msQC: MsQuality - Quality metric calculation from Spectra and MsExperiment objects

Documented in areaUnderTic areaUnderTicRtQuantiles chromatographyDuration extentIdentifiedPrecursorIntensity meanCharge medianCharge medianPrecursorMz medianTicOfRtRange medianTicRtIqr msSignal10xChange mzAcquisitionRange numberEmptyScans numberSpectra precursorIntensityMean precursorIntensityQuartiles precursorIntensityRange precursorIntensitySd ratioCharge1over2 ratioCharge3over2 ratioCharge4over2 rtAcquisitionRange rtIqr rtIqrRate .rtOrderSpectra rtOverMsQuarters ticQuartersRtFraction ticQuartileToQuartileLogRatio

#' @name chromatographyDuration
#' 
#' @title chromatography duration (MS:4000053)
#' 
#' @description
#' MS:4000053
#' "The retention time duration of the chromatography in seconds." [PSI:MS] \cr 
#' 
#' The metric is calculated as follows: \cr 
#' (1) the retention time associated to the \code{Spectra} object is obtained, \cr  
#' (2) the maximum and the minimum of the retention time is obtained, \cr  
#' (3) the difference between the maximum and the minimum is calculated and 
#' returned. \cr 
#' 
#' @details
#' MS:4000053
#' synonym: "RT-Duration" RELATED [PMID:24494671] \cr
#' is_a: MS:4000003 ! single value \cr
#' relationship: has_metric_category MS:4000009 ! ID free metric \cr
#' relationship: has_metric_category MS:4000012 ! single run based metric \cr
#' relationship: has_metric_category MS:4000016 ! retention time metric \cr
#' relationship: has_value_type xsd:float ! The allowed value-type for this CV term \cr
#' relationship: has_value_concept NCIT:C25330 ! Duration \cr
#' relationship: has_units UO:0000010 ! second \cr
#' 
#' Retention time values that are \code{NA} are removed.
#' 
#' @param spectra \code{Spectra} object
#' 
#' @param ... not used here
#' 
#' @return \code{numeric(1)}
#' 
#' @author Thomas Naake
#' 
#' @export
#' 
#' @importMethodsFrom Spectra rtime
#' 
#' @examples
#' library(S4Vectors)
#' library(Spectra)
#' 
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
#' spd$rtime <- c(9.44, 9.44, 15.84)
#' sps <- Spectra(spd)
#' chromatographyDuration(spectra = sps)
chromatographyDuration <- function(spectra, ...) {  
    RT <- rtime(object = spectra)
    res <- max(RT, na.rm = TRUE) - min(RT, na.rm = TRUE)
    
    ## add attributes and return
    attributes(res) <- list(chromatographyDuration = "MS:4000053")
    res
}

#' @name ticQuartersRtFraction
#' 
#' @title TIC quarters RT fraction (MS:4000054)
#' 
#' @description
#' MS:4000054
#' "The interval when the respective quarter of the TIC accumulates divided by 
#' retention time duration." [PSI:MS] \cr
#' 
#' The metric is calculated as follows: \cr
#' (1) the \code{Spectra} object is ordered according to the retention time, \cr 
#' (2) the cumulative sum of the ion count is calculated (TIC), \cr 
#' (3) the quantiles are calculated according to the \code{probs} argument, e.g.
#' when \code{probs} is set to \code{c(0, 0.25, 0.5, 0.75, 1)} the 0\%, 25\%, 
#' 50\%, 75\%, and 100\% quantile is calculated, \cr 
#' (4) the retention time/relative retention time (retention time divided by 
#' the total run time taking into account the minimum retention time) is 
#' calculated, \cr 
#' (5) the (relative) duration of the LC run after which the cumulative
#' TIC exceeds (for the first time) the respective quantile of the
#' cumulative TIC is calculated and returned. \cr
#' 
#' @details
#' MS:4000054
#' synonym: "RT-TIC-Q1" RELATED [PMID:24494671] \cr
#' synonym: "RT-TIC-Q2" RELATED [PMID:24494671] \cr
#' synonym: "RT-TIC-Q3" RELATED [PMID:24494671] \cr
#' synonym: "RT-TIC-Q4" RELATED [PMID:24494671] \cr
#' is_a: MS:4000004 ! n-tuple \cr
#' relationship: has_metric_category MS:4000009 ! ID free metric \cr
#' relationship: has_metric_category MS:4000012 ! single run based metric \cr
#' relationship: has_metric_category MS:4000016 ! retention time metric \cr
#' relationship: has_metric_category MS:4000017 ! chromatogram metric \cr
#' relationship: has_value_type xsd:float ! The allowed value-type for this CV term \cr
#' relationship: has_units UO:0000191 ! fraction \cr
#' 
#' @param spectra \code{Spectra} object
#' @param probs \code{numeric} defining the quantiles. See \code{probs = seq(0, 1, 0.25)}.
#' @param msLevel \code{integer}
#' @param relative \code{logical}, if set to \code{TRUE} the relative retention time 
#' will be returned instead of the abolute retention time
#' @param ... not used here
#' 
#' @return \code{numeric} of length equal to length \code{probs} with the relative
#'    duration (duration divided by the total run time) after which the TIC
#'    exceeds the respective quantile of the TIC.
#' 
#' @author Thomas Naake, Johannes Rainer
#' 
#' @export 
#' 
#' @importMethodsFrom Spectra ionCount filterMsLevel
#'
#' @importFrom stats quantile
#' 
#' @examples
#' library(S4Vectors)
#' library(Spectra)
#' 
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
#' spd$rtime <- c(9.44, 9.44, 15.84)
#' sps <- Spectra(spd)
#' ticQuartersRtFraction(spectra = sps, msLevel = 2L)
ticQuartersRtFraction <- function(spectra, probs = seq(0, 1, 0.25),
    msLevel = 1L, relative = TRUE, ...) {
    
    ## truncate spectra based on the MS level
    spectra <- filterMsLevel(object = spectra, msLevel)
    
    ## order spectra according to increasing retention time
    spectra <- .rtOrderSpectra(spectra)
    RT <- rtime(spectra)

    ## what is the (relative) duration of the LC run after which the cumulative
    ## TIC exceeds (for the first time) the respective quantile of the
    ## cumulative TIC? The "accumulates" is interpreted as the 
    ## "sum of the TICs of all previous spectra".
    ## probs * max(TIC) calculates the portion of the total TIC at the probs,
    ## e.g. if sum of TIC is 10000 and probs = seq(0, 1, 0.25), ids will be 
    ## the indices where the TIC is first higher than 0, 2500, 5000, 7500, 
    ## and 10000
    TIC <- cumsum(ionCount(spectra))
    idxs <- lapply(probs * max(TIC), function(z) which(TIC >= z)[1]) |>
        unlist()
    
    if (relative) {
        rtMin <- min(RT)
        duration <- chromatographyDuration(spectra)
        res <- (RT[idxs] - rtMin) / duration  
    } else {
        res <- RT[idxs]
    }
    
    ## add attributes and return
    attributes(res) <- list(names = paste0(probs * 100, "%"), 
        ticQuartersRtFraction = "MS:4000054")
    res
}

#' @title Order Spectra according to increasing retention time
#' 
#' @description 
#' The function \code{.rtOrderSpectra} orders the features in a \code{Spectra} object
#' according to the (increasing) retention time values. 
#' 
#' @details
#' Internal function in quality metric functions.
#' 
#' @param spectra \code{Spectra} object
#' 
#' @return \code{Spectra} object with the features ordered according to the 
#' (increasing) retention time
#' 
#' @author Johannes Rainer
#' 
#' @importFrom ProtGenerics rtime
#' 
#' @examples
#' library(S4Vectors)
#' library(Spectra)
#' 
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0001847", "HMDB0000001", "HMDB0000001"),
#'     name = c("Caffeine", "1-Methylhistidine", "1-Methylhistidine"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876),
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16))
#' spd$intensity <- list(
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994),
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643))
#' spd$rtime <- c(15.84, 9.44, 9.44)
#' sps <- Spectra(spd)
#' MsQuality:::.rtOrderSpectra(sps)
.rtOrderSpectra <- function(spectra) {
    RT <- rtime(spectra)
    if (any(is.na(RT)))
        warning("missing retention time values. Will keep original ",
                "ordering of spectra.")
    else if (is.unsorted(RT))
        spectra <- spectra[order(RT)]
    spectra
}

#' @name rtOverMsQuarters
#' 
#' @title MS1 quarter RT fraction (MS:4000055) or 
#' MS2 quarter RT fraction (MS:4000056)
#' 
#' @description
#' MS:4000055 \cr
#' "The interval used for acquisition of the first, second, third, and fourth 
#' quarter of all MS1 events divided by retention time duration." [PSI:MS] \cr
#' 
#' MS:4000056 \cr
#' "The interval used for acquisition of the first, second, third, and fourth 
#' quarter of all MS2 events divided by retention time duration." [PSI:MS] \cr
#' 
#' The metric is calculated as follows: \cr
#' (1) the retention time duration of the whole \code{Spectra} object is determined
#' (taking into account all the MS levels), \cr 
#' (2) the \code{Spectra} object is filtered according to the MS level and 
#' subsequently ordered according to the retention time \cr 
#' (3) the MS events are split into four (approximately) equal parts, \cr 
#' (4) the relative retention time is calculated (using the retention time 
#' duration from (1) and taking into account the minimum retention time), \cr 
#' (5) the relative retention time values associated to the MS event parts
#' are returned.
#' 
#' @details
#' MS:4000055 \cr
#' synonym: "RT-MS-Q1" RELATED [PMID:24494671] \cr
#' synonym: "RT-MS-Q2" RELATED [PMID:24494671] \cr
#' synonym: "RT-MS-Q3" RELATED [PMID:24494671] \cr
#' synonym: "RT-MS-Q4" RELATED [PMID:24494671] \cr
#' is_a: MS:4000004 ! n-tuple \cr 
#' relationship: has_metric_category MS:4000009 ! ID free metric \cr
#' relationship: has_metric_category MS:4000012 ! single run based metric \cr
#' relationship: has_metric_category MS:4000016 ! retention time metric \cr
#' relationship: has_metric_category MS:4000021 ! MS1 metric \cr
#' relationship: has_value_type xsd:float ! The allowed value-type for this CV term \cr
#' relationship: has_units UO:0000191 ! fraction \cr
#'
#' MS:4000056 \cr
#' synonym: "RT-MSMS-Q1" RELATED [PMID:24494671] \cr
#' synonym: "RT-MSMS-Q2" RELATED [PMID:24494671] \cr
#' synonym: "RT-MSMS-Q3" RELATED [PMID:24494671] \cr
#' synonym: "RT-MSMS-Q4" RELATED [PMID:24494671] \cr
#' is_a: MS:4000004 ! n-tuple \cr
#' relationship: has_metric_category MS:4000009 ! ID free metric \cr
#' relationship: has_metric_category MS:4000012 ! single run based metric \cr
#' relationship: has_metric_category MS:4000016 ! retention time metric \cr
#' relationship: has_metric_category MS:4000022 ! MS2 metric \cr
#' relationship: has_value_type xsd:float ! The allowed value-type for this CV term \cr
#' relationship: has_units UO:0000191 ! fraction \cr
#' 
#' The function returns \code{c(NaN, NaN, NaN, NaN)} if the filtered 
#' \code{spectra} object has less than 4 scan events.
#'
#' An attribute containing the PSI:MS term will only be returned if 
#' \code{msLevel} is 1 or 2.
#' 
#' @note
#' \code{chromatographyDuration} considers the total runtime (including MS1 
#' and MS2 scans).
#' 
#' @param spectra \code{Spectra} object
#' @param msLevel \code{integer}
#' @param ... not used here
#' @param ... not used here
#'  
#' @return \code{numeric(4)}
#' 
#' @author Thomas Naake, Johannes Rainer
#' 
#' @export
#' 
#' @examples
#' library(S4Vectors)
#' library(Spectra)
#' 
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847", "unknown"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine", "unknown"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876),
#'     c(83.0603, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994),
#'     c(3.146, 61.611))
#' spd$rtime <- c(9.44, 9.44, 15.84, 15.81)
#' sps <- Spectra(spd)
#' rtOverMsQuarters(spectra = sps, msLevel = 2L)
rtOverMsQuarters <- function(spectra, msLevel = 1L, ...) {

    if (length(msLevel) != 1)
        stop("'msLevel' has to be of length 1")
    
    ## we assume that with RT duration the mzQC consortium means the run time 
    ## of the whole run, including MS1 and MS2
    rtd <- chromatographyDuration(spectra)

    ## truncate spectra based on the msLevel
    spectra <- filterMsLevel(object = spectra, msLevel)
    if (length(spectra) < 4) {
        res <- c(NaN, NaN, NaN, NaN)
    } else {
        ## order spectra according to increasing retention time
        spectra <- .rtOrderSpectra(spectra)
        RT <- rtime(spectra)
        rtmin <- min(RT)
        
        ## partition the spectra (rows) into four parts 
        ## (they are not necessarily equal)
        ind <- sort(rep(seq_len(4), length.out = length(spectra)))
        idx <- which(c(diff(ind), 1) == 1)
        
