1 Introduction

NADIA accepts five types of input: Spectronaut reports, DIA-NN protein-group matrices, Proteome Discoverer TMT and LFQ reports, and SummarizedExperiment objects prepared with other tools. Each has a dedicated importer: preprocess_spectronaut(), preprocess_diann(), preprocess_tmt(), preprocess_lfq() or preprocess_se(). All return the same proteomics_data structure, so the imported data can follow a common processing and analysis workflow.

This vignette applies each preprocessor to an example report and explains the main distinction among them: where NADIA obtains the experimental design. Correctly linking every measured column or run to its condition and replicate is essential, because that mapping determines the groups compared later in the analysis.

For example, preprocess_spectronaut() converts a long-format report—one row for each reported combination of LC-MS run and protein group—into an object of class proteomics_data.

The proteomics_data object separates the information contained in the source report into three coordinated tables:

metadata contains one row per sample or LC-MS run, including the sample identifier, experimental condition, replicate and any available run-level information.
protein_id contains identification-level information for each protein group, including protein and gene annotations and, when available, run-specific evidence such as peptide and precursor counts, sequence coverage and identification scores.
protein_quant contains the protein-level quantification data. It includes the abundance measured for each protein group in each sample, together with the protein annotations and quantitative evidence required by the downstream workflow.

In the Spectronaut example used below, metadata contains 12 samples, whereas protein_id and protein_quant each contain 2,000 protein groups.

The three tables have distinct but complementary roles. metadata defines the experimental design; protein_quant provides the abundance matrix used for normalization, imputation and differential abundance analysis; and protein_id retains the more detailed identification evidence used for inspection and reporting.

2 Installation

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("NADIA")

library(NADIA)

3 DIA: Spectronaut

Spectronaut export schema included with NADIA

NADIA provides a ready-to-use schema for generating a compatible Spectronaut report. Locate the installed file from R with:

system.file(“extdata”, “NADIA_Report.rs”, package = “NADIA”)

NADIA_Report.rs is a binary file interpreted by Spectronaut, not a text file that lists the exported columns. Import it directly into Spectronaut and select it when generating the report. The resulting table will contain the structure and columns required by preprocess_spectronaut().

Spectronaut exports a long report: one row per protein group per run, with the condition and replicate written in every row. Among the four input formats supported by NADIA, Spectronaut is the only one for which the experimental design does not need to be supplied separately. preprocess_spectronaut() extracts it directly from the R.Condition and R.Replicate columns in the report.

The arguments accepted by preprocess_spectronaut() are summarized below. The input report and the order of the conditions are required; the remaining arguments control optional export and the aggregation of run-level annotations.

Show the complete preprocess_spectronaut() call and default arguments

dia <- preprocess_spectronaut(
    # Required input and experimental conditions
    file_path = "spectronaut_report.tsv",
    condition_order = c("Control", "Treatment"),

    # Optional export; NULL keeps the result in memory
    export_dir = NULL,

    # Aggregation of annotations reported for multiple runs
    agg_coverage_run = "max",
    agg_coverage_global = "max",
    agg_mw = "max",
    agg_cscore_runwise = "mean",

    # Exported file names and progress messages
    timestamp_suffix = TRUE,
    verbose = TRUE
)

dia_file <- system.file("extdata", "nadia_dia_report.tsv.gz", package = "NADIA")
dia <- preprocess_spectronaut(dia_file, condition_order = c("A", "B", "D"),
                              verbose = FALSE)
dia
#> Preprocessed Spectronaut data
#> -----------------------------
#> Runs (metadata): 12 
#> Proteins (ID): 2000 
#> Proteins (QUANT): 2000 
#> 
#> Conditions: A, B, D

Because the design already comes from the report, condition_order does not infer or redefine the condition assigned to each run. Instead, it selects the conditions to retain—excluding any other conditions present in the report—and sets their order in the subsequent stages of the analysis, including downstream comparisons. It should therefore list every condition that is to be analyzed.

The following output shows an example of the metadata table extracted and constructed from the imported Spectronaut report:

