R/pairwise_pep_peak_corp.R
In Roestlab/DIAlignR: Dynamic Programming Based Alignment of MS2 Chromatograms
Defines functions
alignedTimes2
getAlignedIndices
getAlignedTimesFast
getAlignedTimes
getMappedRT
getAlignObj2
getAlignObj
Documented in
getAlignedIndices
getAlignedTimes
getAlignedTimesFast
getAlignObj
getMappedRT
#' Outputs AlignObj from an alignment of two XIC-groups
#'
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2019) + GPL-3
#' Date: 2019-12-13
#'
#' @param XICs.ref List of extracted ion chromatograms from reference run.
#' @param XICs.eXp List of extracted ion chromatograms from experiment run.
#' @param globalFit Linear or loess fit object between reference and experiment run.
#' @param alignType Available alignment methods are "global", "local" and "hybrid".
#' @param adaptiveRT (numeric) Similarity matrix is not penalized within adaptive RT.
#' @param normalization (character) Must be selected from "mean", "l2".
#' @param simType (string) Must be selected from dotProduct, cosineAngle, crossCorrelation,
#' cosine2Angle, dotProductMasked, euclideanDist, covariance and correlation.
#' @param goFactor (numeric) Penalty for introducing first gap in alignment. This value is multiplied by base gap-penalty.
#' @param geFactor (numeric) Penalty for introducing subsequent gaps in alignment. This value is multiplied by base gap-penalty.
#' @param cosAngleThresh (numeric) In simType = dotProductMasked mode, angular similarity should be higher than cosAngleThresh otherwise similarity is forced to zero.
#' @param OverlapAlignment (logical) An input for alignment with free end-gaps. False: Global alignment, True: overlap alignment.
#' @param dotProdThresh (numeric) In simType = dotProductMasked mode, values in similarity matrix higher than dotProdThresh quantile are checked for angular similarity.
#' @param gapQuantile (numeric) Must be between 0 and 1. This is used to calculate base gap-penalty from similarity distribution.
#' @param kerLen (integer) In simType = crossCorrelation, length of the kernel used to sum similarity score. Must be an odd number.
#' @param hardConstrain (logical) If FALSE; indices farther from noBeef distance are filled with distance from linear fit line.
#' @param samples4gradient (numeric) This parameter modulates penalization of masked indices.
#' @param objType (char) Must be selected from light, medium and heavy.
#' @return A S4 object. Three most-important slots are:
#' \item{indexA_aligned}{(integer) aligned indices of reference run.}
#' \item{indexB_aligned}{(integer) aligned indices of experiment run.}
#' \item{score}{(numeric) cumulative score of alignment.}
#'
#' @seealso \code{\link{alignChromatogramsCpp}}
#' @examples
#' data(XIC_QFNNTDIVLLEDFQK_3_DIAlignR, package="DIAlignR")
#' data(oswFiles_DIAlignR, package="DIAlignR")
#' run1 <- "hroest_K120809_Strep0%PlasmaBiolRepl2_R04_SW_filt"
#' run2 <- "hroest_K120809_Strep10%PlasmaBiolRepl2_R04_SW_filt"
#' XICs.ref <- XIC_QFNNTDIVLLEDFQK_3_DIAlignR[[run1]][["4618"]]
#' XICs.eXp <- XIC_QFNNTDIVLLEDFQK_3_DIAlignR[[run2]][["4618"]]
#' RUNS_RT <- getRTdf(oswFiles_DIAlignR, ref = "run1", eXp = "run2", maxFdrGlobal = 0.05)
#' globalFit <- loess(RT.eXp ~ RT.ref, data = RUNS_RT, span = 0.1, control=loess.control(surface="direct"))