        ## calculate the retention time inidces
        res <- (RT[idx] - rtmin) / rtd
    }
    
    names(res) <- c("Quarter1", "Quarter2", "Quarter3", "Quarter4")
    
    ## add attributes and return
    if (msLevel == 1L) 
        ms_term <- "MS:4000055"
    if (msLevel == 2L) 
        ms_term <- "MS:4000056"
    if (msLevel %in% c(1L, 2L))
        attributes(res) <- c(attributes(res), list(rtOverMsQuarters = ms_term))
    
    res
}

#' @name ticQuartileToQuartileLogRatio
#' 
#' @title MS1 TIC-change quartile ratios (MS:4000057) or  
#' MS1 TIC quartile ratios (MS:4000058)
#' 
#' @description 
#' MS:4000057 \cr
#' "The log ratios of successive TIC-change quartiles. The TIC changes are 
#' the list of MS1 total ion current (TIC) value changes from one to the next 
#' scan, produced when each MS1 TIC is subtracted from the preceding MS1 TIC. 
#' The metric's value triplet represents the log ratio of the TIC-change 
#' Q2 to Q1, Q3 to Q2, TIC-change-max to Q3" [PSI:MS] \cr 
#' For calculation of MS:400057 set \code{mode = "TIC_change"}. \cr
#' 
#' MS:4000058 \cr
#' "The log ratios of successive TIC quartiles. The metric's value triplet 
#' represents the log ratios of TIC-Q2 to TIC-Q1, TIC-Q3 to TIC-Q2, 
#' TIC-max to TIC-Q3." [PSI:MS] \cr
#' For calculation of MS:400058 set \code{mode = "TIC"}. \cr
#'
#' The metric is calculated as follows: \cr
#' (1) the TIC (\code{ionCount}) of the \code{Spectra} object is calculated 
#' per scan event (with spectra ordered by retention time), \cr
#' (2) for *MS:4000057*, the differences between TIC values are calculated 
#' between subsequent scan events,
#' for *MS:4000058*, the TIC values between subsequent scan events are taken
#' as they are, \cr
#' (3) for *MS:4000057* and *MS:4000058* the ratios between the 25\%, 50\%, 
#' 75\%, and 100\% quantile to the 25\% quantile of the values of (2) are 
#' calculated.
#' Alternatively, if \code{relativeTo = "Q1"}, the ratios are calculated 
#' between the 50\%/25\%, 75\%/25\%, and 100\%/25\% quantiles, \cr
#' (4) The \code{log} values of the ratios are returned. \cr
#' 
#' @note
#' This function interprets the *quantiles* from the [PSI:MS] definition as
#' *quartiles*, i.e. the 0, 25, 50, 75 and 100\% quantiles are used.
#' 
#' @details
#' MS:4000057 \cr
#' synonym: "MS1-TIC-Change-Q2" RELATED [PMID:24494671] \cr
#' synonym: "MS1-TIC-Change-Q3" RELATED [PMID:24494671] \cr
#' synonym: "MS1-TIC-Change-Q4" RELATED [PMID:24494671] \cr
#' is_a: MS:4000004 ! n-tuple \cr
#' relationship: has_metric_category MS:4000009 ! ID free metric \cr
#' relationship: has_metric_category MS:4000012 ! single run based metric \cr
#' relationship: has_metric_category MS:4000017 ! chromatogram metric \cr
#' relationship: has_metric_category MS:4000021 ! MS1 metric \cr
#' relationship: has_value_type xsd:float ! The allowed value-type for this CV term \cr
#' relationship: has_value_concept STATO:0000105 ! log signal intensity ratio \cr
#' 
#' MS:4000058 \cr
#' synonym: "MS1-TIC-Q2" RELATED [PMID:24494671] \cr
#' synonym: "MS1-TIC-Q3" RELATED [PMID:24494671] \cr
#' synonym: "MS1-TIC-Q4" RELATED [PMID:24494671] \cr
#' is_a: MS:4000004 ! n-tuple \cr
#' relationship: has_metric_category MS:4000009 ! ID free metric \cr
#' relationship: has_metric_category MS:4000012 ! single run based metric \cr
#' relationship: has_metric_category MS:4000017 ! chromatogram metric \cr
#' relationship: has_metric_category MS:4000021 ! MS1 metric \cr
#' relationship: has_value_type xsd:float ! The allowed value-type for this CV term \cr
#' relationship: has_value_concept STATO:0000105 ! log signal intensity ratio \cr
#' 
#' An attribute containing the PSI:MS term will only be returned if 
#' \code{relativeTo} is \code{"previous"} and \code{msLevel} is 1. 
#'  
#' @param spectra \code{Spectra} object
#' @param relativeTo \code{character(1)}, one of \code{"Q1"} or 
#' \code{"previous"}
#' @param mode \code{character(1)}, one of \code{"TIC_change"} or \code{"TIC"}
#' @param msLevel \code{integer}
#' @param ... not used here
#' 
#' @return \code{numeric(1)}
#' 
#' @author Thomas Naake
#' 
#' @export
#' 
#' @examples
#' library(S4Vectors)
#' library(Spectra)
#' 
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
#' sps <- Spectra(spd)
#' 
#' ## MS:4000057
#' ticQuartileToQuartileLogRatio(spectra = sps, relativeTo = "previous",
#'     msLevel = 2L, mode = "TIC_change")
#' ticQuartileToQuartileLogRatio(spectra = sps, relativeTo = "Q1",
#'     msLevel = 2L, mode = "TIC_change")
#' 
#' ## MS:4000058
#' ticQuartileToQuartileLogRatio(spectra = sps, relativeTo = "previous",
#'     msLevel = 2L, mode = "TIC")
#' ticQuartileToQuartileLogRatio(spectra = sps, relativeTo = "Q1",
#'     msLevel = 2L, mode = "TIC")
ticQuartileToQuartileLogRatio <- function(spectra, 
    relativeTo = c("previous", "Q1"), mode = c("TIC_change", "TIC"), msLevel = 1L, ...) {

    relativeTo <- match.arg(relativeTo)
    
    mode <- match.arg(mode)
    
    spectra <- filterMsLevel(object = spectra, msLevel)  
    
    if (length(spectra) == 0) {
        
        ratioQuantileTIC <- c(NaN, NaN, NaN)
        
    } else {
        ## order spectra according to increasing retention time
        spectra <- .rtOrderSpectra(spectra)
        
        ## calculate TIC/ionCount
        TIC <- ionCount(spectra)
        
        ## calculate TIC changes if mode == "TIC_change",
        ## otherwise if mode == "TIC" take values as they are
        if (mode == "TIC_change")
            TIC <- diff(TIC) 
        if (mode == "TIC")
            TIC <- TIC
        
        ## obtain the quantiles (will be quartiles, 0%, 25%, 50%, 75%, 100%)
        quantileTIC <- quantile(TIC, na.rm = TRUE)
        
        ## calculate the changes in TIC per quantile
        changeQ1 <- quantileTIC[["25%"]]
        changeQ2 <- quantileTIC[["50%"]] 
        changeQ3 <- quantileTIC[["75%"]]
        changeQ4 <- quantileTIC[["100%"]]
        
        ## calculate the ratio between Q2/Q3/Q4 to Q1 quantile TIC (changes)
        if (relativeTo == "Q1") {
            ratioQuantileTIC <- c(changeQ2, changeQ3, changeQ4) / changeQ1
        }
        
        ## calculate the ratio between Q2/Q3/Q4 to previous quantile TIC (changes)
        if (relativeTo == "previous") {
            ratioQuantileTIC <- c(changeQ2 / changeQ1, changeQ3 / changeQ2, 
                                  changeQ4 / changeQ3)
        }
    }
    
    ## add names 
    if (relativeTo == "Q1") {
        names(ratioQuantileTIC) <- c("Q2/Q1", "Q3/Q1", "Q4/Q1")
    }
    if (relativeTo == "previous") {
        names(ratioQuantileTIC) <- c("Q2/Q1", "Q3/Q2", "Q4/Q3")
    }

    ## take the log and return
    res <- log(ratioQuantileTIC)
    
    ## add attributes and return
    if (mode == "TIC_change" & msLevel == 1L) 
        ms_term <- "MS:4000057"
    if (mode == "TIC" & msLevel == 1L) 
        ms_term <- "MS:4000058"
    
    ## add the attribute only in case of relativeTo = "previous"
    if (relativeTo == "previous" & msLevel == 1L)
        attributes(res) <- c(attributes(res), 
            list(ticQuartileToQuartileLogRatio = ms_term))
    
    res
}

#' @name numberSpectra
#'
#' @title number of MS1 spectra (MS:4000059) or number of MS2 spectra 
#' (MS:4000060)
#'
#' @description
#' MS:4000059 \cr
#' "The number of MS1 events in the run." [PSI:MS] \cr
#' 
#' MS:4000060 \cr
#' "The number of MS2 events in the run." [PSI:MS] \cr
#'
#' For *MS:4000059*, \code{msLevel} is set to 1. For *MS:4000060*, 
#' \code{msLevel} is set to 2.
#' 
#' The metric is calculated as follows: \cr
#' (1) the \code{Spectra} object is filtered according to the MS level, \cr
#' (2) the number of the spectra are obtained (\code{length} of \code{Spectra})
#' and returned.  
#'
#' @details
#' MS:4000059 \cr
#' synonym: "MS1-Count" EXACT [PMID:24494671] \cr
#' is_a: MS:4000003 ! single value \cr
#' relationship: has_metric_category MS:4000009 ! ID free metric \cr
#' relationship: has_metric_category MS:4000012 ! single run based metric \cr
#' relationship: has_metric_category MS:4000021 ! MS1 metric \cr
#' relationship: has_value_type xsd:int ! The allowed value-type for this CV term \cr
#' relationship: has_units UO:0000189 ! count unit \cr
#' 
#' MS:4000060 \cr
#' synonym: "MS2-Count" EXACT [PMID:24494671] \cr
#' is_a: MS:4000003 ! single value \cr
#' relationship: has_metric_category MS:4000009 ! ID free metric \cr
#' relationship: has_metric_category MS:4000012 ! single run based metric \cr
#' relationship: has_metric_category MS:4000022 ! MS2 metric \cr
#' relationship: has_value_type xsd:int ! The allowed value-type for this CV term \cr
#' relationship: has_units UO:0000189 ! count unit \cr
#' 
#' An attribute containing the PSI:MS term will only be returned if 
#' \code{msLevel} is either 1 or 2.
#' 
#' @param spectra \code{Spectra} object
#' @param msLevel \code{integer}
#' @param ... not used here
#'
#' @return \code{numeric(1)}
#'
#' @author Thomas Naake
#'
#' @export
#'
#' @importFrom ProtGenerics filterMsLevel
#' 
#' @examples
#' library(S4Vectors)
#' library(Spectra)
#' 
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
#' sps <- Spectra(spd)
#' numberSpectra(spectra = sps, msLevel = 1L)
#' numberSpectra(spectra = sps, msLevel = 2L)
numberSpectra <- function(spectra, msLevel = 1L, ...) {  
    
    if (length(msLevel) != 1)
        stop("'msLevel' has to be of length 1")
    
    spectra <- filterMsLevel(object = spectra, msLevel)
    res <- length(spectra)
    
    ## add attributes and return
    if (msLevel == 1L) 
        ms_term <- "MS:4000059"
    if (msLevel == 2L) 
        ms_term <- "MS:4000060"
    if (msLevel %in% c(1L, 2L))
        attributes(res) <- list(numberSpectra = ms_term)
    
    res
}

#' @name mzAcquisitionRange
#' 
#' @title m/z acquisition range (MS:4000069)
#' 
#' @description 
#' MS:4000069 \cr
#' "Upper and lower limit of m/z precursor values at which MSn spectra are 
#' recorded." [PSI:MS] \cr
#' 
#' The metric is calculated as follows: \cr
#' (1) the \code{Spectra} object is filtered according to the MS level, \cr 
#' (2) the precursor m/z values of the peaks within the \code{Spectra} object are obtained, \cr 
#' (3) the minimum and maximum precursor m/z values are obtained and returned. 
#'
#' @details
#' MS:4000069 \cr
#' is_a: MS:4000004 ! n-tuple \cr
#' relationship: has_metric_category MS:4000009 ! ID free metric \cr
#' relationship: has_metric_category MS:4000012 ! single run based metric \cr
#' relationship: has_metric_category MS:4000019 ! MS metric \cr
#' relationship: has_units MS:1000040 ! m/z \cr
#' relationship: has_value_concept STATO:0000035 ! range \cr
#'  
#' @param spectra \code{Spectra} object
#' @param msLevel \code{integer}
#' @param ... not used here
#' 
#' @return \code{numeric(2)}
#' 
#' @author Thomas Naake
#' 
#' @export
#' 
#' @importFrom ProtGenerics filterMsLevel mz
#' 
#' @examples
#' library(S4Vectors)
#' library(Spectra)
#' 
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"),
#'     precursorMz = c(170.16, 170.16, 195.08))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
#' sps <- Spectra(spd)
#' mzAcquisitionRange(spectra = sps, msLevel = 2L)
mzAcquisitionRange <- function(spectra, msLevel = 2L, ...) {
    
    spectra <- filterMsLevel(object = spectra, msLevel)
    
    if (length(spectra) == 0) {
        res <- c(NaN, NaN)
    } else {
        mzList <- precursorMz(spectra)
        mz <- unlist(mzList)
        res <- range(mz)
    }
    