dia$metadata
#>                                      R.FileName R.Condition R.Replicate Coding
#> A_1 20241014_XII_CursoProtQ_DIA_500ng_sample_A1           A           1    A_1
#> A_2 20241014_XII_CursoProtQ_DIA_500ng_sample_A2           A           2    A_2
#> A_3 20241014_XII_CursoProtQ_DIA_500ng_sample_A3           A           3    A_3
#> A_4 20241014_XII_CursoProtQ_DIA_500ng_sample_A4           A           4    A_4
#> B_1 20241014_XII_CursoProtQ_DIA_500ng_sample_B1           B           1    B_1
#> B_2 20241014_XII_CursoProtQ_DIA_500ng_sample_B2           B           2    B_2
#> B_3 20241014_XII_CursoProtQ_DIA_500ng_sample_B3           B           3    B_3
#> B_4 20241014_XII_CursoProtQ_DIA_500ng_sample_B4           B           4    B_4
#> D_1 20241014_XII_CursoProtQ_DIA_500ng_sample_D1           D           1    D_1
#> D_2 20241014_XII_CursoProtQ_DIA_500ng_sample_D2           D           2    D_2
#> D_3 20241014_XII_CursoProtQ_DIA_500ng_sample_D3           D           3    D_3
#> D_4 20241014_XII_CursoProtQ_DIA_500ng_sample_D4           D           4    D_4
#>     R.PrecursorsIdentified R.StrippedSequencesIdentified R.ProteinGroupsIdentified
#> A_1                 119383                         92079                      9999
#> A_2                 118762                         88994                      9992
#> A_3                 124304                         94053                     10101
#> A_4                 118937                         91948                     10037
#> B_1                 125709                         94781                     10213
#> B_2                 127547                         96821                     10282
#> B_3                 125104                         95927                     10233
#> B_4                 120909                         93568                     10160
#> D_1                 107757                         82180                      8597
#> D_2                 107470                         81586                      8601
#> D_3                 105982                         81075                      8612
#> D_4                 102539                         79005                      8535

Because the report is long and run-level, the same protein group may have multiple values for annotations such as sequence coverage, molecular weight and C-score. The agg_* arguments determine how those values are reduced when a single annotation value is required:

dia_median <- preprocess_spectronaut(
    dia_file, condition_order = c("A", "B", "D"),
    agg_coverage_run = "median",   # default "max"
    agg_cscore_runwise = "median", # default "mean"
    verbose = FALSE)

These arguments affect only the annotations stored alongside the quantitative data; they do not modify protein intensities.

4 DIA: DIA-NN

A DIA-NN protein-group matrix (report.pg_matrix.tsv) is wide: it contains one row per protein group and one intensity column per run. This differs from the long Spectronaut report and resembles the wide Proteome Discoverer exports described below, which is why it requires a dedicated importer.

The arguments accepted by preprocess_diann() are shown below. The report and the condition order are required. annot_path is optional and is used when the run columns do not follow the Abundance: <Condition>_<Replicate> convention.

Show the complete preprocess_diann() call and default arguments

diann <- preprocess_diann(
    # Required input and conditions to retain
    file_path = "report.pg_matrix.tsv",
    condition_order = c("Control", "Treatment"),

    # Optional sample annotation file; NULL uses the column suffixes
    annot_path = NULL,

    # Optional export; NULL keeps the result in memory
    export_dir = NULL,
    timestamp_suffix = TRUE,

    # Progress messages
    verbose = TRUE
)

diann_file <- system.file("extdata", "nadia_diann_report.tsv.gz",
                          package = "NADIA")
diann <- preprocess_diann(diann_file, condition_order = c("A", "B", "D"),
                          verbose = FALSE)
diann
#> Preprocessed DIA-NN data
#> ------------------------
#> Runs (metadata): 12 
#> Proteins (ID): 2000 
#> Proteins (QUANT): 2000 
#> 
#> Conditions: A, B, D

4.1 Declaring the experimental design

DIA-NN names each intensity column after the raw file it came from:

D:\DIA-NN\Projects\20241014_XII_CursoProtQ_DIA_500ng_sample_A1.raw

The text sample_A1 may suggest condition A and replicate 1 to a reader, but that interpretation depends on a laboratory-specific naming convention. A regular expression that worked for this report could silently misclassify the next one. NADIA therefore does not infer the design from raw-file names. Instead, it can be declared in either of two ways:

Rename the run columns using the Abundance: <Condition>_<Replicate> convention also used by Proteome Discoverer. The example distributed with NADIA follows this convention, so the call above needs no additional argument.
Supply an annotation file through annot_path and leave the report exactly as DIA-NN exported it. The Column field must contain the complete run headers, including the raw-file paths:

preprocess_diann(
    "report.pg_matrix.tsv",
    condition_order = c("A", "B", "D"),
    annot_path = "design.tsv")

design.tsv must be a tab-separated file with one row for each run that should be imported and two columns named exactly Column and Condition. Column contains the complete header of the corresponding intensity column in the DIA-NN matrix, while Condition assigns that run to an experimental group. For example:

Column                                                           Condition
D:\DIA-NN\Projects\experiment\sample_A1.raw                       A
D:\DIA-NN\Projects\experiment\sample_A2.raw                       A
D:\DIA-NN\Projects\experiment\sample_B1.raw                       B

The values in Column must match the report headers exactly, including the complete raw-file path when DIA-NN uses paths as column names. Runs omitted from the annotation file are not imported. condition_order then selects which of the annotated conditions are retained and sets their order in the subsequent analysis. If neither valid Abundance: <Condition>_<Replicate> headers nor an annotation file are supplied, the error message lists the headers that must be entered in Column.