#' AlignObj <- getAlignObj(XICs.ref, XICs.eXp, globalFit, alignType = "hybrid", adaptiveRT = 77.82315,
#' normalization = "mean", simType = "dotProductMasked", goFactor = 0.125,
#' geFactor = 40, cosAngleThresh = 0.3, OverlapAlignment = TRUE, dotProdThresh = 0.96,
#' gapQuantile = 0.5, kerLen = 9L, hardConstrain = FALSE, samples4gradient = 100, objType = "light")
#' @export
getAlignObj <- function(XICs.ref, XICs.eXp, globalFit, alignType, adaptiveRT,
normalization, simType, goFactor, geFactor,
cosAngleThresh, OverlapAlignment,
dotProdThresh, gapQuantile, kerLen, hardConstrain,
samples4gradient, objType = "light"){
XICs.ref1 <- xicIntersect(XICs.ref) # Fixed common time in fragment-ions
XICs.eXp1 <- xicIntersect(XICs.eXp) # Fixed common time in fragment-ions
tVec.ref <- XICs.ref1[[1]][, "time"] # Extracting time component
tVec.eXp <- XICs.eXp1[[1]][, "time"] # Extracting time component
len <- length(tVec.ref)
B1p <- stats::predict(globalFit, data.frame("RT.ref" = tVec.ref[1]))[[1]]
B2p <- stats::predict(globalFit, data.frame("RT.ref" = tVec.ref[len]))[[1]]
# Set up constraints for penalizing similarity matrix
samplingTime <- (tVec.ref[len] - tVec.ref[1])/(len-1)
noBeef <- ceiling(adaptiveRT/samplingTime)
# Perform dynamic programming for chromatogram alignment
intensityList.ref <- lapply(XICs.ref1, `[`, i =, j = 2) # Extracting intensity values
intensityList.eXp <- lapply(XICs.eXp1, `[`, i =, j = 2) # Extracting intensity values
AlignObj <- alignChromatogramsCpp(intensityList.ref, intensityList.eXp,
alignType, tVec.ref, tVec.eXp,
normalization = normalization, simType = simType,
B1p = B1p, B2p = B2p, noBeef = noBeef,
goFactor = goFactor, geFactor = geFactor,
cosAngleThresh = cosAngleThresh, OverlapAlignment = OverlapAlignment,
dotProdThresh = dotProdThresh, gapQuantile = gapQuantile, kerLen = kerLen,
hardConstrain = hardConstrain, samples4gradient = samples4gradient,
objType = objType)
AlignObj
}
getAlignObj2 <- function(XICs.ref, XICs.eXp, globalFit, adaptiveRT, params,
objType = "light"){
XICs.ref1 <- xicIntersect(XICs.ref) # Fixed common time in fragment-ions
XICs.eXp1 <- xicIntersect(XICs.eXp) # Fixed common time in fragment-ions
tVec.ref <- XICs.ref1[[1]][, "time"] # Extracting time component
tVec.eXp <- XICs.eXp1[[1]][, "time"] # Extracting time component
len <- length(tVec.ref)
B1p <- getPredict(globalFit, tVec.ref[1], params[["globalAlignment"]])
B2p <- getPredict(globalFit, tVec.ref[len], params[["globalAlignment"]])
# Set up constraints for penalizing similarity matrix
samplingTime <- (tVec.ref[len] - tVec.ref[1])/(len-1)
noBeef <- ceiling(adaptiveRT/samplingTime)
# Perform dynamic programming for chromatogram alignment
intensityList.ref <- lapply(XICs.ref1, `[`, i =, j = 2) # Extracting intensity values
intensityList.eXp <- lapply(XICs.eXp1, `[`, i =, j = 2) # Extracting intensity values
AlignObj <- alignChromatogramsCpp(intensityList.ref, intensityList.eXp,
params[["alignType"]], tVec.ref, tVec.eXp,
params[["normalization"]], params[["simMeasure"]],
B1p = B1p, B2p = B2p, noBeef = noBeef,
params[["goFactor"]], params[["geFactor"]],
params[["cosAngleThresh"]], params[["OverlapAlignment"]],
params[["dotProdThresh"]], params[["gapQuantile"]], params[["kerLen"]],
params[["hardConstrain"]], params[["samples4gradient"]],
objType = objType)
AlignObj
}
#' Get mapping of reference RT on experiment run.