    ## add attributes and return
    attributes(res) <- list(names = c("min", "max"), 
        mzAcquisitionRange = "MS:4000069")
    res
}

#' @name rtAcquisitionRange
#' 
#' @title retention time acquisition range (MS:4000070)
#' 
#' @description 
#' MS:4000070 \cr
#' "Upper and lower limit of retention time at which spectra are recorded." 
#' [PSI:MS] \cr
#' 
#' The metric is calculated as follows: \cr
#' (1) the \code{Spectra} object is filtered according to the MS level, \cr 
#' (2) the retention time values of the features within the \code{Spectra} 
#' object are obtained, \cr 
#' (3) the minimum and maximum retention time values are obtained and 
#' returned. \cr
#' 
#' @details
#' MS:4000070 \cr
#' is_a: MS:4000004 ! n-tuple \cr
#' relationship: has_metric_category MS:4000009 ! ID free metric \cr
#' relationship: has_metric_category MS:4000012 ! single run based metric \cr
#' relationship: has_metric_category MS:4000016 ! retention time metric \cr
#' relationship: has_units UO:0000010 ! second \cr
#' relationship: has_value_concept STATO:0000035 ! range \cr
#' 
#' @param spectra \code{Spectra} object
#' @param msLevel \code{integer}
#' @param ... not used here
#' 
#' @return
#' \code{numeric(2)}
#' 
#' @author Thomas Naake
#' 
#' @export
#' 
#' @importFrom ProtGenerics rtime filterMsLevel
#' 
#' @examples 
#' library(S4Vectors)
#' library(Spectra)
#' 
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
#' spd$rtime <- c(9.44, 9.44, 15.84)
#' sps <- Spectra(spd)
#' rtAcquisitionRange(spectra = sps, msLevel = 2L)
rtAcquisitionRange <- function(spectra, msLevel = 1L, ...) {
    
    spectra <- filterMsLevel(object = spectra, msLevel)
    
    if (length(spectra) == 0) {
        res <- c(NaN, NaN)
    } else {
        rt <- rtime(spectra)
        res <- range(rt)
    }
    
    ## add attributes and return
    attributes(res) <- list(names = c("min", "max"), 
        rtAcquisitionRange = "MS:4000070")
    res
}

#' @name msSignal10xChange
#' 
#' @title MS1 signal jump (10x) count (MS:4000097) or MS1 signal fall (10x) 
#' count (MS:4000098)
#' 
#' @description 
#' MS:4000097 \cr
#' "The number of times where MS1 TIC increased more than 10-fold between 
#' adjacent MS1 scans. An unusual high count of signal jumps or falls can 
#' indicate ESI stability issues." [PSI:MS] \cr
#' 
#' MS:4000098 \cr
#' "The number of times where MS1 TIC decreased more than 10-fold between 
#' adjacent MS1 scans. An unusual high count of signal jumps or falls can 
#' indicate ESI stability issues." [PSI:MS] \cr
#' 
#' The metric is calculated as follows: \cr
#' (1) the \code{Spectra} object is filtered according to the MS level, \cr 
#' (2) the intensity of the precursor ions within the \code{Spectra} object are 
#' obtained, \cr 
#' (3) the intensity values of the features are obtained via the ion count, \cr 
#' (4) the signal jumps/declines of the intensity values with the two 
#' subsequent intensity values is calculated, \cr 
#' (5) in the case of *MS:4000097*, the signal jumps by a factor of ten or more
#' are counted and returned; \cr
#' in the case of *MS:4000098*, the signal declines by a factor of ten or more
#' are counted and returned. \cr
#' 
#' @details
#' MS:4000097 \cr
#' is_a: MS:4000003 ! single value \cr
#' relationship: has_metric_category MS:4000009 ! ID free metric \cr
#' relationship: has_metric_category MS:4000021 ! MS1 metric \cr
#' relationship: has_units UO:0000189 ! count unit \cr
#' relationship: has_value_type xsd:integer ! The allowed value-type for this CV 
#' term \cr
#' synonym: "IS-1A"  RELATED [] \cr
#' 
#' MS:4000098 \cr
#' is_a: MS:4000003 ! single value \cr
#' relationship: has_metric_category MS:4000009 ! ID free metric \cr
#' relationship: has_metric_category MS:4000021 ! MS1 metric \cr
#' relationship: has_units UO:0000189 ! count unit \cr
#' relationship: has_value_type xsd:integer ! The allowed value-type for this CV
#' term \cr
#' synonym: "IS-1B"  RELATED [] \cr
#'  
#' The function \code{msSignal10xChange} uses the function \code{ionCount} as an 
#' equivalent to the TIC.
#' 
#' An attribute containing the PSI:MS term will only be returned if 
#' \code{msLevel} is 1.
#' 
#' @param spectra \code{Spectra} object
#' @param change \code{character(1)}, one of \code{"jump"} or \code{"fall"}
#' @param msLevel \code{integer}
#' @param ... not used here
#' 
#' @return \code{numeric(1)}
#' 
#' @author Thomas Naake
#' 
#' @export
#' 
#' @importFrom ProtGenerics tic ionCount filterMsLevel
#' 
#' @examples
#' library(S4Vectors)
#' library(Spectra)
#'
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
#' spd$rtime <- c(9.44, 9.44, 15.84)
#' sps <- Spectra(spd)
#' msSignal10xChange(spectra = sps, change = "jump", msLevel = 2L)
#' msSignal10xChange(spectra = sps, change = "fall", msLevel = 2L)
msSignal10xChange <- function(spectra, change = "jump", msLevel = 1L, ...) {
    
    if (length(change) != 1) {
        stop("'change' has to be of length 1")
    } else {
        change <- match.arg(change, choices = c("jump", "fall"))
    }
    
    spectra <- filterMsLevel(object = spectra, msLevel)
    
    if (length(spectra) == 0) {
        res <- NaN
    } else {
        ## order spectra according to increasing retention time
        spectra <- .rtOrderSpectra(spectra)
        
        tic <- ionCount(spectra)
        
        precedingTic <- tic[seq_len(length(tic) - 1)]
        followingTic <- tic[seq_len(length(tic))[-1]]
        
        ## calculate the ratio between following and preceding TICs and calculate
        ## the number of 10X jumps or falls depending on the change argument
        ratioTic <- followingTic / precedingTic
        
        if (change == "jump") 
            res <- sum(ratioTic >= 10)
        if (change == "fall")
            res <- sum(ratioTic <= 0.1)
    }
    
    ## add attributes and return
    if (change == "jump")
        ms_term <- "MS:4000097"
    if (change == "fall")
        ms_term <- "MS:4000098"
    if (msLevel == 1L)
        attributes(res) <- list(msSignal10xChange = ms_term)
    
    res
}

#' @name numberEmptyScans
#'
#' @title number of empty MS1 scans (MS:4000099), number of empty MS2 scans 
#' (MS:4000100), or number of empty MS3 scans (MS:4000101)
#'
#' @description
#' MS:4000099 \cr
#' "Number of MS1 scans where the scans' peaks intensity sums to 0 
#' (i.e. no peaks or only 0-intensity peaks)." [PSI:MS] \cr
#' 
#' MS:4000100 \cr
#' "Number of MS2 scans where the scans' peaks intensity sums to 0 
#' (i.e. no peaks or only 0-intensity peaks)." [PSI:MS] \cr
#' 
#' MS:4000101 \cr
#' "Number of MS3 scans where the scans' peaks intensity sums to 0 
#' (i.e. no peaks or only 0-intensity peaks)." [PSI:MS] \cr
#' 
#' 
#' The metric is calculated as follows: \cr
#' (1) the \code{Spectra} object is filtered according to the MS level, \cr 
#' (2) the intensities per entry are obtained, \cr
#' (3) the number of intensity entries that are \code{NULL}, \code{NA}, or 
#' that have a sum of \code{0} are obtained and returned. 
#' 
#' @details 
#' MS:4000099 \cr
#' is_a: MS:4000003 ! single value \cr
#' relationship: has_metric_category MS:4000009 ! ID free metric \cr
#' relationship: has_metric_category MS:4000012 ! single run based metric \cr
#' relationship: has_metric_category MS:4000021 ! MS1 metric \cr
#' relationship: has_units UO:0000189 ! count unit \cr
#' relationship: has_value_type xsd:integer ! The allowed value-type for this 
#' CV term \cr
#' 
#' MS:4000100 \cr
#' is_a: MS:4000003 ! single value \cr
#' relationship: has_metric_category MS:4000009 ! ID free metric \cr
#' relationship: has_metric_category MS:4000012 ! single run based metric \cr
#' relationship: has_metric_category MS:4000022 ! MS2 metric \cr
#' relationship: has_units UO:0000189 ! count unit \cr
#' relationship: has_value_type xsd:integer ! The allowed value-type for this 
#' CV term \cr
#' 
#' MS:4000101 \cr
#' is_a: MS:4000003 ! single value \cr
#' relationship: has_metric_category MS:4000009 ! ID free metric \cr
#' relationship: has_metric_category MS:4000012 ! single run based metric \cr
#' relationship: has_units UO:0000189 ! count unit \cr
#' relationship: has_value_type xsd:integer ! The allowed value-type for this CV 
#' term \cr
#' 
#' #' For *MS:4000099*, \code{msLevel} is set to 1. For *MS:4000100*, 
#' \code{msLevel} is set to 2. For *MS:4000101*, \code{msLevel} is set to 3.
#' 
#' An attribute containing the PSI:MS term will only be returned if 
#' \code{msLevel} is either 1, 2, or 3.
#' 
#' @param spectra \code{Spectra} object
#' @param msLevel \code{integer}
#' @param ... not used here
#'
#' @return \code{numeric(1)}
#'
#' @author Thomas Naake
#'
#' @export
#'
#' @importFrom ProtGenerics filterMsLevel intensity
#' 
#' @examples
#' library(S4Vectors)
#' library(Spectra)
#' 
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
#' sps <- Spectra(spd)
#' numberEmptyScans(spectra = sps, msLevel = 1L)
#' numberEmptyScans(spectra = sps, msLevel = 2L)
numberEmptyScans <- function(spectra, msLevel = 1L, ...) {
    
    if (length(msLevel) != 1)
        stop("'msLevel' has to be of length 1")
    
    spectra <- filterMsLevel(object = spectra, msLevel)
    
    ## three cases to take into account: 1) entry is NULL, 2) entry is NA,
    ## or 3) entry is of length 0; in all three cases set to TRUE, otherwise 
    ## to FALSE
    res <- intensity(spectra) |>
        lapply(FUN = function(i) 
            ifelse(is.null(i), TRUE, is.na(i) | length(i) == 0 | sum(i) == 0)) |> 
        unlist() |>
        sum()
    
    ## add attributes and return
    if (msLevel == 1L) 
        ms_term <- "MS:4000099"
    if (msLevel == 2L) 
        ms_term <- "MS:4000100"
    if (msLevel == 3L) 
        ms_term <- "MS:4000101"
    if (msLevel %in% c(1L, 2L, 3L))
        attributes(res) <- list(numberEmptyScans = ms_term)
    