Whichever route is used, NADIA constructs one row of metadata per retained run. The columns have the following meaning:

R.FileName preserves the complete intensity-column header read from the report.
R.Condition contains the experimental condition, obtained either from the renamed header or from Condition in the annotation file.
R.Replicate is read from a <Condition>_<Replicate> suffix when that naming convention is used. For original DIA-NN headers without such a suffix, runs are numbered sequentially within each condition.
Coding is the sample identifier used to name the quantitative columns. It is the <Condition>_<Replicate> suffix for renamed columns and the value of Column when annot_path is used.
R.ProteinGroupsIdentified reports how many protein groups have a non-missing intensity in that run.

The following output shows an example of the metadata table extracted and constructed from the imported DIA-NN report:

diann$metadata
#>         R.FileName R.Condition R.Replicate Coding R.ProteinGroupsIdentified
#> A_1 Abundance: A_1           A           1    A_1                      1854
#> A_2 Abundance: A_2           A           2    A_2                      1813
#> A_3 Abundance: A_3           A           3    A_3                      1860
#> A_4 Abundance: A_4           A           4    A_4                      1831
#> B_1 Abundance: B_1           B           1    B_1                      1865
#> B_2 Abundance: B_2           B           2    B_2                      1888
#> B_3 Abundance: B_3           B           3    B_3                      1856
#> B_4 Abundance: B_4           B           4    B_4                      1850
#> D_1 Abundance: D_1           D           1    D_1                      1550
#> D_2 Abundance: D_2           D           2    D_2                      1547
#> D_3 Abundance: D_3           D           3    D_3                      1548
#> D_4 Abundance: D_4           D           4    D_4                      1538

Note on identification information. The DIA-NN protein-group matrix contains little identification evidence. The two available fields are N.Sequences, which gives the total peptide-sequence count, and N.Proteotypic.Sequences, which gives the proteotypic peptide count. To preserve the common structure of the proteomics_data object, NADIA repeats these protein-level values across runs in protein_id, as it does for the global Proteome Discoverer metrics in TMT data. NADIA also calculates the number of protein groups with a reported intensity in each run and stores it as R.ProteinGroupsIdentified in the metadata table shown above.

5 TMT: Proteome Discoverer

A TMT report from Proteome Discoverer is wide: it contains one row per protein group and one abundance column per sample. As with DIA-NN, the experimental design must be declared either through column names that follow the Abundance: <Condition>_<Replicate> convention or through an annotation file supplied with annot_path.

The arguments accepted by preprocess_tmt() are listed below. The report and the conditions to retain are required. annot_path provides an alternative way to declare the design when the abundance suffixes do not follow the naming convention.

Show the complete preprocess_tmt() call and default arguments

tmt <- preprocess_tmt(
    # Required input and conditions to retain
    file_path = "tmt_report.tsv",
    condition_order = c("Control", "Treatment"),

    # Optional channel annotation file; NULL uses the column suffixes
    annot_path = NULL,

    # Optional export; NULL keeps the result in memory
    export_dir = NULL,
    timestamp_suffix = TRUE,

    # Progress messages
    verbose = TRUE
)

5.1 Declaring the experimental design

The design can be supplied in either of two ways:

Use informative abundance-column suffixes. Columns named Abundance: A_1, Abundance: A_2 and Abundance: B_1, for example, identify the condition and replicate directly. No annotation file is required.
Supply an annotation file. This is useful when the abundance columns retain identifiers such as the TMT tags 126, 127N or 127C. The annotation file assigns each identifier to its experimental condition without modifying the Proteome Discoverer report.

design.tsv must be a tab-separated file with the columns Column and Condition. For TMT data, Column contains the exact text that follows the Abundance: prefix in the report. For example:

Column    Condition
126       A
127N      A
127C      B
128N      B
128C      D
129N      D

Thus, an abundance column named Abundance: 127N is identified by the value 127N in Column. Identifiers omitted from the annotation file are not imported. condition_order subsequently selects the conditions to retain and sets their order in the downstream analysis. The annotation file is passed to preprocess_tmt() as follows:

preprocess_tmt(
    "tmt_report.tsv",
    condition_order = c("A", "B", "D"),
    annot_path = "design.tsv")