#'
#' This function aligns XICs of reference and experiment runs. Using alignment, it maps retention time from refernce run on experiment run.
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2019) + GPL-3
#' Date: 2019-12-13
#' @param refRT Peak's retention-time in reference run.
#' @param XICs.ref List of extracted ion chromatograms from reference run.
#' @param XICs.eXp List of extracted ion chromatograms from experiment run.
#' @param globalFit Linear or loess fit object between reference and experiment run.
#' @param alignType Available alignment methods are "global", "local" and "hybrid".
#' @param adaptiveRT (numeric) Similarity matrix is not penalized within adaptive RT.
#' @param normalization (character) Must be selected from "mean", "l2".
#' @param simMeasure (string) Must be selected from dotProduct, cosineAngle, crossCorrelation,
#' cosine2Angle, dotProductMasked, euclideanDist, covariance and correlation.
#' @param goFactor (numeric) Penalty for introducing first gap in alignment. This value is multiplied by base gap-penalty.
#' @param geFactor (numeric) Penalty for introducing subsequent gaps in alignment. This value is multiplied by base gap-penalty.
#' @param cosAngleThresh (numeric) In simType = dotProductMasked mode, angular similarity should be higher than cosAngleThresh otherwise similarity is forced to zero.
#' @param OverlapAlignment (logical) An input for alignment with free end-gaps. False: Global alignment, True: overlap alignment.
#' @param dotProdThresh (numeric) In simType = dotProductMasked mode, values in similarity matrix higher than dotProdThresh quantile are checked for angular similarity.
#' @param gapQuantile (numeric) Must be between 0 and 1. This is used to calculate base gap-penalty from similarity distribution.
#' @param kerLen (integer) In simType = crossCorrelation, length of the kernel used to sum similarity score. Must be an odd number.
#' @param hardConstrain (logical) If FALSE; indices farther from noBeef distance are filled with distance from linear fit line.
#' @param samples4gradient (numeric) This parameter modulates penalization of masked indices.
#' @param objType (char) Must be selected from light, medium and heavy.
#' @return (numeric)
#' @seealso \code{\link{alignChromatogramsCpp}}
#' @keywords internal
#' @examples
#' data(XIC_QFNNTDIVLLEDFQK_3_DIAlignR, package="DIAlignR")
#' data(oswFiles_DIAlignR, package="DIAlignR")
#' run1 <- "hroest_K120809_Strep0%PlasmaBiolRepl2_R04_SW_filt"
#' run2 <- "hroest_K120809_Strep10%PlasmaBiolRepl2_R04_SW_filt"
#' XICs.ref <- XIC_QFNNTDIVLLEDFQK_3_DIAlignR[[run1]][["4618"]]
#' XICs.eXp <- XIC_QFNNTDIVLLEDFQK_3_DIAlignR[[run2]][["4618"]]
#' RUNS_RT <- getRTdf(oswFiles_DIAlignR, ref = "run1", eXp = "run2", maxFdrGlobal = 0.05)
#' globalFit <- loess(RT.eXp ~ RT.ref, data = RUNS_RT, span = 0.1, control=loess.control(surface="direct"))
#' adaptiveRT <- 77.82315 #3.5*globalFit$s
#' \dontrun{
#' getMappedRT(refRT = 5238.35, XICs.ref, XICs.eXp, globalFit, alignType = "hybrid",
#' adaptiveRT = adaptiveRT, normalization = "mean",
#' simMeasure = "dotProductMasked", goFactor = 0.125, geFactor = 40, cosAngleThresh = 0.3,
#' OverlapAlignment = TRUE, dotProdThresh = 0.96, gapQuantile = 0.5, kerLen = 9L, hardConstrain = FALSE,
#' samples4gradient = 100)
#' }
getMappedRT <- function(refRT, XICs.ref, XICs.eXp, globalFit, alignType, adaptiveRT,
normalization, simMeasure, goFactor, geFactor, cosAngleThresh,
OverlapAlignment, dotProdThresh, gapQuantile, kerLen, hardConstrain,
samples4gradient, objType = "light"){
AlignObj <- getAlignObj(XICs.ref, XICs.eXp, globalFit, alignType, adaptiveRT,
normalization, simType = simMeasure, goFactor, geFactor,
cosAngleThresh, OverlapAlignment, dotProdThresh, gapQuantile,
kerLen, hardConstrain, samples4gradient, objType)
tVec.ref <- XICs.ref[[1]][["time"]] # Extracting time component
tVec.eXp <- XICs.eXp[[1]][["time"]] # Extracting time component
eXpRT <- mappedRTfromAlignObj(refRT, tVec.ref, tVec.eXp, AlignObj)
eXpRT
}
#' Get aligned Retention times.