    res
}

#' @name precursorIntensityQuartiles
#' 
#' @title MS2 precursor intensity distribution (MS:4000116), 
#' identified MS2 precursor intensity distribution (MS:4000161), or
#' unidentified MS2 precursor intensity distribution (MS:4000162)
#' 
#' @description
#' MS:4000116 \cr
#' "From the distribution of MS2 precursor intensities, the quantiles. E.g. a 
#' value triplet represents the quartiles Q1, Q2, Q3. The intensity 
#' distribution of the precursors informs about the dynamic range of the
#' acquisition." [PSI:MS] \cr
#' 
#' MS:40000161 \cr
#' From the distribution of identified MS2 precursor intensities, the quantiles. 
#' E.g. a value triplet represents the quartiles Q1, Q2, Q3. The intensity 
#' distribution of the precursors informs about 
#' the dynamic range of the acquisition in relation to identifiability. 
#' The used type of identification should be noted in the metadata or analysis 
#' methods section of the recording file for the respective run. In case of 
#' multiple acceptance criteria (FDR) available in proteomics, PSM-level FDR 
#' should be used for better comparability." [PSI:MS]" \cr
#' 
#' id: MS:4000162 \cr
#' "From the distribution of unidentified MS2 precursor intensities, the 
#' quantiles. E.g. a value triplet represents the quartiles Q1, Q2, Q3.
#' The intensity distribution of the precursors informs about the 
#' dynamic range of the acquisition in relation to identifiability. The used 
#' type of identification should be noted in the metadata or analysis methods 
#' section of the recording file for the respective run. In case of multiple 
#' acceptance criteria (FDR) available in proteomics, PSM-level FDR should be 
#' used for better comparability." [PSI:MS]" \cr
#' 
#' The metric is calculated as follows: \cr
#' (1) the \code{Spectra} object is filtered according to the MS level, \cr 
#' (2) the intensity of the precursor ions within the \code{Spectra} object 
#' are obtained, \cr 
#' (3) the 25\%, 50\%, and 75\% quantile of the  precursor intensity values are 
#' obtained (\code{NA} values are removed) and returned. \cr
#' 
#' @details
#' id: MS:4000116 \cr
#' is_a: MS:4000004 ! n-tuple \cr
#' relationship: has_metric_category MS:4000009 ! ID free metric \cr
#' relationship: has_metric_category MS:4000022 ! MS2 metric \cr
#' relationship: has_value_concept STATO:0000291 ! quantile \cr
#' relationship: has_value_type xsd:float ! The allowed value-type for this CV 
#' term \cr
#' relationship: has_units MS:1000043 ! intensity unit \cr
#' 
#' MS:4000161 \cr
#' is_a: MS:4000004 ! n-tuple \cr
#' is_a: MS:4000008 ! ID based \cr
#' relationship: has_metric_category MS:4000022 ! MS2 metric \cr
#' relationship: has_value_concept STATO:0000291 ! quantile \cr
#' relationship: has_value_type xsd:float ! The allowed value-type for this CV 
#' term \cr
#' relationship: has_units MS:1000043 ! intensity unit \cr
#' 
#' id: MS:4000162 \cr
#' is_a: MS:4000004 ! n-tuple \cr
#' is_a: MS:4000008 ! ID based \cr
#' relationship: has_metric_category MS:4000022 ! MS2 metric \cr
#' relationship: has_value_concept STATO:0000291 ! quantile \cr
#' relationship: has_value_type xsd:float ! The allowed value-type for this CV 
#' term \cr
#' relationship: has_units MS:1000043 ! intensity unit \cr
#' 
#' @note 
#' The \code{Spectra} object might contain features that were (not) identified. If
#' the calculation needs to be done according to *MS:4000161*/*MS:4000162*, the 
#' \code{Spectra} object should be prepared accordingly. 
#' 
#' @param spectra \code{Spectra} object
#' @param msLevel \code{integer}
#' @param identificationLevel \code{character(1)}, one of \code{"all"}, 
#' \code{"identified"}, or \code{"unidentified"}
#' @param ... not used here
#' 
#' @return \code{numeric(3)}
#' 
#' @author Thomas Naake
#' 
#' @export
#' 
#' @importFrom ProtGenerics precursorIntensity filterMsLevel
#' @importFrom stats quantile
#' 
#' @examples
#' library(S4Vectors)
#' library(Spectra)
#'
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
#' spd$precursorIntensity <- c(100.0, 100.0, 100.0)
#' sps <- Spectra(spd)
#' 
#' precursorIntensityQuartiles(spectra = sps, msLevel = 2L)
precursorIntensityQuartiles <- function(spectra, msLevel = 1L, 
        identificationLevel = c("all", "identified", "unidentified"), ...) {
    
    identificationLevel <- match.arg(identificationLevel)
    
    spectra <- filterMsLevel(object = spectra, msLevel)
    
    if (length(spectra) == 0) {
        res <- c(NaN, NaN, NaN)
    } else {
        int <- precursorIntensity(spectra)
        res <- quantile(int, probs = c(0.25, 0.50, 0.75), na.rm = TRUE)
    }
    
    ## add attributes and return
    if (identificationLevel == "all")
        ms_term <- "MS:4000116"
    if (identificationLevel == "identified")
        ms_term <- "MS:4000161"
    if (identificationLevel == "unidentified")
        ms_term <- "MS:4000162"
    
    attributes(res) <- list(names = c("Q1", "Q2", "Q3"), 
        precursorIntensityQuartiles = ms_term)
    res
}

#' @name precursorIntensityMean
#' 
#' @title MS2 precursor intensity distribution mean (MS:4000117),
#' identified MS2 precursor intensity distribution mean (MS:4000163), or
#' unidentified MS2 precursor intensity distribution mean (MS:4000164)
#' 
#' @description
#' MS:4000117 \cr
#' "From the distribution of MS2 precursor intensities, the mean. The intensity 
#' distribution of the precursors informs about the dynamic range of the 
#' acquisition." [PSI:MS] \cr
#' 
#' MS:4000163 \cr
#' "From the distribution of identified MS2 precursor intensities, the mean. The 
#' intensity distribution of the identified precursors informs about the dynamic 
#' range of the acquisition in relation to identifiability." [PSI:MS] \cr
#' 
#' MS:4000164 \cr
#' "From the distribution of unidentified MS2 precursor intensities, the mean. The 
#' intensity distribution of the unidentified precursors informs about the 
#' dynamic range of the acquisition in relation to identifiability." 
#' [PSI:MS] \cr
#' 
#' The metric is calculated as follows: \cr
#' (1) the \code{Spectra} object is filtered according to the MS level, \cr 
#' (2) the intensity of the precursor ions within the \code{Spectra} object are obtained, \cr 
#' (3) the mean of the precursor intensity values is obtained 
#' (\code{NA} values are removed) and returned. \cr
#' 
#' @details
#' MS:4000117 \cr
#' is_a: MS:4000003 ! single value \cr
#' relationship: has_metric_category MS:4000009 ! ID free metric \cr
#' relationship: has_metric_category MS:4000022 ! MS2 metric \cr
#' relationship: has_value_concept STATO:0000401 ! sample mean \cr
#' relationship: has_value_type xsd:float ! The allowed value-type for this CV term \cr
#' relationship: has_units MS:1000043 ! intensity unit \cr
#' 
#' MS:4000163 \cr
#' is_a: MS:4000003 ! single value \cr
#' is_a: MS:4000008 ! ID based \cr
#' relationship: has_metric_category MS:4000022 ! MS2 metric \cr
#' relationship: has_value_concept STATO:0000401 ! sample mean \cr
#' relationship: has_value_type xsd:float ! The allowed value-type for this CV term \cr
#' relationship: has_units MS:1000043 ! intensity unit \cr
#' 
#' MS:4000164 \cr
#' is_a: MS:4000003 ! single value \cr
#' is_a: MS:4000008 ! ID based \cr
#' relationship: has_metric_category MS:4000022 ! MS2 metric \cr
#' relationship: has_value_concept STATO:0000401 ! sample mean \cr
#' relationship: has_value_type xsd:float ! The allowed value-type for this CV term \cr
#' relationship: has_units MS:1000043 ! intensity unit \cr
#' 
#' @note 
#' The \code{Spectra} object might contain features that were (not) identified. If
#' the calculation needs to be done according to *MS:4000163*/*MS:4000164*, the 
#' \code{Spectra} object should be prepared accordingly. 
#' 
#' @param spectra \code{Spectra} object
#' @param msLevel \code{integer}
#' @param identificationLevel \code{character(1)}, one of \code{"all"}, 
#' \code{"identified"}, or \code{"unidentified"}
#' @param ... not used here
#' 
#' @return \code{numeric(1)}
#' 
#' @author Thomas Naake
#' 
#' @export
#' 
#' @importFrom ProtGenerics precursorIntensity filterMsLevel
#' 
#' @examples
#' library(S4Vectors)
#' library(Spectra)
#'
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
#' spd$precursorIntensity <- c(100.0, 100.0, 100.0)     
#' sps <- Spectra(spd)
#' precursorIntensityMean(spectra = sps, msLevel = 2L)
precursorIntensityMean <- function(spectra, msLevel = 1L, 
        identificationLevel = c("all", "identified", "unidentified"), ...) {
    
    identificationLevel <- match.arg(identificationLevel)
    
    spectra <- filterMsLevel(object = spectra, msLevel)
    
    if (length(spectra) == 0) {
        res <- NaN
    } else {
        int <- precursorIntensity(spectra)
        res <- mean(int, na.rm = TRUE)
    }
    
    ## add attributes and return
    if (identificationLevel == "all")
        ms_term <- "MS:4000117"
    if (identificationLevel == "identified")
        ms_term <- "MS:4000163"
    if (identificationLevel == "unidentified")
        ms_term <- "MS:4000164"
    
    attributes(res) <- list(precursorIntensityMean = ms_term)
    
    res
}

#' @name precursorIntensitySd
#' 
#' @title MS2 precursor intensity distribution sigma (MS:4000118),
#' identified MS2 precursor intensity distribution sigma (MS:4000165), or 
#' unidentified MS2 precursor intensity distribution sigma (MS:4000166)
#' 
#' @description 
#' MS:4000118 \cr
#' "From the distribution of MS2 precursor intensities, the sigma value. The 
#' intensity distribution of the precursors informs about the dynamic range 
#' of the acquisition." [PSI:MS] \cr
#' 
#' MS:4000165 \cr
#' "From the distribution of identified MS2 precursor intensities, the sigma value. 
#' The intensity distribution of the precursors informs about the dynamic 
#' range of the acquisition in relation to identifiability. The used type of 
#' identification should be noted in the metadata or analysis methods section 
#' of the recording file for the respective run. In case of multiple acceptance 
#' criteria (FDR) available in proteomics, PSM-level FDR should be used for 
#' better comparability." [PSI:MS] \cr
#' 
#' MS:4000166 \cr
#' "From the distribution of unidentified MS2 precursor intensities, the sigma 
#' value. The intensity distribution of the precursors informs about the 
#' dynamic range of the acquisition in relation to identifiability. The used 
#' type of identification should be noted in the metadata or analysis methods 
#' section of the recording file for the respective run. In case of multiple 
#' acceptance criteria (FDR) available in proteomics, PSM-level FDR should be 
#' used for better comparability." [PSI:MS] \cr
#' 
#' The metric is calculated as follows: \cr
#' (1) the \code{Spectra} object is filtered according to the MS level, \cr 
#' (2) the intensity of the precursor ions within the \code{Spectra} object are obtained, \cr 
#' (3) the standard deviation of precursor intensity values is obtained 
#' (\code{NA} values are removed) and returned. 
#' 
#' @details
#' MS:4000118 \cr 
#' is_a: MS:4000003 ! single value \cr 
#' relationship: has_metric_category MS:4000009 ! ID free metric \cr 
#' relationship: has_metric_category MS:4000022 ! MS2 metric \cr
#' relationship: has_value_concept STATO:0000237 ! standard deviation \cr 
#' relationship: has_value_type xsd:float ! The allowed value-type for this CV 
#' term \cr 
#' relationship: has_units MS:1000043 ! intensity unit \cr 
#' 
#' MS:4000165 \cr 
#' is_a: MS:4000003 ! single value \cr 
#' relationship: has_metric_category MS:4000008 ! ID based \cr 
#' relationship: has_metric_category MS:4000022 ! MS2 metric \cr
#' relationship: has_value_concept STATO:0000237 ! standard deviation \cr 
#' relationship: has_value_type xsd:float ! The allowed value-type for this CV 
#' term \cr 
#' relationship: has_units MS:1000043 ! intensity unit \cr 
#' 
#' MS:4000166 \cr 
#' is_a: MS:4000003 ! single value \cr 
#' relationship: has_metric_category MS:4000008 ! ID based \cr 
#' relationship: has_metric_category MS:4000022 ! MS2 metric \cr
#' relationship: has_value_concept STATO:0000237 ! standard deviation \cr 
#' relationship: has_value_type xsd:float ! The allowed value-type for this CV 
#' term \cr 
#' relationship: has_units MS:1000043 ! intensity unit \cr 
#'  
#' @note 
#' The \code{Spectra} object might contain features that were (not) identified. If
#' the calculation needs to be done according to *MS:4000165*/*MS:4000166*, the 
#' \code{Spectra} object should be prepared accordingly. 
#'
#' @param spectra \code{Spectra} object
#' @param msLevel \code{integer}
#' @param identificationLevel \code{character(1)}, one of \code{"all"}, 
#' \code{"identified"}, or \code{"unidentified"}
#' @param ... not used here
#' 
#' @return \code{numeric(1)}
#' 
#' @author Thomas Naake
#' 
#' @export
#' 
#' @importFrom ProtGenerics precursorIntensity filterMsLevel
#' @importFrom stats sd
#' 
#' @examples
#' library(S4Vectors)
#' library(Spectra)
#'
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
#' spd$precursorIntensity <- c(100.0, 100.0, 100.0)
#' sps <- Spectra(spd)
#' precursorIntensitySd(spectra = sps, msLevel = 2L)
precursorIntensitySd <- function(spectra, msLevel = 1L, 
        identificationLevel = c("all", "identified", "unidentified"), ...) {
    
    identificationLevel <- match.arg(identificationLevel)
    
    spectra <- filterMsLevel(object = spectra, msLevel)
    
    if (length(spectra) == 0) {
        res <- NaN
    } else {
        int <- precursorIntensity(spectra)
        res <- sd(int, na.rm = TRUE)
    }
    