This example contains three biological conditions with eight replicates each, plus four IS channels that provide an internal standard across two TMT mixes. Replicates 1–4 belong to the first mix and replicates 5–8 to the second. Because condition_order also filters the parsed channels, omitting "IS" excludes the standards and prevents NADIA from treating them as a biological condition in downstream contrasts:

tmt_file <- system.file("extdata", "nadia_tmt_report.tsv.gz", package = "NADIA")
tmt <- preprocess_tmt(tmt_file, condition_order = c("A", "B", "D"),
                      verbose = FALSE)
tmt
#> Preprocessed TMT (Proteome Discoverer) data
#> ------------------------------------------
#> Channels (metadata): 24 
#> Proteins (ID): 2000 
#> Proteins (QUANT): 2000 
#> 
#> Conditions: A, B, D

The following output shows an example of the metadata table extracted and constructed from the imported TMT report. The condition counts confirm that the IS channels were excluded by condition_order:

head(tmt$metadata)
#>         R.FileName R.Condition R.Replicate Coding
#> A_1 Abundance: A_1           A           1    A_1
#> A_2 Abundance: A_2           A           2    A_2
#> A_3 Abundance: A_3           A           3    A_3
#> A_4 Abundance: A_4           A           4    A_4
#> A_5 Abundance: A_5           A           5    A_5
#> A_6 Abundance: A_6           A           6    A_6
table(tmt$metadata$R.Condition)
#> 
#> A B D 
#> 8 8 8

Note on identification information. TMT samples are digested and labeled individually, but they are then combined and analyzed as a multiplexed mixture. Consequently, the identification evidence reported by Proteome Discoverer is global to the multiplex rather than specific to each original sample. To preserve the common structure of the proteomics_data object, NADIA repeats global values such as the numbers of PSMs and identified peptides, sequence coverage and the PEP score across the samples in protein_id. These repeated values must not be interpreted as independent sample-level identifications. In contrast, the reporter-ion abundances are sample-specific and remain separate in protein_quant.

Any additional columns in design.tsv are ignored. Batch, TMT/LFQ experiment and other covariate information should instead be supplied through covariate_df in process_proteomics(), as explained in vignette("batch-correction").

6 Label-free: Proteome Discoverer

A label-free report from Proteome Discoverer is wide: it contains one row per protein group and one abundance column per sample. As with DIA-NN and TMT, the experimental design can be obtained from column names that follow the Abundance: <Condition>_<Replicate> convention or supplied separately through annot_path.

Unlike DIA-NN and TMT, but like Spectronaut, this report contains per-sample identification metrics. For every sample, Proteome Discoverer can report a Mascot score, a PSM count and a peptide count in addition to the abundance. NADIA matches these column families to the corresponding sample by name rather than by their position in the report. The resulting identification values therefore retain differences among samples in protein_id.

The arguments accepted by preprocess_lfq() are shown below. Only the report is required. Both annot_path and condition_order are optional; when omitted, the design and condition order are obtained from the abundance suffixes.

Show the complete preprocess_lfq() call and default arguments

lfq <- preprocess_lfq(
    # Required input
    file_path = "lfq_report.tsv",

    # Optional design file and condition filtering/order
    annot_path = NULL,
    condition_order = NULL,

    # Optional export; NULL keeps the result in memory
    export_dir = NULL,
    timestamp_suffix = TRUE,

    # Progress messages
    verbose = TRUE
)

6.1 Declaring the experimental design

The design can be supplied in either of two ways:

Use informative abundance-column suffixes. Columns named Abundance: A_1, Abundance: A_2 and Abundance: B_1, for example, identify the condition and replicate directly. This is the convention used by the example report distributed with NADIA.
Supply an annotation file. This route is used when the text after Abundance: identifies a sample but does not encode its experimental condition and replicate. The report itself does not need to be renamed.

design.tsv must be a tab-separated file with the columns Column and Condition. Column contains the exact sample identifier that follows the Abundance: prefix in the report. For example:

Column      Condition
Sample01    Control
Sample02    Control
Sample03    Treatment
Sample04    Treatment

Thus, the abundance column Abundance: Sample01 is identified by Sample01 in Column. Samples omitted from the annotation file are not imported. If condition_order is supplied, it additionally selects the conditions to retain and sets their order; otherwise, NADIA uses their order of appearance. The annotation file is passed to preprocess_lfq() as follows:

preprocess_lfq(
    "lfq_report.tsv",
    condition_order = c("Control", "Treatment"),
    annot_path = "design.tsv")

