R/functions-TopDownSet.R

In topdownr: Investigation of Fragmentation Conditions in Top-Down Proteomics

#' Read top-down files.
#'
#' It creates an [TopDownSet-class] object and is its only constructor.
#'
#' @details
#'
#' `readTopDownFiles` reads and processes all top-down files, namely:
#' * `.fasta` (protein sequence)
#' * `.mzML` (spectra)
#' * `.experiments.csv` (method/fragmentation conditions)
#' * `.txt` (scan header information)
#'
#' `adducts`: *Thermo's Xtract*
#' allows some mistakes in deisotoping, mostly it
#' allows `+/- C13-C12` and `+/- H+`.
#' The `adducts` argument takes a
#' `data.frame` with the `mass` to add, the `name`
#' that should assign to these
#' new fragments and an information `to`
#' whom the modification should be
#' applied, e.g. for `H+` on `z`,
#' `data.frame(mass=1.008, name="zpH", to="z")`.
#'
#' *Please note:* The `adducts` are added to the output of
#' [`MSnbase::calculateFragments()`][MSnbase::calculateFragments-methods].
#' That has some limitations, e.g.
#' neutral loss calculation could not be done in
#' [topdownr-package].
#' If neutral loss should be applied on adducts you have to create
#' additional rows, e.g.:
#' `data.frame(mass=c(1.008, 1.008), name=c("cpH", "cpH_"), to=c("c", "c_"))`.
#'
#' @param path `character`,
#' path to directory that contains the top-down files.
#' @param pattern `character`,
#' a filename pattern, the default `.*` means all files.
#' @param type `character`,
#' type of fragments, currently *a-c* and *x-z* are
#' supported, see
#' [`MSnbase::calculateFragments()`][MSnbase::calculateFragments-methods]
#' for details.
#' @param modifications `character`,
#' unimod names of modifications that should be applied.
#' Currenlty just *Acetyl* (Unimod:1 but just protein N-term),
#' *Carbamidomethyl* (Unimod:4) and
#' *Met-loss* (Unimod:765) are supported.
#' *Met-loss* removes M
#' (if followed by A, C, G, P, S, T, or V;
#' (see also
#' http://www.unimod.org/modifications_view.php?editid1=1,
#' http://www.unimod.org/modifications_view.php?editid1=4, and
#' http://www.unimod.org/modifications_view.php?editid1=765 for details)).
#' Use `NULL` to disable all modifications.
#' @param adducts `data.frame`,
#' with 3 columns, namely: mass, name, to, see details section.
#' @param neutralLoss `list`,
#' neutral loss that should be applied, see
#' [`MSnbase::calculateFragments()`][MSnbase::calculateFragments-methods] and
#' [`MSnbase::defaultNeutralLoss()`][MSnbase::calculateFragments-methods]
#' for details.
#' @param sequenceOrder `character`,
#' order of the sequence before fragment calculation and matching is done.
#' `"original"` doesn't change anything.
#' `"inverse"` reverse the sequence and
#' `"random"` arranges the amino acid sequence at random.
#' @param tolerance `double`,
#' tolerance in *ppm* that is used to match the
#' theoretical fragments with the observed ones.
#' @param redundantIonMatch `character`, a mz could be matched to one, two or
#' more fragments. If it is matched against more than one fragment the match
#' could be `"remove"`d or the match to the `"closest"` fragment could be
#' chosen.
#' @param redundantFragmentMatch `character`, one or more mz could be matched to
#' the same fragment, these matches could be `"remove"`d or the match to the
#' `"closest"` mz is chosen.
#' @param dropNonInformativeColumns logical,
#' should columns with just one identical value across all runs be removed?
#' @param sampleColumns `character`,
#' column names of the [colData()]
#' used to define a sample (technical replicate). This is used to add the
#' `Sample` column (used for easier aggregation, etc.).
#' @param conditions `character`/`numeric`, one of:
#'  - `"ScanDescription"` (default): create condition IDs based on the given
#'    "Scan Description" parameter (set automatically by
#'    [createExperimentsFragmentOptimisation()]).
#'  - `"FilterString"`: create condition IDs based on mass labels in
#'    the *FilterString* column (included for backward-compatibilty, used
#'    in [writeMethodXmls()] prior version 1.5.2 in Dec 2018).
#'  - A single `numeric` value giving the number of conditions.
#' @param verbose `logical`, verbose output?
#' @return A `TopDownSet` object.
#' @export
#' @seealso [`MSnbase::calculateFragments()`][MSnbase::calculateFragments-methods],
#' [`MSnbase::defaultNeutralLoss()`][MSnbase::calculateFragments-methods]
#' @examples
#' if (require("topdownrdata")) {
#'     # add H+ to z and no neutral loss of water
#'     tds <- readTopDownFiles(
#'         topdownrdata::topDownDataPath("myoglobin"),
#'         ## Use an artifical pattern to load just the fasta
#'         ## file and files from m/z == 1211, ETD reagent
#'         ## target 1e6 and first replicate to keep runtime
#'         ## of the example short
#'         pattern=".*fasta.gz$|1211_.*1e6_1",
#'         adducts=data.frame(mass=1.008, name="zpH", to="z"),
#'         neutralLoss=MSnbase::defaultNeutralLoss(
#'             disableWaterLoss=c("Cterm", "D", "E", "S", "T")),
#'         tolerance=25e-6
#'    )
#' }
readTopDownFiles <- function(path, pattern=".*",
                             type=c("a", "b", "c", "x", "y", "z"),
                             modifications=c("Carbamidomethyl",
                                             "Acetyl", "Met-loss"),
                             adducts=data.frame(),
                             neutralLoss=MSnbase::defaultNeutralLoss(),
                             sequenceOrder=c("original", "random", "inverse"),
                             tolerance=5e-6,
                             redundantIonMatch=c("remove", "closest"),
                             redundantFragmentMatch=c("remove", "closest"),
                             dropNonInformativeColumns=TRUE,
                             sampleColumns=c("Mz", "AgcTarget",
                                             "EtdReagentTarget",
                                             "EtdActivation",
                                             "CidActivation",
                                             "HcdActivation",
                                             "UvpdActivation"),
                             conditions="ScanDescription",
                             verbose=interactive()) {