#'
#' This function aligns XICs of reference and experiment runs.
#' It produces aligned retention times between refernce run and experiment run.
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2019) + GPL-3
#' Date: 2019-12-13
#' @param XICs.ref List of extracted ion chromatograms from reference run.
#' @param XICs.eXp List of extracted ion chromatograms from experiment run.
#' @param globalFit Linear or loess fit object between reference and experiment run.
#' @param alignType Available alignment methods are "global", "local" and "hybrid".
#' @param adaptiveRT (numeric) Similarity matrix is not penalized within adaptive RT.
#' @param normalization (character) Must be selected from "mean", "l2".
#' @param simMeasure (string) Must be selected from dotProduct, cosineAngle, crossCorrelation,
#' cosine2Angle, dotProductMasked, euclideanDist, covariance and correlation.
#' @param goFactor (numeric) Penalty for introducing first gap in alignment. This value is multiplied by base gap-penalty.
#' @param geFactor (numeric) Penalty for introducing subsequent gaps in alignment. This value is multiplied by base gap-penalty.
#' @param cosAngleThresh (numeric) In simType = dotProductMasked mode, angular similarity should be higher than cosAngleThresh otherwise similarity is forced to zero.
#' @param OverlapAlignment (logical) An input for alignment with free end-gaps. False: Global alignment, True: overlap alignment.
#' @param dotProdThresh (numeric) In simType = dotProductMasked mode, values in similarity matrix higher than dotProdThresh quantile are checked for angular similarity.
#' @param gapQuantile (numeric) Must be between 0 and 1. This is used to calculate base gap-penalty from similarity distribution.
#' @param kerLen (integer) In simType = crossCorrelation, length of the kernel used to sum similarity score. Must be an odd number.
#' @param hardConstrain (logical) If FALSE; indices farther from noBeef distance are filled with distance from linear fit line.
#' @param samples4gradient (numeric) This parameter modulates penalization of masked indices.
#' @param objType (char) Must be selected from light, medium and heavy.
#' @return (list) the first element corresponds to the aligned reference time, the second element is the aligned experiment time.
#' @seealso \code{\link{alignChromatogramsCpp}, \link{getAlignObj}}
#' @examples
#' data(XIC_QFNNTDIVLLEDFQK_3_DIAlignR, package="DIAlignR")
#' data(oswFiles_DIAlignR, package="DIAlignR")
#' run1 <- "hroest_K120809_Strep0%PlasmaBiolRepl2_R04_SW_filt"
#' run2 <- "hroest_K120809_Strep10%PlasmaBiolRepl2_R04_SW_filt"
#' XICs.ref <- XIC_QFNNTDIVLLEDFQK_3_DIAlignR[[run1]][["4618"]]
#' XICs.eXp <- XIC_QFNNTDIVLLEDFQK_3_DIAlignR[[run2]][["4618"]]
#' RUNS_RT <- getRTdf(oswFiles_DIAlignR, ref = "run1", eXp = "run2", maxFdrGlobal = 0.05)
#' globalFit <- loess(RT.eXp ~ RT.ref, data = RUNS_RT, span = 0.1, control=loess.control(surface="direct"))
#' adaptiveRT <- 77.82315 #3.5*globalFit$s
#' getAlignedTimes(XICs.ref, XICs.eXp, globalFit, alignType = "hybrid",
#' adaptiveRT = adaptiveRT, normalization = "mean",
#' simMeasure = "dotProductMasked", goFactor = 0.125, geFactor = 40, cosAngleThresh = 0.3,
#' OverlapAlignment = TRUE, dotProdThresh = 0.96, gapQuantile = 0.5, kerLen = 9L, hardConstrain = FALSE,
#' samples4gradient = 100)
#' @export
getAlignedTimes <- function(XICs.ref, XICs.eXp, globalFit, alignType, adaptiveRT,