The example report distributed with NADIA contains three conditions with four replicates each. Its abundance-column suffixes already encode the complete design, so neither annot_path nor condition_order needs to be supplied:

lfq_file <- system.file("extdata", "nadia_lfq_report.tsv.gz", package = "NADIA")
lfq <- preprocess_lfq(lfq_file, verbose = FALSE)
lfq
#> Preprocessed LFQ (Proteome Discoverer) data
#> ------------------------------------------
#> Samples (metadata): 12 
#> Proteins (ID): 2000 
#> Proteins (QUANT): 2000 
#> 
#> Conditions: A, B, D

The following output shows an example of the metadata table extracted and constructed from the imported LFQ report:

lfq$metadata
#>         R.FileName R.Condition R.Replicate Coding
#> A_1 Abundance: A_1           A           1    A_1
#> A_2 Abundance: A_2           A           2    A_2
#> A_3 Abundance: A_3           A           3    A_3
#> A_4 Abundance: A_4           A           4    A_4
#> B_1 Abundance: B_1           B           1    B_1
#> B_2 Abundance: B_2           B           2    B_2
#> B_3 Abundance: B_3           B           3    B_3
#> B_4 Abundance: B_4           B           4    B_4
#> D_1 Abundance: D_1           D           1    D_1
#> D_2 Abundance: D_2           D           2    D_2
#> D_3 Abundance: D_3           D           3    D_3
#> D_4 Abundance: D_4           D           4    D_4

7 Bioconductor: SummarizedExperiment

Protein intensities prepared with other Bioconductor tools can be supplied in a SummarizedExperiment, for instance a protein-level assay of a QFeatures object. preprocess_se() converts one into the same proteomics_data object, and process_proteomics() can call this importer automatically when given a SummarizedExperiment. NADIA needs three things from the object:

the intensities in an assay, proteins in rows and samples in columns, linear by default or on the log2 scale with log2_input = TRUE;
unique row names, which become the protein identifiers;
the condition of each sample in colData()$Condition, with the replicate in colData()$Replicate or, without that column, numbered within each condition.

Use assay_name to select an assay when the object contains several; otherwise, the first assay is used.

Gene names and descriptions are read from rowData()$Genes and rowData()$Description when present. The samples are renamed <condition>_<replicate>, as the other readers do, and the original names are kept in metadata$R.FileName.

quant_cols <- grep("^PG.Quantity_", names(dia$protein_quant))
log2_intensities <- log2(as.matrix(dia$protein_quant[, quant_cols]))
rownames(log2_intensities) <- dia$protein_quant$PG.ProteinGroups

se <- SummarizedExperiment::SummarizedExperiment(
    assays  = list(log2 = log2_intensities),
    colData = data.frame(Condition = dia$metadata$R.Condition))

from_se <- preprocess_se(se, log2_input = TRUE, verbose = FALSE)
from_se
#> Protein intensities imported from a SummarizedExperiment
#> --------------------------------------------------------
#> Samples (metadata): 12 
#> Proteins: 2000 
#> 
#> Conditions: A, B, D
head(from_se$metadata[, c("R.FileName", "R.Condition", "R.Replicate", "Coding")])
#>          R.FileName R.Condition R.Replicate Coding
#> A_1 PG.Quantity_A_1           A           1    A_1
#> A_2 PG.Quantity_A_2           A           2    A_2
#> A_3 PG.Quantity_A_3           A           3    A_3
#> A_4 PG.Quantity_A_4           A           4    A_4
#> B_1 PG.Quantity_B_1           B           1    B_1
#> B_2 PG.Quantity_B_2           B           2    B_2

This importer does not transfer identification metrics from rowData(): the precursor counts of protein_id are left empty, and no peptide counts or scores are created, so the interactive tables show only the quantities. The quantitative analysis is unaffected.

8 Where the design comes from

All five importers produce a common object, but the location of the experimental design depends on the input. NADIA must know which condition and replicate correspond to each sample:

Input	Where the design is stored
Spectronaut	`R.Condition` and `R.Replicate` in the long report
DIA-NN	Run-column suffixes, or an annotation file supplied through `annot_path`
TMT (PD)	Abundance-column suffixes, or `annot_path`
LFQ (PD)	Abundance-column suffixes, or `annot_path`
`SummarizedExperiment`	`Condition` and, optionally, `Replicate` in `colData()`

Spectronaut carries the design within its report. For the three wide report formats, use the Abundance: <condition>_<replicate> naming convention or a two-column annotation file. DIA-NN matches the file’s Column values against complete run headers; the Proteome Discoverer importers match them against the text after Abundance:.