    redundantIonMatch <- match.arg(redundantIonMatch)
    redundantFragmentMatch <- match.arg(redundantFragmentMatch)

    files <- .listTopDownFiles(path, pattern=pattern)

    sequence <- .readFasta(files$fasta, verbose=verbose)

    fragmentViews <- .calculateFragments(
        sequence=sequence,
        type=type,
        modifications=modifications,
        neutralLoss=neutralLoss,
        adducts=adducts,
        sequenceOrder=sequenceOrder,
        verbose=verbose
    )

    scanConditions <- .rbind(
        lapply(
            files$txt,
            .readScanHeadsTable,
            conditions=conditions,
            verbose=verbose
        )
    )

    scanConditions$Scan <- unlist(mapply(
        .rtToScanId,
        file=files$mzML,
        rt=split(scanConditions$RtMin, scanConditions$File),
        MoreArgs=list(
            verbose=verbose
        ),
        SIMPLIFY=FALSE, USE.NAMES=FALSE
    ))

    headerInformation <- .rbind(
        lapply(files$csv, .readExperimentCsv, verbose=verbose)
    )

    mzml <- mapply(
        .readMzMl,
        file=files$mzML,
        scans=split(scanConditions$Scan, scanConditions$File),
        MoreArgs=list(
            fmass=mz(fragmentViews),
            tolerance=tolerance,
            redundantIonMatch=redundantIonMatch,
            redundantFragmentMatch=redundantFragmentMatch,
            verbose=verbose
        ),
        SIMPLIFY=FALSE, USE.NAMES=FALSE
    )

    mzmlHeader <- do.call(rbind, lapply(mzml, "[[", "hd"))

    scanHeadsman <- .mergeScanConditionAndHeaderInformation(
        scanConditions, headerInformation
    )

    header <- .mergeSpectraAndHeaderInformation(mzmlHeader, scanHeadsman)

    if (dropNonInformativeColumns) {
        header <- .dropNonInformativeColumns(
            header,
            keep=c(
                "File", "Scan", "SpectrumIndex", "Activation", "Mz", "AgcTarget"
            )
        )
    }

    header$Charge <- round(fragmentViews@metadata$mass / header$Mz)

    assay <- do.call(cbind, lapply(mzml, "[[", "m"))
    dimnames(assay) <- list(names(fragmentViews), rownames(header))

    tds <- new(
        "TopDownSet",
        rowViews=fragmentViews,
        colData=.colsToRle(.colsToLogical(as(header, "DataFrame"))),
        assay=assay,
        files=unlist(unname(files)),
        tolerance=tolerance,
        redundantMatching=c(
            ion=redundantIonMatch,
            fragment=redundantFragmentMatch
        )
    )
    tds <- .atdsLogMsg(
        tds, .logdim(tds), " matched (tolerance: ",
        round(tolerance / 1e-6, 1L), " ppm, strategies ion/fragment: ",
        redundantIonMatch, "/", redundantFragmentMatch, ").", addDim=FALSE
    )
    tds <- updateConditionNames(tds, sampleColumns=sampleColumns, verbose=FALSE)
    updateMedianInjectionTime(tds)
}