    
    ## add attributes and return
    if (identificationLevel == "all")
        ms_term <- "MS:4000118"
    if (identificationLevel == "identified")
        ms_term <- "MS:4000165"
    if (identificationLevel == "unidentified")
        ms_term <- "MS:4000166"
    
    attributes(res) <- list(precursorIntensitySd = ms_term)
    
    res
}


#' @name medianPrecursorMz
#' 
#' @title MS2 precursor median m/z of identified quantification data points
#' (MS:4000152)
#' 
#' @description
#' MS:4000152 \cr
#' "Median m/z value for MS2 precursors of all quantification data points after 
#' user-defined acceptance criteria are applied. These data points may be for 
#' example XIC profiles, isotopic pattern areas, or reporter ions 
#' (see MS:1001805). The used type should be noted in the metadata or 
#' analysis methods section of the recording file for the respective run. In 
#' case of multiple acceptance criteria (FDR) available in proteomics, PSM-level 
#' FDR should be used for better comparability." [PSI:MS] \cr
#' 
#' The metric is calculated as follows: \cr
#' (1) the \code{Spectra} object is filtered according to the MS level, \cr 
#' (2) the precursor m/z values are obtained, \cr 
#' (3) the median value is returned (\code{NA}s are removed). \cr
#' 
#' @details
#' MS:4000152 \cr
#' is_a: MS:4000003 ! single value \cr
#' is_a: MS:4000008 ! ID based \cr
#' is_a: MS:4000020 ! ion source metric \cr
#' relationship: has_metric_category MS:4000022 ! MS2 metric \cr
#' relationship: has_units MS:1000040 ! m/z
#' 
#' An attribute containing the PSI:MS term will only be returned if 
#' \code{identificationLevel} is \code{"identified"} and \code{msLevel} is 1.
#' 
#' @note
#' \code{medianPrecursorMz} will calculate the *precursor* median m/z of all 
#' Spectra within \code{spectra}. If the calculation needs be done according to
#' *MS:4000152*, the \code{Spectra} object should be prepared accordingly, i.e.
#' filtered with e.g. [filterPrecursorMz()] or subsetted to spectra with
#' identification data.
#' 
#' @param spectra \code{Spectra} object
#' @param msLevel \code{integer}
#' @param identificationLevel \code{character(1)}, one of \code{"all"}, 
#' \code{"identified"}, or \code{"unidentified"}
#' @param ... not used here
#' 
#' @return \code{numeric(1)}
#' 
#' @author Thomas Naake
#' 
#' @export
#' 
#' @importFrom ProtGenerics precursorMz
#' 
#' @examples
#' library(S4Vectors)
#' library(Spectra)
#' 
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
#' spd$precursorMz <- c(170.16, 170.16, 195.0876)
#' sps <- Spectra(spd)
#' medianPrecursorMz(spectra = sps, msLevel = 2L)
medianPrecursorMz <- function(spectra, msLevel = 1L, 
        identificationLevel = c("all", "identified", "unidentified"), ...) {
    
    identificationLevel <- match.arg(identificationLevel)
    
    spectra <- filterMsLevel(object = spectra, msLevel)
    
    if (length(spectra) == 0) {
        res <- NaN
    } else {
        mz <- precursorMz(spectra)
        res <- median(mz, na.rm = TRUE)    
    }
    
    ## add attributes and return
    if (identificationLevel == "identified" & msLevel == 1L)
        attributes(res) <- list(medianPrecursorMz = "MS:4000152")
    
    res
    
}

#' @name rtIqr
#' 
#' @title interquartile RT period for identified quantification data points
#' (MS:4000153)
#' 
#' @description
#' MS:4000153 \cr
#' "The interquartile retention time period, in seconds, for all quantification 
#' data points after user-defined acceptance criteria are applied over the 
#' complete run. Longer times indicate better chromatographic separation. 
#' These data points may be for example XIC profiles, isotopic pattern areas, 
#' or reporter ions (see MS:1001805). The used type should be noted in the 
#' metadata or analysis methods section of the recording file for the respective 
#' run. In case of multiple acceptance criteria (FDR) available in proteomics, 
#' PSM-level FDR should be used for better comparability." [PSI:MS] \cr
#' 
#' The metric is calculated as follows: \cr
#' (1) the \code{Spectra} object is filtered according to the MS level, \cr
#' (2) the retention time values are obtained, \cr
#' (3) the interquartile range is obtained from the values and returned
#' (\code{NA} values are removed). \cr
#'
#' @details
#' MS:4000153 \cr
#' is_a: MS:4000003 ! single value \cr
#' is_a: MS:4000008 ! ID based \cr
#' is_a: MS:4000017 ! chromatogram metric \cr
#' relationship: has_units UO:0000010 ! second \cr
#' synonym: "C-2A"  RELATED [PMID:19837981] \cr
#' 
#' Retention time values that are \code{NA} are removed.
#' 
#' An attribute containing the PSI:MS term will only be returned if 
#' \code{identificationLevel} is \code{"identified"}.
#' 
#' @note 
#' The \code{Spectra} object might contain features that were not identified. If
#' the calculation needs to be done according to *MS:4000153*, the 
#' \code{Spectra} object should be prepared accordingly, i.e. subsetted to 
#' \code{spectra} with identification data.
#' 
#' The stored retention time information in \code{spectra} might have a 
#' different unit than seconds. \code{rtIqr} will return the IQR based on the 
#' values stored in \code{spectra} and will not convert these values to seconds. 
#' 
#' @param spectra \code{Spectra} object
#' @param msLevel \code{integer}
#' @param identificationLevel \code{character(1)}, one of \code{"all"}, 
#' \code{"identified"}, or \code{"unidentified"}
#' @param ... not used here
#' 
#' @return \code{numeric(1)}
#' 
#' @author Thomas Naake
#' 
#' @export
#' 
#' @importFrom ProtGenerics filterMsLevel rtime
#' @importFrom stats IQR
#' 
#' @examples
#' library(S4Vectors)
#' library(Spectra)
#' 
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
#' spd$rtime <- c(9.44, 9.44, 15.84)
#' sps <- Spectra(spd)
#' rtIqr(spectra = sps, msLevel = 2L)
rtIqr <- function(spectra, msLevel = 1L, 
        identificationLevel = c("all", "identified", "unidentified"), ...) {
    
    identificationLevel <- match.arg(identificationLevel)
    
    spectra <- filterMsLevel(object = spectra, msLevel) 
    
    if (length(spectra) == 0) {
        res <- NaN
    } else {
        ## get the retention time
        rt <- rtime(spectra)
        
        ## remove the retention time values that are NA and return the interquartile 
        ## range 
        res <- IQR(rt, na.rm = TRUE)    
    }
    
    ## add attributes and return
    if (identificationLevel == "identified")
        attributes(res) <- list(rtIqr = "MS:4000153")
    
    res
}

#' @name rtIqrRate
#' 
#' @title rate of the interquartile RT period for identified quantification 
#' data points (MS:4000154)
#' 
#' @description
#' MS:4000154 \cr
#' "The rate of identified quantification data points for the interquartile 
#' retention time period, in identified quantification data points per second. 
#' Higher rates indicate efficient sampling and identification. These data 
#' points may be for example XIC profiles, isotopic pattern areas, or reporter 
#' ions (see MS:1001805). The used type should be noted in the metadata or 
#' analysis methods section of the recording file for the respective run. In 
#' case of multiple acceptance criteria (FDR) available in proteomics, PSM-level 
#' FDR should be used for better comparability." [PSI:MS]\cr
#' 
#' The metric is calculated as follows: \cr
#' (1) the \code{Spectra} object is filtered according to the MS level, \cr 
#' (2) the retention time values are obtained, \cr 
#' (3) the 25\% and 75\% quantiles are obtained from the retention time values
#' (\code{NA} values are removed), \cr 
#' (4) the number of eluted features between this 25\% and 75\% quantile is 
#' calculated, \cr 
#' (5) the number of features is divided by the interquartile range of the 
#' retention time and returned. \cr 
#' 
#' @details
#' MS:4000154 \cr
#' is_a: MS:4000003 ! single value \cr
#' is_a: MS:4000008 ! ID based \cr
#' is_a: MS:4000017 ! chromatogram metric \cr
#' relationship: has_units UO:0000106 ! hertz
#' synonym: "C-2B"  RELATED [PMID:19837981] \cr
#' 
#' An attribute containing the PSI:MS term will only be returned if 
#' \code{identificationLevel} is \code{"identified"}.
#' 
#' @note 
#' The \code{Spectra} object might contain features that were not identified. If
#' the calculation needs to be done according to *MS:4000154*, the 
#' \code{Spectra} object should be prepared accordingly, i.e. being subsetted to
#' \code{spectra} with identification data.
#' 
#' The stored retention time information in \code{spectra} might have a different
#' unit than seconds. \code{.rtIqr} will return the IQR based on the values stored
#' in \code{spectra} and will not convert these values to seconds. 
#' 
#' @param spectra \code{Spectra} object
#' @param msLevel \code{integer}
#' @param identificationLevel \code{character(1)}, one of \code{"all"}, 
#' \code{"identified"}, or \code{"unidentified"}
#' @param ... not used here
#' 
#' @return \code{numeric(2)}
#' 
#' @author Thomas Naake
#' 
#' @export
#' 
#' @importFrom ProtGenerics rtime
#' @importFrom stats quantile
#' 
#' @examples
#' library(S4Vectors)
#' library(Spectra)
#' 
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
#' spd$rtime <- c(9.44, 9.44, 15.84)
#' sps <- Spectra(spd)
#' rtIqrRate(spectra = sps, msLevel = 2L)
rtIqrRate <- function(spectra, msLevel = 1L, 
        identificationLevel = c("all", "identified", "unidentified"), ...) {
    
    identificationLevel <- match.arg(identificationLevel)
    
    spectra <- filterMsLevel(object = spectra, msLevel)
    
    if (length(spectra) == 0) {
        res <- NaN
    } else {
        ## order spectra according to increasing retention time
        spectra <- .rtOrderSpectra(spectra)
        RT <- rtime(spectra)
        
        quantileRT <- quantile(RT, na.rm = TRUE)
        
        ## get the RT values of the 25% and 75% quantile
        quantile25RT <- quantileRT[["25%"]]
        quantile75RT <- quantileRT[["75%"]]
        
        ## get the number of eluted features between the 25% and 75% quantile
        nFeatures <- RT >= quantile25RT & RT <= quantile75RT
        nFeatures <- sum(nFeatures)
        
        ## divide the number of eluted features between the 25% and 75% quantile
        ## by the IQR to get the elution rate per second 
        res <- nFeatures / rtIqr(spectra, msLevel = msLevel)    
    }
    
    ## add attributes and return
    if (identificationLevel == "identified")
        attributes(res) <- list(rtIqrRate = "MS:4000154")
    
    res
}

#' @name areaUnderTic
#' 
#' @title area under TIC (MS:4000155)
#' 
#' @description 
#' MS:4000155 \cr
#' "The area under the total ion chromatogram." [PSI:MS] \cr
#' 
#' The metric is calculated as follows: \cr
#' (1) the \code{Spectra} object is filtered according to the MS level, \cr 
#' (2) the sum of the ion counts are obtained and returned. 
#' 
#' @details
#' MS:4000155 \cr
#' is_a: MS:4000003 ! single value \cr
#' is_a: MS:4000009 ! ID free \cr
#' is_a: MS:4000017 ! chromatogram metric \cr
#' 
#' The sum of the TIC is returned as an equivalent to the area. \cr
#' 
#' @param spectra \code{Spectra} object
#' @param msLevel \code{integer}
#' @param ... not used here
#' 
#' @return \code{numeric(1)}
#' 
#' @author Thomas Naake
#' 
#' @export
#' 
#' @importFrom ProtGenerics tic ionCount
#' 
#' @examples 
#' library(S4Vectors)
#' library(Spectra)
#' 
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
#' sps <- Spectra(spd)
#' areaUnderTic(spectra = sps, msLevel = 2L)
areaUnderTic <- function(spectra, msLevel = 1L, ...) {
    
    spectra <- filterMsLevel(object = spectra, msLevel)
    
    if (length(spectra) == 0) {
        res <- NaN
    } else {
        TIC <- ionCount(spectra)
        
        ## sum up the TIC (equivalent to the area) and return
        res <- sum(TIC, na.rm = TRUE)
    }
    
    ## add attributes and return
    attributes(res) <- list(areaUnderTic = "MS:4000155")
    
    res
}

#' @name areaUnderTicRtQuantiles
#' 
#' @title area under TIC RT quantiles (MS:4000156)
#' 
#' @description 
#' MS:4000156 \cr
#' "The area under the total ion chromatogram of the retention time quantiles. 
#' Number of quantiles are given by the n-tuple." [PSI:MS] \cr
#' 
#' The metric is calculated as follows: \cr
#' (1) the \code{Spectra} object is filtered according to the MS level, \cr 
#' (2) the \code{Spectra} object is ordered according to the retention time, \cr 
#' (3) the 0\%, 25\%, 50\%, 75\%, and 100\% quantiles of the retention time 
#' values are obtained, \cr 
#' (4) the ion count of the intervals between the 0\%/25\%, 25\%/50\%, 
#' 50\%/75\%, and 75\%/100\% are obtained, \cr 
#' (5) the ion counts of the intervals are summed (TIC) and the values returned.
#' 
#' @details
#' MS:4000156 \cr
#' is_a: MS:4000004 ! n-tuple \cr
#' is_a: MS:4000009 ! ID free \cr
#' is_a: MS:4000017 ! chromatogram metric \cr
#' 
#' The sum of the TIC is returned as an equivalent to the area. \cr
#' 
#' @note
#' This function interprets the *quantiles* from the [PSI:MS] definition as
#' *quartiles*, i.e. the 0, 25, 50, 75 and 100\% quantiles are used.
#' 
#' @param spectra \code{Spectra} object
#' @param msLevel \code{integer}
#' @param ... not used here
#' 
#' @return \code{.numeric(4)}
#' 
#' @author Thomas Naake
#' 
#' @export
#' 
#' @importFrom ProtGenerics tic ionCount rtime
#' @importFrom stats quantile
#' 
#' @examples
#' library(S4Vectors)
#' library(Spectra)
#' 
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
#' spd$rtime <- c(9.44, 9.44, 15.84)
#' sps <- Spectra(spd)
#' areaUnderTicRtQuantiles(spectra = sps, msLevel = 2L)
areaUnderTicRtQuantiles <- function(spectra, msLevel = 1L, ...) {
    
    spectra <- filterMsLevel(object = spectra, msLevel)
    
    if (length(spectra) == 0) {
        res <- c(NaN, NaN, NaN, NaN)
    } else {
        ## order spectra according to increasing retention time
        spectra <- .rtOrderSpectra(spectra)
        rt <- rtime(spectra)
        
        quantileRT <- quantile(rt, na.rm = TRUE)
        
        tic <- ionCount(spectra)
        