normalization, simMeasure, goFactor, geFactor, cosAngleThresh,
OverlapAlignment, dotProdThresh, gapQuantile, kerLen, hardConstrain,
samples4gradient, objType = "light"){
alignedIndices <- getAlignedIndices(XICs.ref, XICs.eXp, globalFit, alignType, adaptiveRT,
normalization, simMeasure, goFactor, geFactor,
cosAngleThresh, OverlapAlignment, dotProdThresh, gapQuantile,
kerLen, hardConstrain, samples4gradient, objType)
keep <- !is.na(alignedIndices[,"indexAligned.ref"])
tVec.ref <- XICs.ref[[1]][,"time"] # Extracting time component
tVec.eXp <- XICs.eXp[[1]][, "time"] # Extracting time component
tAligned.ref <- mapIdxToTime(tVec.ref, alignedIndices[,"indexAligned.ref"])
tAligned.eXp <- mapIdxToTime(tVec.eXp, alignedIndices[,"indexAligned.eXp"])
tAligned.ref <- tAligned.ref[keep]
tAligned.eXp <- tAligned.eXp[keep]
list(tAligned.ref, tAligned.eXp)
}
#' Get aligned Retention times.
#'
#' This function aligns XICs of reference and experiment runs.
#' It produces aligned retention times between refernce run and experiment run.
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2021) + GPL-3
#' Date: 2021-01-02
#' @inheritParams checkParams
#' @param XICs.ref List of extracted ion chromatograms from reference run.
#' @param XICs.eXp List of extracted ion chromatograms from experiment run.
#' @param globalFit Linear or loess fit object between reference and experiment run.
#' @param adaptiveRT (numeric) Similarity matrix is not penalized within adaptive RT.
#' @return (matrix) the first column corresponds to the aligned reference time, the second column is the aligned experiment time.
#' @seealso \code{\link{alignChromatogramsCpp}, \link{getAlignObj}}
#' @examples
#' data(XIC_QFNNTDIVLLEDFQK_3_DIAlignR, package="DIAlignR")
#' data(oswFiles_DIAlignR, package="DIAlignR")
#' run1 <- "hroest_K120809_Strep0%PlasmaBiolRepl2_R04_SW_filt"
#' run2 <- "hroest_K120809_Strep10%PlasmaBiolRepl2_R04_SW_filt"
#' XICs.ref <- lapply(XIC_QFNNTDIVLLEDFQK_3_DIAlignR[[run1]][["4618"]], as.matrix)
#' XICs.eXp <- lapply(XIC_QFNNTDIVLLEDFQK_3_DIAlignR[[run2]][["4618"]], as.matrix)
#' params <- paramsDIAlignR()
#' params[["globalAlignment"]] <- "linear"
#' globalFit <- getGlobalAlignment(oswFiles_DIAlignR, ref = "run2", eXp = "run0",
#' fitType = params[["globalAlignment"]], maxFdrGlobal = 0.05, spanvalue = 0.1)
#' adaptiveRT <- 77.82315 #3.5*getRSE(globalFit, params[["globalAlignment"]])
#' globalFit <- coef(globalFit)
#' getAlignedTimesFast(XICs.ref, XICs.eXp, globalFit, adaptiveRT, params)
#' @export
getAlignedTimesFast <- function(XICs.ref, XICs.eXp, globalFit, adaptiveRT, params){
B1p <- getPredict(globalFit, XICs.ref[[1]][1,1], params[["globalAlignment"]])
len <- nrow(XICs.ref[[1]])
B2p <- getPredict(globalFit, XICs.ref[[1]][len,1], params[["globalAlignment"]])
if(is.na(B1p) || is.na(B2p)){
B1p <- XICs.eXp[[1]][1,1]
B2p <- XICs.eXp[[1]][nrow(XICs.eXp[[1]]),1]
}
tAligned <- getAlignedTimesCpp(XICs.ref, XICs.eXp, params[["kernelLen"]], params[["polyOrd"]], params[["alignType"]],
adaptiveRT, params[["normalization"]], params[["simMeasure"]],
B1p = B1p, B2p = B2p, params[["goFactor"]], params[["geFactor"]], params[["cosAngleThresh"]],
params[["OverlapAlignment"]], params[["dotProdThresh"]], params[["gapQuantile"]], 9L,
params[["hardConstrain"]], params[["samples4gradient"]])
tAligned
}
#' Get aligned indices.
