sample>) and the three per-sample metric families (Score Mascot, # PSMs (by Search Engine), # Peptides (by Search Engine)) are mapped BY SAMPLE NAME, never by position: Proteome Discoverer writes the four families in different orders.

Usage

preprocess_lfq(
  file_path,
  annot_path = NULL,
  condition_order = NULL,
  export_dir = NULL,
  timestamp_suffix = TRUE,
  verbose = TRUE
)

Arguments

file_path: Path to the data TSV exported from Proteome Discoverer.
annot_path: Path to a sample sheet with columns Column and Condition, where Column holds the text after Abundance: . If NULL (default), the design comes from the suffixes. Any other column in the sheet is ignored – batch structure belongs in covariate_df of process_proteomics(), see vignette("batch-correction").
condition_order: Character vector with the order of the conditions. If NULL (default), it is derived from the data (order of appearance). If supplied, it fixes the factor levels and discards samples whose condition is not in the list; conditions listed but absent are dropped too, with a warning, so that no empty factor level reaches the metadata.
export_dir: Directory to export the TSVs to. NULL (default) = no export.
timestamp_suffix: Logical. If TRUE (default), appends a timestamp to the exported files.
verbose: Logical. If TRUE (default), shows progress messages.

Value

A list with class c("lfq_data", "proteomics_data", "list") containing metadata, protein_id and protein_quant.

The call that produced the object and a fingerprint of the files it was read from – the report, and the sample sheet when there is one – (path, size, modification time and MD5) travel with it as the attributes nadia_call and nadia_source. They are attributes rather than list elements so that the three-element structure above is unchanged; write_nadia() records them as the provenance of an analysis.

Examples

# A trimmed Proteome Discoverer LFQ report ships with the package. Its columns
# follow the "Abundance: <Condition>_<Replicate>" convention, so the design
# needs no further argument.
report <- system.file("extdata", "nadia_lfq_report.tsv.gz", package = "NADIA")

lfq <- preprocess_lfq(report, verbose = FALSE)
lfq
#> Preprocessed LFQ (Proteome Discoverer) data
#> ------------------------------------------
#> Samples (metadata): 12 
#> Proteins (ID): 2000 
#> Proteins (QUANT): 2000 
#> 
#> Conditions: A, B, D 
lfq$metadata[, c("Coding", "R.Condition", "R.Replicate")]
#>     Coding R.Condition R.Replicate
#> A_1    A_1           A           1
#> A_2    A_2           A           2
#> A_3    A_3           A           3
#> A_4    A_4           A           4
#> B_1    B_1           B           1
#> B_2    B_2           B           2
#> B_3    B_3           B           3
#> B_4    B_4           B           4
#> D_1    D_1           D           1
#> D_2    D_2           D           2
#> D_3    D_3           D           3
#> D_4    D_4           D           4

# The same design, declared in a sheet instead. Use this route when the
# columns are not named by the convention; here it is the same experiment, so
# it produces the same sample table.
annot <- system.file("extdata", "nadia_lfq_annotation.tsv", package = "NADIA")
from_sheet <- preprocess_lfq(report, annot_path = annot, verbose = FALSE)
identical(from_sheet$metadata, lfq$metadata)
#> [1] TRUE