        ## get the TICs for the 1st, 2nd, 3rd, and 4th quartile
        ticQ1 <- tic[rt > quantileRT[["0%"]] & rt <= quantileRT[["25%"]]]
        ticQ2 <- tic[rt > quantileRT[["25%"]] & rt <= quantileRT[["50%"]]]
        ticQ3 <- tic[rt > quantileRT[["50%"]] & rt <= quantileRT[["75%"]]]
        ticQ4 <- tic[rt > quantileRT[["75%"]] & rt <= quantileRT[["100%"]]]
        
        ## sum the TICs (area) for the 1st, 2nd, 3rd, and 4th quartile
        areaTicQ1 <- sum(ticQ1, na.rm = TRUE)
        areaTicQ2 <- sum(ticQ2, na.rm = TRUE)
        areaTicQ3 <- sum(ticQ3, na.rm = TRUE)
        areaTicQ4 <- sum(ticQ4, na.rm = TRUE)
        
        ## return the summed TICs as a named vector
        res <- c(areaTicQ1, areaTicQ2, areaTicQ3, areaTicQ4)   
    }
    
    ## add attributes and return
    attributes(res) <- list(
        names = c("25%", "50%", "75%", "100%"),
        areaUnderTicRtQuantiles = "MS:4000156")
    
    res
}

#' @name extentIdentifiedPrecursorIntensity
#' 
#' @title extent of identified MS2 precursor intensity (MS:4000157)
#' 
#' @description 
#' MS:4000157 \cr
#' "Ratio of 95th over 5th percentile of MS2 precursor intensity for all 
#' quantification data points after user-defined acceptance criteria are 
#' applied. Can be used to approximate the dynamic range of signal. The used 
#' type of identification should be noted in the metadata or analysis methods 
#' section of the recording file for the respective run. In case of multiple 
#' acceptance criteria (FDR) available in proteomics, PSM-level FDR should 
#' be used for better comparability." [PSI:MS] \cr
#' 
#' The metric is calculated as follows: \cr
#' (1) the \code{Spectra} object is filtered according to the MS level, \cr 
#' (2) the intensities of the precursor ions are obtained, \cr 
#' (3) the 5\% and 95\% quantile of these intensities are obtained 
#' (\code{NA} values are removed), \cr 
#' (4) the ratio between the 95\% and the 5\% intensity quantile is calculated
#' and returned. \cr
#' 
#' @details
#' MS:4000157 \cr
#' is_a: MS:4000001 ! QC metric \cr
#' is_a: MS:4000003 ! single value \cr
#' is_a: MS:4000008 ! ID based \cr
#' relationship: has_metric_category MS:4000022 ! MS2 metric \cr
#' synonym: "MS1-3A"  RELATED [PMID:19837981] \cr
#' 
#' Precursor intensity values that are \code{NA} are removed. \cr
#' 
#' An attribute containing the PSI:MS term will only be returned if 
#' \code{identificationLevel} is \code{"identified"}.
#' 
#' @note 
#' The \code{Spectra} object might contain features that were not identified. If
#' the calculation needs to be done according to *MS:4000157*, the 
#' \code{Spectra} object should be prepared accordingly, i.e. being subsetted to
#' spectra with identification data.
#' 
#' @param spectra \code{Spectra} object
#' @param msLevel \code{integer}
#' @param identificationLevel \code{character(1)}, one of \code{"all"}, 
#' \code{"identified"}, or \code{"unidentified"}
#' @param ... not used here
#' 
#' @return \code{numeric(1)}
#' 
#' @author Thomas Naake
#' 
#' @export
#' 
#' @importFrom ProtGenerics filterMsLevel precursorIntensity
#' @importFrom stats quantile
#' 
#' @examples
#' library(S4Vectors)
#' library(Spectra)
#' 
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
#' spd$precursorIntensity <- c(100, 100, 100)
#' sps <- Spectra(spd)
#' extentIdentifiedPrecursorIntensity(spectra = sps, msLevel = 2L)
extentIdentifiedPrecursorIntensity <- function(spectra, msLevel = 1L, 
        identificationLevel = c("all", "identified", "unidentified"), ...) { 
    
    identificationLevel <- match.arg(identificationLevel)
    
    spectra <- filterMsLevel(object = spectra, msLevel)
    
    if (length(spectra) == 0) {
        res <- NaN
    } else {
        ## retrieve the precursorIntensity and calculate the 5% and 95% 
        ## quantile
        precInt <- precursorIntensity(spectra)
        quantilePrecInt <- quantile(precInt, probs = c(0.05, 0.95), 
            na.rm = TRUE)
        
        ## calculate the ratio between the 95% and 5% quantile and return the 
        ## value
        res <- quantilePrecInt[["95%"]] / quantilePrecInt[["5%"]]
    }
    
    ## add attributes and return
    if (identificationLevel == "identified")
        attributes(res) <- list(extentIdentifiedPrecursorIntensity = "MS:4000157")
    
    res
}

#' @name medianTicRtIqr
#' 
#' @title median of TIC values in the RT range in which the middle half of 
#' quantification data points are identified (MS:4000158)
#' 
#' @description
#' MS:4000158 \cr
#' "Median of TIC values in the RT range in which half of quantification data 
#' points are identified (RT values of Q1 to Q3 of identifications). These 
#' data points may be for example XIC profiles, isotopic pattern areas, or 
#' reporter ions (see MS:1001805). The used type should be noted in the metadata 
#' or analysis methods section of the recording file for the respective run. 
#' In case of multiple acceptance criteria (FDR) available in proteomics, 
#' PSM-level FDR should be used for better comparability." [PSI:MS] \cr
#' 
#' The metric is calculated as follows: \cr
#' (1) the \code{Spectra} object is filtered according to the MS level, \cr 
#' (2) the \code{Spectra} object is ordered according to the retention time, \cr 
#' (3) the features between the 1st and 3rd quartile are obtained 
#' (half of the features that are present in the \code{Spectra} object), \cr 
#' (4) the ion count of the features within the 1st and 3rd quartile is 
#' obtained, \cr 
#' (5) the median value of the ion count is calculated (\code{NA} values are 
#' removed) and the median value is returned. \cr
#' 
#' @details
#' MS:4000158 \cr
#' is_a: MS:4000001 ! QC metric \cr
#' is_a: MS:4000003 ! single value \cr
#' is_a: MS:4000008 ! ID based \cr
#' 
#' The function \code{medianTicRtIqr} uses the function [ionCount()] as an 
#' equivalent to the TIC.
#' 
#' An attribute containing the PSI:MS term will only be returned if 
#' \code{identificationLevel} is \code{"identified"}.
#' 
#' @note 
#' The \code{Spectra} object might contain features that were not identified. If
#' the calculation needs to be done according to *MS:4000158*, the 
#' \code{Spectra} object should be prepared accordingly, i.e. being subsetted to
#' spectra with identification data.
#' 
#' @param spectra \code{Spectra} object
#' @param msLevel \code{integer}
#' @param identificationLevel \code{character(1)}, one of \code{"all"}, 
#' \code{"identified"}, or \code{"unidentified"}
#' @param ... not used here
#' 
#' @return \code{numeric(1)}
#'
#' @author Thomas Naake
#'
#' @export
#' 
#' @importFrom ProtGenerics filterMsLevel tic ionCount
#' @importFrom stats median
#'
#' @examples
#' library(S4Vectors)
#' library(Spectra)
#' 
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
#' spd$rtime <- c(9.44, 9.44, 15.84)
#' sps <- Spectra(spd)
#' medianTicRtIqr(spectra = sps, msLevel = 2L)
medianTicRtIqr <- function(spectra, msLevel = 1L,
        identificationLevel = c("all", "identified", "unidentified"), ...) {
    
    identificationLevel <- match.arg(identificationLevel)
    
    spectra <- filterMsLevel(object = spectra, msLevel)
    
    if (length(spectra) == 0) {
        res <- NaN
    } else {
    
        ## order spectra according to increasing retention time
        spectra <- .rtOrderSpectra(spectra)
        
        ## get the Q1 to Q3 of identifications 
        ## (half of peptides that are identitied)
        ind <- rep(seq_len(4), length.out = length(spectra))
        ind <- sort(ind)
        Q1ToQ3 <- spectra[ind %in% c(2, 3), ]
        
        ## take the ionCount of the Q1 to Q3 of identifications
        ticQ1ToQ3 <- ionCount(Q1ToQ3)
        
        ## take the median value of the TIC within this interval and return it
        res <- median(ticQ1ToQ3, na.rm = TRUE)
    }
    
    ## add attributes and return
    if (identificationLevel == "identified")
        attributes(res) <- list(medianTicRtIqr = "MS:4000158")
    
    res
}

#' @name medianTicOfRtRange
#' 
#' @title median of TIC values in the shortest RT range in which half of the 
#' quantification data points are identified (MS:4000159)
#' 
#' @description 
#' MS:4000159 \cr
#' "Median of TIC values in the shortest RT range in which half of the 
#' quantification data points are identified. These data points may be for 
#' example XIC profiles, isotopic pattern areas, or reporter ions 
#' (see MS:1001805). The used type should be noted in the metadata or analysis 
#' methods section of the recording file for the respective run. In case of 
#' multiple acceptance criteria (FDR) available in proteomics, PSM-level FDR 
#' should be used for better comparability." [PSI:MS]  \cr
#' 
#' The metric is calculated as follows: \cr
#' (1) the \code{Spectra} object is filtered according to the MS level, \cr 
#' (2) the \code{Spectra} object is ordered according to the retention time, \cr 
#' (3) the number of features in the \code{Spectra} object is obtained and 
#' the number for half of the features is calculated, \cr 
#' (4) iterate through the features (always by taking the neighbouring
#' half of features) and calculate the retention time range of the
#' set of features, \cr 
#' (5) retrieve the set of features with the minimum retention time 
#' range, \cr 
#' (6) calculate from the set of (5) the median TIC (\code{NA} values are removed)
#' and return it.
#' 
#' @details
#' MS:4000159 \cr
#' is_a: MS:4000001 ! QC metric \cr
#' is_a: MS:4000003 ! single value \cr
#' is_a: MS:4000008 ! ID based \cr
#' synonym: "MS1-2B"  RELATED [PMID:19837981]
#' 
#' The function \code{medianTicOfRtRange} uses the function \code{ionCount} as an 
#' equivalent to the TIC.
#' 
#' An attribute containing the PSI:MS term will only be returned if 
#' \code{identificationLevel} is \code{"identified"}.
#' 
#' @note 
#' The \code{Spectra} object might contain features that were not identified. If
#' the calculation needs to be done according to *MS:4000159*, the 
#' \code{Spectra} object should be prepared accordingly, i.e. being subsetted to
#' spectra with identification data. 
#' 
#' @param spectra \code{Spectra} object
#' @param msLevel \code{integer}
#' @param identificationLevel \code{character(1)}, one of \code{"all"}, 
#' \code{"identified"}, or \code{"unidentified"}
#' @param ... not used here
#' 
#' @return
#' \code{numeric(1)}
#' 
#' @author Thomas Naake
#' 
#' @export
#' 
#' @importFrom ProtGenerics filterMsLevel tic ionCount rtime
#' @importFrom stats median
#' 
#' @examples
#' library(S4Vectors)
#' library(Spectra)
#' 
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
#' spd$rtime <- c(9.44, 9.44, 15.84)
#' sps <- Spectra(spd)
#' medianTicOfRtRange(spectra = sps, msLevel = 2L)
medianTicOfRtRange <- function(spectra, msLevel = 1L, 
        identificationLevel = c("all", "identified", "unidentified"), ...) {
    
    identificationLevel <- match.arg(identificationLevel)
  
    spectra <- filterMsLevel(object = spectra, msLevel)
    
    if (length(spectra) == 0) {
        res <- NaN
        
    } else {
    
        ## order spectra according to increasing retention time
        spectra <- .rtOrderSpectra(spectra)
        rt <- rtime(spectra)
        tic <- ionCount(spectra)
        