#'
#' This function aligns XICs of reference and experiment runs.
#' It produces aligned indices between refernce run and experiment run.
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2020) + GPL-3
#' Date: 2020-06-07
#' @param XICs.ref List of extracted ion chromatograms from reference run.
#' @param XICs.eXp List of extracted ion chromatograms from experiment run.
#' @param globalFit Linear or loess fit object between reference and experiment run.
#' @param alignType Available alignment methods are "global", "local" and "hybrid".
#' @param adaptiveRT (numeric) Similarity matrix is not penalized within adaptive RT.
#' @param normalization (character) Must be selected from "mean", "l2".
#' @param simMeasure (string) Must be selected from dotProduct, cosineAngle, crossCorrelation,
#' cosine2Angle, dotProductMasked, euclideanDist, covariance and correlation.
#' @param goFactor (numeric) Penalty for introducing first gap in alignment. This value is multiplied by base gap-penalty.
#' @param geFactor (numeric) Penalty for introducing subsequent gaps in alignment. This value is multiplied by base gap-penalty.
#' @param cosAngleThresh (numeric) In simType = dotProductMasked mode, angular similarity should be higher than cosAngleThresh otherwise similarity is forced to zero.
#' @param OverlapAlignment (logical) An input for alignment with free end-gaps. False: Global alignment, True: overlap alignment.
#' @param dotProdThresh (numeric) In simType = dotProductMasked mode, values in similarity matrix higher than dotProdThresh quantile are checked for angular similarity.
#' @param gapQuantile (numeric) Must be between 0 and 1. This is used to calculate base gap-penalty from similarity distribution.
#' @param kerLen (integer) In simType = crossCorrelation, length of the kernel used to sum similarity score. Must be an odd number.
#' @param hardConstrain (logical) If FALSE; indices farther from noBeef distance are filled with distance from linear fit line.
#' @param samples4gradient (numeric) This parameter modulates penalization of masked indices.
#' @param objType (char) Must be selected from light, medium and heavy.
#' @return (data-frame) Aligned indices of reference and experiment runs. Gaps are introduced as NA.
