# Basic R-Usage Guide for rMZQC

#### Chris Bielow chris.bielow@fu-berlin.de

#### 2025-07-15

# Basic R-Usage Guide for rMZQC

This vignette serves as a quickstart guide for R users to create and save an mzQC document.

Target Audience: R users

## Read an mzQC file and extract some data

    library(rmzqc)
    data = readMZQC(system.file("./testdata/test.mzQC", package = "rmzqc", mustWork = TRUE))
    cat("This file has ", length(data$runQualities), " runqualities\n")

    ## This file has  1  runqualities

    cat("  - file: ", data$runQualities[[1]]$metadata$inputFiles[[1]]$name, "\n")

    ##   - file:  special.raw

    cat("  - # of metrics: ", length(data$runQualities[[1]]$qualityMetrics), "\n")

    ##   - # of metrics:  1

    cat("    - metric #1 name: ", data$runQualities[[1]]$qualityMetrics[[1]]$name, "\n")

    ##     - metric #1 name:  number of MS1 spectra

    cat("    - metric #1 value: ", data$runQualities[[1]]$qualityMetrics[[1]]$value, "\n")

    ##     - metric #1 value:  13405

Hint: if you receive an error such as

    Error in parse_con(txt, bigint_as_char) :
    lexical error: invalid char in json text.
    cursor_int": [ NaN,NaN,NaN,NaN,825282.0,308263
    (right here) ------^

when calling`readMZQC` this indicates that the mzQC is not valid JSON, since `NaN` values should be quoted (`"NaN"`) or replaced by `null` (unquoted), depending on context. In short: `null` may become an `NA` in R if part of an array, see https://github.com/jeroen/jsonlite/issues/70#issuecomment-431433773.

## Create a minimal mzQC document

    library(rmzqc)
    ## we need a proper URI (i.e. no backslashes and a scheme, e.g. 'file:')
    ## otherwise writing will fail
    raw_file = localFileToURI("c:\\data\\special.raw", FALSE)

    file_format = getCVTemplate(accession = filenameToCV(raw_file))

    ## Downloading obo from 'https://github.com/HUPO-PSI/psi-ms-CV/releases/download/v4.1.195/psi-ms.obo' ...

    ptxqc_software = toAnalysisSoftware(id = "MS:1003162", version = "1.0.13") ## you could use 'version = packageVersion("PTXQC")' to automate further
    run1_qc = MzQCrunQuality$new(metadata = MzQCmetadata$new(label = raw_file,
                             inputFiles = list(MzQCinputFile$new(basename(raw_file),
                                                                 raw_file,
                                                                 file_format)),
                             analysisSoftware = list(ptxqc_software)),
                             qualityMetrics = list(toQCMetric(id = "MS:4000059", value = 13405), ## number of MS1 scans
                                                   toQCMetric(id = "MS:4000063", value = list("MS:1000041" = 1:3, "UO:0000191" = c(0.1, 0.8, 0.1))) # MS2 known precursor charges fractions
                                                   )
                             )

    mzQC_document = MzQCmzQC$new(version = "1.0.0",
                                 creationDate = MzQCDateTime$new(),
                                 contactName = Sys.info()["user"],
                                 contactAddress = "test@user.info",
                                 description = "A minimal mzQC test document with bogus data",
                                 runQualities = list(run1_qc),
                                 setQualities = list(),
                                 controlledVocabularies = list(getCVInfo()))

    ## write it out
    mzqc_filename = paste0(getwd(), "/test.mzQC")
    writeMZQC(mzqc_filename, mzQC_document)
    cat(mzqc_filename, "written to disk!\n")

    ## C:/Users/bielow/AppData/Local/Temp/RtmpwpycmK/Rbuildfa8823957157/rmzqc/vignettes/test.mzQC written to disk!

    ## read it again
    mq = readMZQC(mzqc_filename)

    ## print some basic stats
    gettextf("This mzQC was created on %s and has %d quality metric(s) in total.", dQuote(mq$creationDate$datetime), length(mq$runQualities) + length(mq$setQualities))

    ## [1] "This mzQC was created on \"2025-07-15T00:00:00Z\" and has 1 quality metric(s) in total."

## Different Value Types (single value, n-tuple, table, matrix) for QualityMetric’s

mzQC allows for 4 different kinds of value types for a `qualityMetric`: - Single value - n-tuple - table - matrix

See `toQCMetric()` for more examples.