For a SummarizedExperiment, conditions come from colData()$Condition. Replicates come from colData()$Replicate when available; otherwise they are numbered within each condition. With preprocess_se(), condition_column and replicate_column allow these fields to have other names. No annotation file is needed.

condition_order selects the conditions to retain and determines their order in the analysis. It is required by the Spectronaut, DIA-NN and TMT importers, and optional for LFQ and preprocess_se(). LFQ defaults to order of appearance; preprocess_se() uses the condition factor’s levels, or order of appearance when the condition is not a factor.

9 Naming the conditions

Regardless of the input format, condition names become column names in the design matrix. The differential abundance model evaluates a contrast such as "B-A" as the expression B - A; it is not only a display label.

This determines which condition names are safe. For example, conditions named Tr-1 and Ct-1 would produce Tr-1-Ct-1. R would interpret the hyphens as several subtraction operators rather than as characters within two condition names, so the intended contrast could not be constructed.

The rule is simple: each condition must have a syntactically valid R name. In practice:

In the name	Verdict	Why
letters, digits (not first)	fine	`Ctrl`, `Treat24h`
`.` full stop	fine	`Ct.1` gives the contrast `Tr.1-Ct.1`
`_` underscore	fine	`Ct_a` gives `Tr_a-Ct_a`
`-` hyphen	rejected	collides with the contrast separator
space	rejected	not a syntactic name
leading digit	rejected	`1Ct` is not a name; R would rewrite it `X1Ct`

Names that follow the rows marked fine can be used safely and allow the pipeline to complete without naming-related errors. If there is any doubt, consult the table above or check the proposed names with make.names():

candidates <- c("Ctrl", "Ct.1", "Ct_a", "Ct-1", "Ct 1", "1Ct")
data.frame(name  = candidates,
           valid = make.names(candidates) == candidates,
           what_r_would_do = make.names(candidates))
#>   name valid what_r_would_do
#> 1 Ctrl  TRUE            Ctrl
#> 2 Ct.1  TRUE            Ct.1
#> 3 Ct_a  TRUE            Ct_a
#> 4 Ct-1 FALSE            Ct.1
#> 5 Ct 1 FALSE            Ct.1
#> 6  1Ct FALSE            X1Ct

The pipeline validates condition names before fitting the model and stops with an informative error if any are invalid. Sample identifiers must also remain consistent. A Column value in design.tsv that does not match the report produces an error, whereas samples omitted from the annotation file are excluded. If metadata$Coding is subsequently edited and no longer matches the corresponding PG.Quantity_<coding> column, the unmatched samples are removed from the analysis and the result contains fewer samples than expected.

In all three wide formats, the shared suffix parser reads the replicate from the trailing digits of <condition>_<replicate>. An underscore within the condition remains valid: Ct_a_1 is parsed as condition Ct_a, replicate 1. When annot_path is used, the labels in its Condition field define the experimental conditions. A condition label can therefore be corrected in design.tsv without renaming any abundance column in the quantitative report; the values in Column must still match the corresponding report identifiers.

10 Five sources, one common object

The five importers convert their inputs into a proteomics_data object. Each retains a source-specific class, while sharing the same three components: metadata, protein_id and protein_quant. The following comparison includes the SummarizedExperiment imported above:

inputs <- list(Spectronaut = dia, DIA_NN = diann, TMT = tmt, LFQ = lfq,
               SummarizedExperiment = from_se)
vapply(inputs, function(x) class(x)[1], character(1))
#>          Spectronaut               DIA_NN                  TMT                  LFQ 
#>   "spectronaut_data"         "diann_data"           "tmt_data"           "lfq_data" 
#> SummarizedExperiment 
#>            "se_data"
vapply(inputs, function(x) inherits(x, "proteomics_data"), logical(1))
#>          Spectronaut               DIA_NN                  TMT                  LFQ 
#>                 TRUE                 TRUE                 TRUE                 TRUE 
#> SummarizedExperiment 
#>                 TRUE

lapply(inputs, names)
#> $Spectronaut
#> [1] "metadata"      "protein_id"    "protein_quant"
#> 
#> $DIA_NN
#> [1] "metadata"      "protein_id"    "protein_quant"
#> 
#> $TMT
#> [1] "metadata"      "protein_id"    "protein_quant"
#> 
#> $LFQ
#> [1] "metadata"      "protein_id"    "protein_quant"
#> 
#> $SummarizedExperiment
#> [1] "metadata"      "protein_id"    "protein_quant"