#' @seealso \code{\link{alignChromatogramsCpp}, \link{getAlignObj}}
#' @keywords internal
#' @examples
#' data(XIC_QFNNTDIVLLEDFQK_3_DIAlignR, package="DIAlignR")
#' data(oswFiles_DIAlignR, package="DIAlignR")
#' run1 <- "hroest_K120809_Strep0%PlasmaBiolRepl2_R04_SW_filt"
#' run2 <- "hroest_K120809_Strep10%PlasmaBiolRepl2_R04_SW_filt"
#' XICs.ref <- XIC_QFNNTDIVLLEDFQK_3_DIAlignR[[run1]][["4618"]]
#' XICs.eXp <- XIC_QFNNTDIVLLEDFQK_3_DIAlignR[[run2]][["4618"]]
#' globalFit <- getGlobalAlignment(oswFiles_DIAlignR, ref = "run1", eXp = "run2",
#' fitType = "loess", maxFdrGlobal = 0.05, spanvalue = 0.1)
#' adaptiveRT <- 77.82315 #3.5*globalFit$s
#' \dontrun{
#' getAlignedIndices(XICs.ref, XICs.eXp, globalFit, alignType = "hybrid",
#' adaptiveRT = adaptiveRT, normalization = "mean",
#' simMeasure = "dotProductMasked", goFactor = 0.125, geFactor = 40, cosAngleThresh = 0.3,
#' OverlapAlignment = TRUE, dotProdThresh = 0.96, gapQuantile = 0.5, kerLen = 9L, hardConstrain = FALSE,
#' samples4gradient = 100)
#' }
getAlignedIndices <- function(XICs.ref, XICs.eXp, globalFit, alignType, adaptiveRT,
normalization, simMeasure, goFactor, geFactor, cosAngleThresh,
OverlapAlignment, dotProdThresh, gapQuantile, kerLen, hardConstrain,
samples4gradient, objType = "light"){
alignObj <- getAlignObj(XICs.ref, XICs.eXp, globalFit, alignType, adaptiveRT,
normalization, simType = simMeasure, goFactor, geFactor,
cosAngleThresh, OverlapAlignment, dotProdThresh, gapQuantile,
kerLen, hardConstrain, samples4gradient, objType)
alignedIndices <- cbind(alignObj@indexA_aligned,
alignObj@indexB_aligned,
alignObj@score)
colnames(alignedIndices) <- c("indexAligned.ref", "indexAligned.eXp", "score")
alignedIndices[, 1:2][alignedIndices[, 1:2] == 0] <- NA_integer_
alignedIndices
}
alignedTimes2 <- function(alignObj, XICs.ref, XICs.eXp){
alignedIndices <- cbind(alignObj@indexA_aligned,
alignObj@indexB_aligned,
alignObj@score)
colnames(alignedIndices) <- c("indexAligned.ref", "indexAligned.eXp", "score")
alignedIndices[, 1:2][alignedIndices[, 1:2] == 0] <- NA_integer_
keep <- !is.na(alignedIndices[,"indexAligned.ref"])
tVec.ref <- XICs.ref[[1]][["time"]] # Extracting time component
tVec.eXp <- XICs.eXp[[1]][["time"]] # Extracting time component
tAligned.ref <- mapIdxToTime(tVec.ref, alignedIndices[,"indexAligned.ref"])
tAligned.eXp <- mapIdxToTime(tVec.eXp, alignedIndices[,"indexAligned.eXp"])
tAligned.ref <- tAligned.ref[keep]
tAligned.eXp <- tAligned.eXp[keep]
data.frame(tAligned.ref, tAligned.eXp)
}
Roestlab/DIAlignR documentation built on March 3, 2021, 9:09 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions alignedTimes2 getAlignedIndices getAlignedTimesFast getAlignedTimes getMappedRT getAlignObj2 getAlignObj
Documented in getAlignedIndices getAlignedTimes getAlignedTimesFast getAlignObj getMappedRT
#' Outputs AlignObj from an alignment of two XIC-groups
#'
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2019) + GPL-3
#' Date: 2019-12-13
#'
#' @param XICs.ref List of extracted ion chromatograms from reference run.
#' @param XICs.eXp List of extracted ion chromatograms from experiment run.
#' @param globalFit Linear or loess fit object between reference and experiment run.
#' @param alignType Available alignment methods are "global", "local" and "hybrid".
#' @param adaptiveRT (numeric) Similarity matrix is not penalized within adaptive RT.
#' @param normalization (character) Must be selected from "mean", "l2".
#' @param simType (string) Must be selected from dotProduct, cosineAngle, crossCorrelation,
#' cosine2Angle, dotProductMasked, euclideanDist, covariance and correlation.
#' @param goFactor (numeric) Penalty for introducing first gap in alignment. This value is multiplied by base gap-penalty.
#' @param geFactor (numeric) Penalty for introducing subsequent gaps in alignment. This value is multiplied by base gap-penalty.
#' @param cosAngleThresh (numeric) In simType = dotProductMasked mode, angular similarity should be higher than cosAngleThresh otherwise similarity is forced to zero.
#' @param OverlapAlignment (logical) An input for alignment with free end-gaps. False: Global alignment, True: overlap alignment.