        ## retrieve number of features in object and calculate the number for half
        ## of the features
        n <- length(spectra)
        n_half <- ceiling(n / 2)
        
        ## iterate through the bunches of features (always take n_half features),
        ## start with 1 + n_half, 2 + n_half, 3 + n_half, ..., i_end + n_half
        ## calculate the RT range
        rangeRT <- lapply(seq_len(n_half + 1), function(i) {
            ind <- seq(i, i - 1 + n_half)
            rt_i <- rt[ind]
            max(rt_i) - min(rt_i)
        })
        rangeRT <- unlist(rangeRT)
        
        ## retrieve the index of the bunch with the minimum range and calculate
        ## the median TIC
        indMin <- which.min(rangeRT)
        ind <- seq(indMin, indMin - 1 + n_half)
        ticMin <- tic[ind]
        res <- median(ticMin, na.rm = TRUE)
    }
    
    ## add attributes and return
    if (identificationLevel == "identified")
        attributes(res) <- list(medianTicOfRtRange = "MS:4000159")
    
    res
}

#' @name precursorIntensityRange
#' 
#' @title MS2 precursor intensity range (MS:4000160)
#' 
#' @description 
#' MS:4000160 \cr
#' "Minimum and maximum MS2 precursor intensity recorded. The intensity range 
#' of the precursors informs about the dynamic range of the acquisition." 
#' [PSI:MS] \cr
#' 
#' The metric is calculated as follows: \cr
#' (1) the \code{Spectra} object is filtered according to the MS level, \cr 
#' (2) the intensity of the precursor ions within the \code{Spectra} object are obtained, \cr 
#' (3) the minimum and maximum precursor intensity values are obtained and 
#' returned. 
#' 
#' @details
#' MS:4000160 \cr
#' is_a: MS:4000001 ! QC metric \cr
#' is_a: MS:4000004 ! n-tuple \cr
#' is_a: MS:4000009 ! ID free \cr
#' relationship: has_metric_category MS:4000022 ! MS2 metric
#' 
#' @param spectra \code{Spectra} object
#' @param msLevel \code{integer}
#' @param ... not used here
#' 
#' @return
#' \code{numeric(2)}
#' 
#' @author Thomas Naake
#' 
#' @export
#' 
#' @importFrom ProtGenerics precursorIntensity filterMsLevel
#' 
#' @examples
#' library(S4Vectors)
#' library(Spectra)
#'
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
#' spd$precursorIntensity <- c(100.0, 100.0, 100.0)
#' sps <- Spectra(spd)
#' precursorIntensityRange(spectra = sps, msLevel = 2L)
precursorIntensityRange <- function(spectra, msLevel = 1, ...) {
  
    spectra <- filterMsLevel(object = spectra, msLevel)
    
    if (length(spectra) == 0) {
        res <- c(NaN, NaN)
    } else {
        int <- precursorIntensity(spectra)
        res <- range(int)
    }

    ## add names
    
    ## add attributes and return
    attributes(res) <- list(names = c("min", "max"), 
        precursorIntensityRange = "MS:4000160")
    
    res
}

#' @name ratioCharge1over2
#' 
#' @title ratio of 1+ over 2+ of all MS2 known precursor charges
#' (MS:4000167) or ratio of 1+ over 2+ of identified MS2 known precursor charges
#' (MS:4000168)
#' 
#' @description 
#' MS:4000167 \cr
#' "The ratio of 1+ over 2+ MS2 precursor charge count of all spectra. High 
#' ratios of 1+/2+ MS2 precursor charge count may indicate inefficient 
#' ionization." [PSI:MS] \cr
#' 
#' MS:4000168 \cr
#' "The ratio of 1+ over 2+ MS2 precursor charge count of identified spectra. 
#' High ratios of 1+/2+ MS2 precursor charge count may indicate inefficient 
#' ionization. The used type of identification should be noted in the metadata 
#' or analysis methods section of the recording file for the respective run. 
#' In case of multiple acceptance criteria (FDR) available in proteomics, 
#' PSM-level FDR should be used for better comparability." [PSI:MS] \cr
#' 
#' The metric is calculated as follows: \cr
#' (1) the \code{Spectra} object is filtered according to the MS level, \cr 
#' (2) the precursor charge is obtained, \cr 
#' (3) the number of precursors with charge 1+ is divided by the number of 
#' precursors with charge 2+ and the ratio is returned. \cr
#' 
#' @details
#' MS:4000167 \cr
#' is_a: MS:4000001 ! QC metric \cr
#' is_a: MS:4000003 ! single value \cr
#' is_a: MS:4000009 ! ID free metric \cr
#' relationship: has_metric_category MS:4000012 ! single run based metric \cr
#' relationship: has_metric_category MS:4000020 ! ion source metric \cr
#' relationship: has_metric_category MS:4000022 ! MS2 metric \cr
#' synonym: "IS-3A"  RELATED [PMID:19837981] \cr
#' synonym: "MS2 known precursor charges fractions"  RELATED [] \cr
#' synonym: "MS2-PrecZ-1" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-2" RELATED [PMID:24494671] \cr
#' 
#' MS:4000168 \cr
#' is_a: MS:4000001 ! QC metric \cr
#' is_a: MS:4000003 ! single value \cr
#' is_a: MS:4000008 ! ID based \cr
#' relationship: has_metric_category MS:4000012 ! single run based metric \cr
#' relationship: has_metric_category MS:4000020 ! ion source metric \cr
#' relationship: has_metric_category MS:4000022 ! MS2 metric \cr
#' synonym: "IS-3A"  RELATED [PMID:19837981] \cr
#' synonym: "MS2 known precursor charges fractions"  RELATED [] \cr
#' synonym: "MS2-PrecZ-1" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-2" RELATED [PMID:24494671] \cr
#' 
#' \code{NA} is returned if there are no features with precursor charge of 1+ or 
#' 2+. \cr
#' 
#' An attribute containing the PSI:MS term will only be returned if 
#' \code{identificationLevel} is either \code{"all"} or \code{"identified"}.
#' 
#' @note 
#' The \code{Spectra} object might contain features that were not identified. If
#' the calculation needs to be done according to *MS:4000168*, the 
#' \code{Spectra} object should be prepared accordingly. 
#' 
#' @param spectra \code{Spectra} object
#' @param msLevel \code{integer}
#' @param identificationLevel \code{character(1)}, one of \code{"all"}, 
#' \code{"identified"}, or \code{"unidentified"}
#' @param ... not used here
#' 
#' @return \code{numeric(1)}
#' 
#' @author Thomas Naake
#' 
#' @export
#' 
#' @importFrom ProtGenerics precursorCharge filterMsLevel
#' 
#' @examples
#' library(S4Vectors)
#' library(Spectra)
#'
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
#' spd$precursorCharge <- c(1L, 1L, 1L)
#' sps <- Spectra(spd)
#' ratioCharge1over2(spectra = sps, msLevel = 2L)
ratioCharge1over2 <- function(spectra, msLevel = 1L, 
        identificationLevel = c("all", "identified", "unidentified"), ...) {
  
    identificationLevel <- match.arg(identificationLevel)
    
    spectra <- filterMsLevel(object = spectra, msLevel)
    
    if (length(spectra) == 0) {
        res <- NaN
    } else {
        ## is there a way to get charge of actual entries, not only of precursor?
        charge <- precursorCharge(spectra)
        
        ## get the number of precursor per charge
        chargeTable <- table(charge)
        
        if (all(c(1, 2) %in% names(chargeTable)))
            res <- chargeTable[["1"]] / chargeTable[["2"]]
        else 
            res <- NaN
    }
    
    ## add attributes and return
    if (identificationLevel == "all")
        ms_term <-  "MS:4000167"
    if (identificationLevel == "identified")
        ms_term <-  "MS:4000168"
    
    if (identificationLevel %in% c("all", "identified"))
        attributes(res) <- list(ratioCharge1over2 = ms_term)
    
    res
}

#' @name ratioCharge3over2
#' 
#' @title ratio of 3+ over 2+ of all MS2 known precursor charges (MS:4000169) or 
#' ratio of 3+ over 2+ of identified MS2 known precursor charges (MS:4000170)
#' 
#' @description 
#' MS:4000169 \cr
#' "The ratio of 3+ over 2+ MS2 precursor charge count of all spectra. Higher 
#' ratios of 3+/2+ MS2 precursor charge count may preferentially favor 
#' longer e.g. peptides." [PSI:MS] \cr
#' 
#' MS:4000170 \cr
#' "The ratio of 3+ over 2+ MS2 precursor charge count of identified spectra. 
#' Higher ratios of 3+/2+ MS2 precursor charge count may preferentially favor 
#' longer e.g. peptides. The used type of identification should be noted in the 
#' metadata or analysis methods section of the recording file for the respective
#' run. In case of multiple acceptance criteria (FDR) available in proteomics, 
#' PSM-level FDR should be used for better comparability." [PSI:MS] \cr
#' 
#' The metric is calculated as follows: \cr
#' (1) the \code{Spectra} object is filtered according to the MS level, \cr 
#' (2) the precursor charge is obtained, \cr 
#' (3) the number of precursors with charge 3+ is divided by the number of 
#' precursors with charge 2+ and the ratio is returned. \cr
#' 
#' @details
#' MS:4000169 \cr
#' is_a: MS:4000001 ! QC metric \cr
#' is_a: MS:4000003 ! single value \cr
#' is_a: MS:4000009 ! ID free metric \cr
#' relationship: has_metric_category MS:4000012 ! single run based metric \cr
#' relationship: has_metric_category MS:4000020 ! ion source metric \cr
#' relationship: has_metric_category MS:4000022 ! MS2 metric \cr
#' synonym: "IS-3B"  RELATED [PMID:19837981] \cr
#' synonym: "MS2 known precursor charges fractions"  RELATED [] \cr
#' synonym: "MS2-PrecZ-2" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-3" RELATED [PMID:24494671] \cr
#' 
#' MS:4000170 \cr
#' is_a: MS:4000001 ! QC metric \cr
#' is_a: MS:4000003 ! single value \cr
#' is_a: MS:4000008 ! ID based \cr
#' relationship: has_metric_category MS:4000012 ! single run based metric \cr
#' relationship: has_metric_category MS:4000020 ! ion source metric \cr
#' relationship: has_metric_category MS:4000022 ! MS2 metric \cr
#' synonym: "IS-3B"  RELATED [PMID:19837981] \cr
#' synonym: "MS2 known precursor charges fractions"  RELATED [] \cr
#' synonym: "MS2-PrecZ-2" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-3" RELATED [PMID:24494671] \cr
#' 
#' \code{NA} is returned if there are no features with precursor charge of 2+ or 
#' 3+.
#' 
#' An attribute containing the PSI:MS term will only be returned if 
#' \code{identificationLevel} is either \code{"all"} or \code{"identified"}.
#' 
#' @note 
#' The \code{Spectra} object might contain features that were not identified. If
#' the calculation needs to be done according to *MS:4000170*, the 
#' \code{Spectra} object should be prepared accordingly. 
#' 
#' @param spectra \code{Spectra} object
#' @param msLevel \code{integer}
#' @param identificationLevel \code{character(1)}, one of \code{"all"}, 
#' \code{"identified"}, or \code{"unidentified"}
#' @param ... not used here
#' 
#' @return \code{numeric(1)}
#' 
#' @author Thomas Naake
#' 
#' @export
#' 
#' @importFrom ProtGenerics precursorCharge filterMsLevel
#' 
#' @examples
#' library(S4Vectors)
#' library(Spectra)
#'
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
#' spd$precursorCharge <- c(1L, 1L, 1L)
#' sps <- Spectra(spd)
#' ratioCharge3over2(spectra = sps, msLevel = 2L)
ratioCharge3over2 <- function(spectra, msLevel = 1L, 
        identificationLevel = c("all", "identified", "unidentified"), ...) {
  
    identificationLevel <- match.arg(identificationLevel)
    
    spectra <- filterMsLevel(object = spectra, msLevel)
    
    if (length(spectra) == 0) {
        res <- NaN
    } else {
        ## is there a way to get charge of actual entries, not only of precursor?
        charge <- precursorCharge(spectra)
        
        ## get the number of precursor per charge
        chargeTable <- table(charge)
        
        if (all(c(2, 3) %in% names(chargeTable)))
            res <- chargeTable[["3"]] / chargeTable[["2"]]
        else 
            res <- NaN
    }
    