10.1 Sample information

metadata links each sample to its condition, replicate and quantitative columns. Additional fields depend on the source: Spectronaut includes detailed run-level identification counts, whereas the other inputs may provide fewer annotations. The shared structure does not imply that every source supplies the same evidence:

lapply(inputs, function(x) colnames(x$metadata))
#> $Spectronaut
#> [1] "R.FileName"                    "R.Condition"                   "R.Replicate"                  
#> [4] "Coding"                        "R.PrecursorsIdentified"        "R.StrippedSequencesIdentified"
#> [7] "R.ProteinGroupsIdentified"    
#> 
#> $DIA_NN
#> [1] "R.FileName"                "R.Condition"               "R.Replicate"              
#> [4] "Coding"                    "R.ProteinGroupsIdentified"
#> 
#> $TMT
#> [1] "R.FileName"  "R.Condition" "R.Replicate" "Coding"     
#> 
#> $LFQ
#> [1] "R.FileName"  "R.Condition" "R.Replicate" "Coding"     
#> 
#> $SummarizedExperiment
#> [1] "R.FileName"                "R.Condition"               "R.Replicate"              
#> [4] "Coding"                    "R.ProteinGroupsIdentified"

10.2 Identification evidence

NADIA uses common column names for identification metrics. Most follow the Spectronaut convention, while scores use neutral names. The suffix * below identifies a sample; the global score is stored in protein_quant.

Metric	NADIA column
Precursor or PSM count	`PG.NrOfPrecursorsIdentified_*`
Sequence or peptide count	`PG.NrOfStrippedSequencesIdentified_*`
Sequence coverage	`PG.Coverage_*`
Sample-level score	`PG.Score.RunWise_*`
Global score	`PG.Score`

The meaning and availability of these fields depend on the input:

Spectronaut: precursor and stripped-sequence counts, coverage and PG.Cscore (Run-Wise) provide the sample-level metrics; PG.Cscore supplies the global score.
DIA-NN: N.Sequences supplies the sequence count. Precursor counts are empty; coverage and score columns are not created.
TMT (Proteome Discoverer): # PSMs, # Peptides, Coverage [%] and Sum PEP Score supply the metrics. These are protein-level totals repeated across channels, not independent identifications for each channel. Sum PEP Score also supplies the global score.
LFQ (Proteome Discoverer): the sample-specific # PSMs (by Search Engine), # Peptides (by Search Engine) and Score Mascot columns provide counts and scores. Protein-level Coverage [%] is repeated across samples, and Sum PEP Score supplies the global score.
SummarizedExperiment: the importer does not transfer identification metrics from rowData(). Precursor counts remain empty; peptide counts, coverage and scores are not created. Gene names and descriptions are read when the corresponding annotation fields are present.

A common column name does not make these metrics equivalent: PSM counts are not precursor counts, and scores from different engines do not share a scale. NADIA records the original score name in nadia_labels, which interactive tables use for their column labels:

lapply(inputs, function(x) attr(x$protein_quant, "nadia_labels"))
#> $Spectronaut
#> PG.Score 
#> "Cscore" 
#> 
#> $DIA_NN
#> NULL
#> 
#> $TMT
#>        PG.Score 
#> "Sum PEP Score" 
#> 
#> $LFQ
#>        PG.Score 
#> "Sum PEP Score" 
#> 
#> $SummarizedExperiment
#> NULL

10.3 Quantities and downstream analysis

For every input, each sample’s abundances are stored in a PG.Quantity_<Coding> column linked to metadata$Coding. This common mapping allows all five objects to enter the same processing workflow, even when their identification annotations differ:

processed <- lapply(inputs, process_proteomics, verbose = FALSE)
lapply(processed, function(x) x$comparisons)
#> $Spectronaut
#> [1] B-A D-A D-B
#> Levels: B-A D-A D-B
#> 
#> $DIA_NN
#> [1] B-A D-A D-B
#> Levels: B-A D-A D-B
#> 
#> $TMT
#> [1] B-A D-A D-B
#> Levels: B-A D-A D-B
#> 
#> $LFQ
#> [1] B-A D-A D-B
#> Levels: B-A D-A D-B
#> 
#> $SummarizedExperiment
#> [1] B-A D-A D-B
#> Levels: B-A D-A D-B

The same downstream functions perform normalization, imputation, differential abundance analysis and visualization. Their results depend on the supplied quantities and analysis settings, rather than on the availability of optional identification metrics. See vignette("results-and-export") for presentation and export of the results.