Below we will create an example for each of them, so you know what R datastructures to employ. Using a data.frame, instead of a list, will give you the wrong JSON formatting and leads to validation failures.

### Single Value

A single value (MS:4000003) is the easiest. Just assign a string or a number to the value attribute:

    qualityMetric_singleValue = toQCMetric(id = "MS:4000059", value = 13405) ## number of MS1 scans

    ## let's look at how this looks in JSON
    jsonlite::toJSON(qualityMetric_singleValue, pretty = TRUE, auto_unbox = TRUE)

    ## {
    ##   "accession": "MS:4000059",
    ##   "name": "number of MS1 spectra",
    ##   "description": "\"The number of MS1 events in the run.\" [PSI:MS]",
    ##   "value": 13405
    ## }

### n-tuple

An n-tuple (MS:4000004) is just a vector in R:

    qualityMetric_tuple = toQCMetric(id = "MS:4000061", value = c(0.45, 0.76, 0.23)) ## MS1 density quantiles

    ## let's look at how this looks in JSON
    jsonlite::toJSON(qualityMetric_tuple, pretty = TRUE, auto_unbox = TRUE)

    ## {
    ##   "accession": "MS:4000061",
    ##   "name": "MS1 density quantiles",
    ##   "description": "\"The first to n-th quantile of MS1 peak density (scan peak counts). A value triplet represents the original QuaMeter metrics, the quartiles of MS1 density. The number of values in the tuple implies the quantile mode.\" [PSI:MS]",
    ##   "value": [0.45, 0.76, 0.23]
    ## }

### Table

An table (MS:4000005) is a list (not a data.frame!) of columns.

The example below shows a table with two columns and three rows:

    qualityMetric_table =   # MS2 known precursor charges fractions
                      toQCMetric(id = "MS:4000063", value = list("MS:1000041" = 1:3,   ## charge state
                                                                 "UO:0000191" = c(0.1, 0.8, 0.1)))   ## fraction of precursors with that charge

    ## let's look at how this looks in JSON
    jsonlite::toJSON(qualityMetric_table, pretty = TRUE, auto_unbox = TRUE)

    ## {
    ##   "accession": "MS:4000063",
    ##   "name": "MS2 known precursor charges fractions",
    ##   "description": "\"The fraction of MS/MS precursors of the corresponding charge. The fractions [0,1] are given in the 'Fraction' column, corresponding charges in the 'Charge state' column. The highest charge state is to be interpreted as that charge state or higher.\" [PSI:MS]",
    ##   "value": {
    ##     "MS:1000041": [1, 2, 3],
    ##     "UO:0000191": [0.1, 0.8, 0.1]
    ##   }
    ## }

### Matrix

A matrix (MS:4000006) is an R matrix:

At the point of writing, there is no matrix CV term yet. So we just make one up:

    qualityMetric_matrix = toQCMetric(id = "MS:40000??", value = matrix(1:9, 3, 3), allow_unknown_id = TRUE)

    ## Warning in CV$byID(id): Could not find id 'MS:40000??' in CV list (length:
    ## 6813)

    qualityMetric_matrix$name = "unknown metric"
    ## let's look at how this looks in JSON
    jsonlite::toJSON(qualityMetric_matrix, pretty = TRUE, auto_unbox = TRUE)

    ## {
    ##   "accession": "MS:40000??",
    ##   "name": "unknown metric",
    ##   "value": [
    ##     [1, 4, 7],
    ##     [2, 5, 8],
    ##     [3, 6, 9]
    ##   ]
    ## }