    ## add attributes and return
    if (identificationLevel == "all")
        ms_term <-  "MS:4000169"
    if (identificationLevel == "identified")
        ms_term <-  "MS:4000170"
    if (identificationLevel %in% c("all", "identified"))
        attributes(res) <- list(ratioCharge3over2 = ms_term)
    
    res
}

#' @name ratioCharge4over2
#' 
#' @title ratio of 4+ over 2+ of all MS2 known precursor charges (MS:4000171) or 
#' ratio of 4+ over 2+ of identified MS2 known precursor charges (MS:4000172)
#' 
#' @description 
#' MS:4000171 \cr
#' "The ratio of 4+ over 2+ MS2 precursor charge count of all spectra. Higher 
#' ratios of 4+/2+ MS2 precursor charge count may preferentially favor longer 
#' e.g. peptides." [PSI:MS] \cr
#' 
#' MS:4000172 \cr
#' "The ratio of 4+ over 2+ MS2 precursor charge count of identified spectra. 
#' Higher ratios of 4+/2+ MS2 precursor charge count may preferentially favor 
#' longer e.g. peptides. The used type of identification should be noted in the 
#' metadata or analysis methods section of the recording file for the respective
#' run. In case of multiple acceptance criteria (FDR) available in proteomics,
#' PSM-level FDR should be used for better comparability." [PSI:MS] \cr
#'  
#' The metric is calculated as follows: \cr
#' (1) the \code{Spectra} object is filtered according to the MS level, \cr 
#' (2) the precursor charge is obtained, \cr 
#' (3) the number of precursors with charge 4+ is divided by the number of 
#' precursors with charge 2+ and the ratio is returned.
#' 
#' @details
#' MS:4000171 \cr
#' is_a: MS:4000001 ! QC metric \cr
#' is_a: MS:4000003 ! single value \cr
#' is_a: MS:4000009 ! ID free metric \cr
#' relationship: has_metric_category MS:4000012 ! single run based metric \cr
#' relationship: has_metric_category MS:4000020 ! ion source metric \cr
#' relationship: has_metric_category MS:4000022 ! MS2 metric \cr
#' synonym: "IS-3C"  RELATED [PMID:19837981] \cr
#' synonym: "MS2 known precursor charges fractions"  RELATED [] \cr
#' synonym: "MS2-PrecZ-2" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-4" RELATED [PMID:24494671] \cr
#' 
#' MS:4000172 \cr
#' is_a: MS:4000001 ! QC metric \cr
#' is_a: MS:4000003 ! single value \cr
#' is_a: MS:4000008 ! ID based \cr
#' relationship: has_metric_category MS:4000012 ! single run based metric \cr
#' relationship: has_metric_category MS:4000020 ! ion source metric \cr
#' relationship: has_metric_category MS:4000022 ! MS2 metric \cr
#' synonym: "IS-3C"  RELATED [PMID:19837981] \cr
#' synonym: "MS2 known precursor charges fractions"  RELATED [] \cr
#' synonym: "MS2-PrecZ-2" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-4" RELATED [PMID:24494671] \cr
#' 
#' An attribute containing the PSI:MS term will only be returned if 
#' \code{identificationLevel} is either \code{"all"} or \code{"identified"}.
#' 
#' @note 
#' The \code{Spectra} object might contain features that were not identified. If
#' the calculation needs to be done according to *MS:4000172*, the 
#' \code{Spectra} object should be prepared accordingly. \cr
#' 
#' \code{NA} is returned if there are no features with precursor charge of 2+ or 
#' 3+.
#' 
#' @param spectra \code{Spectra} object
#' @param msLevel \code{integer}
#' @param identificationLevel \code{character(1)}, one of \code{"all"}, 
#' \code{"identified"}, or \code{"unidentified"}
#' @param ... not used here
#' 
#' @return \code{numeric(1)}
#' 
#' @author Thomas Naake
#' 
#' @export
#' 
#' @importFrom ProtGenerics precursorCharge filterMsLevel
#' 
#' @examples
#' library(S4Vectors)
#' library(Spectra)
#'
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
#' spd$precursorCharge <- c(1L, 1L, 1L)
#' sps <- Spectra(spd)
#' ratioCharge4over2(spectra = sps, msLevel = 2L)
ratioCharge4over2 <- function(spectra, msLevel = 1L,
        identificationLevel = c("all", "identified", "unidentified"), ...) {
  
    identificationLevel <- match.arg(identificationLevel)
    
    spectra <- filterMsLevel(object = spectra, msLevel)
    
    if (length(spectra) == 0) {
        res <- NaN
    } else {
        ## is there a way to get charge of actual entries, not only of precursor?
        charge <- precursorCharge(spectra)
        
        ## get the number of precursor per charge
        chargeTable <- table(charge)
        
        if (all(c(2, 4) %in% names(chargeTable)))
            res <- chargeTable[["4"]] / chargeTable[["2"]]
        else 
            res <- NaN
    }
    
    ## add attributes and return
    if (identificationLevel == "all")
        ms_term <-  "MS:4000171"
    if (identificationLevel == "identified")
        ms_term <-  "MS:4000172"
    if (identificationLevel %in% c("all", "identified"))
        attributes(res) <- list(ratioCharge4over2 = ms_term)
    
    res
}


#' @name meanCharge
#' 
#' @title mean MS2 precursor charge in all spectra (MS:4000173) or mean MS2 
#' precursor charge in identified spectra (MS:4000174)
#' 
#' @description 
#' MS:4000173 \cr
#' "Mean MS2 precursor charge in all spectra" [PSI:MS] \cr
#' 
#' MS:4000174 \cr
#' "Mean MS2 precursor charge in identified spectra. The used type of 
#' identification should be noted in the metadata or analysis methods section 
#' of the recording file for the respective run. In case of multiple acceptance 
#' criteria (FDR) available in proteomics, PSM-level FDR should be used for 
#' better comparability." [PSI:MS] \cr
#' 
#' The metric is calculated as follows: \cr
#' (1) the \code{Spectra} object is filtered according to the MS level, \cr
#' (2) the precursor charge is obtained, \cr
#' (3) the mean of the precursor charge values is calculated and returned. \cr
#' 
#' @details
#' MS:4000173 \cr
#' is_a: MS:4000001 ! QC metric
#' is_a: MS:4000003 ! single value
#' is_a: MS:4000009 ! ID free metric
#' relationship: has_metric_category MS:4000012 ! single run based metric \cr
#' relationship: has_metric_category MS:4000020 ! ion source metric \cr
#' relationship: has_metric_category MS:4000022 ! MS2 metric \cr
#' synonym: "MS2 known precursor charges fractions"  RELATED [] \cr
#' synonym: "MS2-PrecZ-1" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-2" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-3" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-4" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-5" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-more" RELATED [PMID:24494671] \cr
#' 
#' MS:4000174 \cr
#' is_a: MS:4000001 ! QC metric \cr
#' is_a: MS:4000003 ! single value \cr
#' is_a: MS:4000008 ! ID based \cr
#' relationship: has_metric_category MS:4000012 ! single run based metric \cr
#' relationship: has_metric_category MS:4000020 ! ion source metric \cr
#' relationship: has_metric_category MS:4000022 ! MS2 metric \cr
#' synonym: "MS2 known precursor charges fractions"  RELATED [] \cr
#' synonym: "MS2-PrecZ-1" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-2" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-3" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-4" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-5" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-more" RELATED [PMID:24494671] \cr
#' 
#' An attribute containing the PSI:MS term will only be returned if 
#' \code{identificationLevel} is either \code{"all"} or \code{"identified"}.
#' 
#' @note 
#' The \code{Spectra} object might contain features that were not identified. If
#' the calculation needs to be done according to *MS:4000174*, the 
#' \code{Spectra} object should be prepared accordingly. 
#' 
#' @param spectra \code{Spectra} object
#' @param msLevel \code{integer}
#' @param identificationLevel \code{character(1)}, one of \code{"all"}, 
#' \code{"identified"}, or \code{"unidentified"}
#' @param ... not used here
#' 
#' @return \code{numeric(1)}
#' 
#' @author Thomas Naake
#' 
#' @export
#' 
#' @importFrom ProtGenerics precursorCharge filterMsLevel
#' 
#' @examples
#' library(S4Vectors)
#' library(Spectra)
#'
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
#' spd$precursorCharge <- c(1L, 1L, 1L)
#' sps <- Spectra(spd)
#' meanCharge(spectra = sps, msLevel = 2L)
meanCharge <- function(spectra, msLevel = 1L, 
        identificationLevel = c("all", "identified", "unidentified"), ...) {
    
    identificationLevel <- match.arg(identificationLevel)
    
    spectra <- filterMsLevel(object = spectra, msLevel)
    
    if (length(spectra) == 0) {
        res <- NaN
    } else {
        charge <- precursorCharge(spectra)
        res <- mean(charge, na.rm = TRUE)   
    }
    
    ## add attributes and return
    if (identificationLevel == "all")
        ms_term <- "MS:4000173"
    if (identificationLevel == "identified")
        ms_term <- "MS:4000174"
    if (identificationLevel %in% c("all", "identified"))
        attributes(res) <- list(meanCharge = ms_term)
    
    res
    
}

#' @name medianCharge
#' 
#' @title median MS2 precursor charge in all spectra (MS:4000175) or median MS2 
#' precursor charge in identified spectra (MS:4000176)
#' 
#' @description 
#' MS:4000175 \cr
#' "Median MS2 precursor charge in all spectra" [PSI:MS] \cr
#' 
#' MS:4000176 \cr
#' "Median MS2 precursor charge in identified spectra. The used type of 
#' identification should be noted in the metadata or analysis methods section 
#' of the recording file for the respective run. In case of multiple acceptance
#' criteria (FDR) available in proteomics, PSM-level FDR should be used for 
#' better comparability." [PSI:MS] \cr
#' 
#' The metric is calculated as follows: \cr
#' (1) the \code{Spectra} object is filtered according to the MS level, \cr 
#' (2) the precursor charge is obtained, \cr 
#' (3) the median of the precursor charge values is calculated and returned.
#' 
#' @details
#' MS:4000175 \cr
#' is_a: MS:4000001 ! QC metric \cr
#' is_a: MS:4000003 ! single value \cr
#' is_a: MS:4000009 ! ID free metric \cr
#' relationship: has_metric_category MS:4000012 ! single run based metric \cr
#' relationship: has_metric_category MS:4000020 ! ion source metric \cr
#' relationship: has_metric_category MS:4000022 ! MS2 metric \cr
#' synonym: "MS2 known precursor charges fractions"  RELATED [] \cr
#' synonym: "MS2-PrecZ-1" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-2" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-3" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-4" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-5" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-more" RELATED [PMID:24494671] \cr
#' 
#' MS:4000176 \cr
#' is_a: MS:4000001 ! QC metric \cr
#' is_a: MS:4000003 ! single value \cr
#' is_a: MS:4000008 ! ID based \cr
#' relationship: has_metric_category MS:4000012 ! single run based metric \cr
#' relationship: has_metric_category MS:4000020 ! ion source metric \cr
#' relationship: has_metric_category MS:4000022 ! MS2 metric \cr
#' synonym: "MS2 known precursor charges fractions"  RELATED [] \cr
#' synonym: "MS2-PrecZ-1" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-2" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-3" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-4" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-5" RELATED [PMID:24494671] \cr
#' synonym: "MS2-PrecZ-more" RELATED [PMID:24494671] \cr
#' 
#' An attribute containing the PSI:MS term will only be returned if 
#' \code{identificationLevel} is either \code{"all"} or \code{"identified"}.
#' 
#' @note 
#' The \code{Spectra} object might contain features that were not identified. If
#' the calculation needs to be done according to *MS:4000176*, the 
#' \code{Spectra} object should be prepared accordingly.
#' 
#' @param spectra \code{Spectra} object
#' @param msLevel \code{integer}
#' @param identificationLevel \code{character(1)}, one of \code{"all"}, 
#' \code{"identified"}, or \code{"unidentified"}
#' @param ... not used here
#' 
#' @return \code{numeric(1)}
#' 
#' @author Thomas Naake
#' 
#' @export
#' 
#' @importFrom ProtGenerics precursorCharge filterMsLevel
#' 
#' @examples
#' library(S4Vectors)
#' library(Spectra)
#'
#' spd <- DataFrame(
#'     msLevel = c(2L, 2L, 2L),
#'     polarity = c(1L, 1L, 1L),
#'     id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
#'     name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))
#' ## Assign m/z and intensity values
#' spd$mz <- list(
#'     c(109.2, 124.2, 124.5, 170.16, 170.52),
#'     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
#'     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
#'         111.0551, 123.0429, 138.0662, 195.0876))
#' spd$intensity <- list(
#'     c(3.407, 47.494, 3.094, 100.0, 13.240),
#'     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
#'     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
#' sps <- Spectra(spd)
#' spd$precursorCharge <- c(1L, 1L, 1L)
#' medianCharge(spectra = sps, msLevel = 2L)
medianCharge <- function(spectra, msLevel = 1L, 
        identificationLevel = c("all", "identified", "unidentified"), ...) {

    identificationLevel <- match.arg(identificationLevel)
    
    spectra <- filterMsLevel(object = spectra, msLevel)
    
    if (length(spectra) == 0) {
        res <- NaN
    } else {
        charge <- precursorCharge(spectra)
        res <- median(charge, na.rm = TRUE)     
    }
    
    ## add attributes and return
    if (identificationLevel == "all")
        ms_term <- "MS:4000175"
    if (identificationLevel == "identified")
        ms_term <- "MS:4000176"
    
    if (identificationLevel %in% c("all", "identified"))
        attributes(res) <- list(medianCharge = ms_term)
    
    res
}
tnaake/msQC documentation built on Feb. 25, 2024, 7:33 p.m.
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tnaake/msQC
MsQuality - Quality metric calculation from Spectra and MsExperiment objects

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