11 Session information

sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.5 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.24-bioc/R/lib/libRblas.so 
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_GB             
#>  [4] LC_COLLATE=C               LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
#> [10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: America/New_York
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#>  [1] SummarizedExperiment_1.43.0 Biobase_2.73.2              GenomicRanges_1.65.4       
#>  [4] Seqinfo_1.3.2               IRanges_2.47.5              S4Vectors_0.51.10          
#>  [7] BiocGenerics_0.59.13        generics_0.1.4              MatrixGenerics_1.25.0      
#> [10] matrixStats_1.5.0           NADIA_0.99.3                BiocStyle_2.41.0           
#> 
#> loaded via a namespace (and not attached):
#>   [1] RColorBrewer_1.1-3    jsonlite_2.0.0        shape_1.4.6.1         magrittr_2.0.5       
#>   [5] magick_2.9.1          farver_2.1.2          nloptr_2.2.1          rmarkdown_2.32       
#>   [9] GlobalOptions_0.1.4   vctrs_0.7.3           memoise_2.0.1         Cairo_1.7-0          
#>  [13] minqa_1.2.8           janitor_2.2.1         BiocBaseUtils_1.15.1  htmltools_0.5.9      
#>  [17] S4Arrays_1.13.2       curl_8.1.0            broom_1.0.13          pROC_1.19.1          
#>  [21] SparseArray_1.13.4    TTR_0.24.4            sass_0.4.10           bslib_0.12.0         
#>  [25] htmlwidgets_1.6.4     sandwich_3.1-3        impute_1.87.0         zoo_1.9-1            
#>  [29] lubridate_1.9.5       cachem_1.1.0          lifecycle_1.0.5       iterators_1.0.14     
#>  [33] pkgconfig_2.0.3       Matrix_1.7-6          R6_2.6.1              fastmap_1.2.0        
#>  [37] rbibutils_2.4.1       snakecase_0.11.1      clue_0.3-68           digest_0.6.39        
#>  [41] pcaMethods_2.5.0      colorspace_2.1-4      tidyHeatmap_1.13.1    AnnotationDbi_1.75.2 
#>  [45] patchwork_1.3.2       RSQLite_3.53.3        rrcovNA_0.5-3         vegan_2.7-6          
#>  [49] labeling_0.4.3        timechange_0.4.0      mgcv_1.9-5            httr_1.4.9           
#>  [53] abind_1.4-8           compiler_4.6.1        bit64_4.8.6           withr_3.0.3          
#>  [57] doParallel_1.0.17     S7_0.2.2              backports_1.5.1       BiocParallel_1.47.0  
#>  [61] DBI_1.3.0             viridis_0.6.5         dendextend_1.19.1     MASS_7.3-66          
#>  [65] DelayedArray_0.39.8   rjson_0.2.23          permute_0.10-0        tools_4.6.1          
#>  [69] rrcov_1.7-7           otel_0.3.0            quantmod_0.4.29       glue_1.8.1           
#>  [73] nlme_3.1-172          grid_4.6.1            cluster_2.1.8.3       sva_3.61.0           
#>  [77] gtable_0.3.6          tidyr_1.3.2           data.table_1.18.6.1   XVector_0.53.0       
#>  [81] foreach_1.5.2         pillar_1.11.1         stringr_1.6.0         limma_3.99.4         
#>  [85] genefilter_1.95.0     gmm_1.9-1             logging_0.10-111      robustbase_0.99-7    
#>  [89] circlize_0.4.18       splines_4.6.1         dplyr_1.2.1           lattice_0.23-1       
#>  [93] survival_3.8-12       bit_4.6.0             annotate_1.91.1       tidyselect_1.2.1     
#>  [97] ComplexHeatmap_2.29.0 Biostrings_2.81.9     knitr_1.52            BERT_1.9.0           
#> [101] reformulas_0.4.4      gridExtra_2.3.1       bookdown_0.48         imputeLCMD_2.1       
#> [105] edgeR_4.99.12         xfun_0.62             statmod_1.5.2         DEoptimR_1.2-2       
#> [109] stringi_1.8.9         yaml_2.3.12           boot_1.3-32           evaluate_1.0.5       
#> [113] codetools_0.2-20      MsCoreUtils_1.25.4    tibble_3.3.1          BiocManager_1.30.27  
#> [117] cli_3.6.6             xtable_1.8-8          Rdpack_2.6.6          jquerylib_0.1.4      
#> [121] dichromat_2.0-1       Rcpp_1.1.2            norm_1.0-11.1         png_0.1-9            
#> [125] XML_3.99-0.25         parallel_4.6.1        blob_1.3.0            ggplot2_4.0.3        
#> [129] assertthat_0.2.1      lme4_2.1-0            viridisLite_0.4.3     mvtnorm_1.4-2        
#> [133] rlist_0.4.6.2         tmvtnorm_1.7          scales_1.4.0          xts_0.14.3           
#> [137] pcaPP_2.0-7           purrr_1.2.2           highcharter_0.9.5     crayon_1.5.3         
#> [141] GetoptLong_1.1.1      rlang_1.3.0           KEGGREST_1.53.6

Input formats: reports and SummarizedExperiment objects

12 November 2026

Package