#' Turn condition into a data.frame
#'
#' @param object `TopDownSet`
#' @return `data.frame`
#' @noRd
.condition2data.frame <- function(object) {
    .isTopDownSet(object)

    if (ncol(object) != 1L) {
        stop("Converting more than one condition is currently not implemented.")
    }

    s <- .readSpectrum(
        object@files[
            .subsetFiles(basename(object@files), object@colData$File[1L]) &
            grepl(.topDownFileExtRx("mzml"), object@files)
        ],
        object$SpectrumIndex[1L]
    )
    nr <- nrow(s)
    fragment <- character(nr)
    type <- rep.int("None", nr)
    fv <- object@rowViews[object@assay[, 1L] > 0L]

    k <- .matchFragments(
        s[, 1L],
        mz(fv),
        tolerance=object@tolerance,
        redundantIonMatch=object@redundantMatching[1L],
        redundantFragmentMatch=object@redundantMatching[2L]
    )

    notNA <- !is.na(k)
    if (sum(notNA)) {
        fragment[notNA] <- names(fv)[k[notNA]]
        type[notNA] <- ifelse(
            elementMetadata(fv)$type[k[notNA]] %in% c("a", "b", "c"),
            "N-terminal", "C-terminal"
        )
    }
    data.frame(
        mz=s[, 1L],
        intensity=s[, 2L],
        fragment=fragment,
        type=factor(
            type,
            levels=c("None", "N-terminal", "C-terminal", "Bidirectional"),
            ordered=TRUE
        ),
        stringsAsFactors=FALSE
    )
}

#' Test for TopDownSet class
#'
#' @param object object to test
#' @return `TRUE` if object is a TopDownSet otherwise fails with an error
#' @noRd
.isTopDownSet <- function(object) {
    if (!isTRUE(is(object, "TopDownSet"))) {
        stop("'object' has to be an 'TopDownSet' object.")
    }
    TRUE
}

#' Create NCB Map (N-/C-terminal, or Bidirectional)
#'
#' @param object `TopDownSet`
#' @return `Matrix`, Nterm == 1, Cterm == 2, bidirectional == 3
#' @noRd
.ncbMap <- function(object, nterm=c("a", "b", "c"), cterm=c("x", "y", "z")) {
    .isTopDownSet(object)

    w <- width(object@rowViews)
    mn <- mc <- object@assay
    selN <- fragmentType(object) %in% nterm
    selC <- fragmentType(object) %in% cterm
    mn[!selN, ] <- 0L
    mn <- drop0(mn)
    mc[!selC, ] <- 0L
    mc <- drop0(mc)

    mn <- as(.colSumsGroup(mn, w) > 0L, "dgCMatrix")
    mc <- as(.colSumsGroup(mc, max(w) + 1L - w) > 0L, "dgCMatrix")
    mc@x[] <- 2L
    m <- drop0(mn + mc)
    colnames(m) <- colnames(object)
    rownames(m) <- paste0(
        "bond", .formatNumbers(seq_len(nrow(m)), asInteger=TRUE)
    )
    m
}

#' Plot a specific condition of an TopDownSet object
#'
#' @param x `TopDownSet`
#' @noRd
.plot <- function(x) {
    .isTopDownSet(x)

    d <- .condition2data.frame(x)
    ggplot(
        data=d,
        aes_string(x="mz", y="intensity", fragment="fragment", color="type")
    ) +
    geom_segment(aes_string(xend="mz", yend=0L)) +
    geom_text(
        aes_string(label="fragment"),
        angle=90L,
        hjust=0.2,
        vjust=0.5,
        nudge_y=max(d$intensity) / 100L
    ) +
    scale_color_manual(
        name="Observed Fragments",
        labels=c("none", "N-terminal", "C-terminal", "Bidirectional"),
        values=c("#000000", "#1b9e77", "#d95f02", "#7570b3"),
        guide=FALSE
    ) +
    theme_classic() +
    theme(
        plot.title=element_text(hjust=0.5, face="bold"),
        legend.position="none",
    ) +
    labs(title=colnames(x)[1L], x="m/z", y="intensity")
}