#' @param dotProdThresh (numeric) In simType = dotProductMasked mode, values in similarity matrix higher than dotProdThresh quantile are checked for angular similarity.
#' @param gapQuantile (numeric) Must be between 0 and 1. This is used to calculate base gap-penalty from similarity distribution.
#' @param kerLen (integer) In simType = crossCorrelation, length of the kernel used to sum similarity score. Must be an odd number.
#' @param hardConstrain (logical) If FALSE; indices farther from noBeef distance are filled with distance from linear fit line.
#' @param samples4gradient (numeric) This parameter modulates penalization of masked indices.
#' @param objType (char) Must be selected from light, medium and heavy.
#' @return A S4 object. Three most-important slots are:
#' \item{indexA_aligned}{(integer) aligned indices of reference run.}
#' \item{indexB_aligned}{(integer) aligned indices of experiment run.}
#' \item{score}{(numeric) cumulative score of alignment.}
#'
#' @seealso \code{\link{alignChromatogramsCpp}}
#' @examples
#' data(XIC_QFNNTDIVLLEDFQK_3_DIAlignR, package="DIAlignR")
#' data(oswFiles_DIAlignR, package="DIAlignR")
#' run1 <- "hroest_K120809_Strep0%PlasmaBiolRepl2_R04_SW_filt"
#' run2 <- "hroest_K120809_Strep10%PlasmaBiolRepl2_R04_SW_filt"
#' XICs.ref <- XIC_QFNNTDIVLLEDFQK_3_DIAlignR[[run1]][["4618"]]
#' XICs.eXp <- XIC_QFNNTDIVLLEDFQK_3_DIAlignR[[run2]][["4618"]]
#' RUNS_RT <- getRTdf(oswFiles_DIAlignR, ref = "run1", eXp = "run2", maxFdrGlobal = 0.05)
#' globalFit <- loess(RT.eXp ~ RT.ref, data = RUNS_RT, span = 0.1, control=loess.control(surface="direct"))
#' AlignObj <- getAlignObj(XICs.ref, XICs.eXp, globalFit, alignType = "hybrid", adaptiveRT = 77.82315,
#' normalization = "mean", simType = "dotProductMasked", goFactor = 0.125,
#' geFactor = 40, cosAngleThresh = 0.3, OverlapAlignment = TRUE, dotProdThresh = 0.96,
#' gapQuantile = 0.5, kerLen = 9L, hardConstrain = FALSE, samples4gradient = 100, objType = "light")
#' @export
getAlignObj <- function(XICs.ref, XICs.eXp, globalFit, alignType, adaptiveRT,
normalization, simType, goFactor, geFactor,
cosAngleThresh, OverlapAlignment,
dotProdThresh, gapQuantile, kerLen, hardConstrain,
samples4gradient, objType = "light"){
XICs.ref1 <- xicIntersect(XICs.ref) # Fixed common time in fragment-ions
XICs.eXp1 <- xicIntersect(XICs.eXp) # Fixed common time in fragment-ions
tVec.ref <- XICs.ref1[[1]][, "time"] # Extracting time component
tVec.eXp <- XICs.eXp1[[1]][, "time"] # Extracting time component
len <- length(tVec.ref)
B1p <- stats::predict(globalFit, data.frame("RT.ref" = tVec.ref[1]))[[1]]
B2p <- stats::predict(globalFit, data.frame("RT.ref" = tVec.ref[len]))[[1]]
# Set up constraints for penalizing similarity matrix
samplingTime <- (tVec.ref[len] - tVec.ref[1])/(len-1)
noBeef <- ceiling(adaptiveRT/samplingTime)
# Perform dynamic programming for chromatogram alignment
intensityList.ref <- lapply(XICs.ref1, `[`, i =, j = 2) # Extracting intensity values
intensityList.eXp <- lapply(XICs.eXp1, `[`, i =, j = 2) # Extracting intensity values
AlignObj <- alignChromatogramsCpp(intensityList.ref, intensityList.eXp,
alignType, tVec.ref, tVec.eXp,
normalization = normalization, simType = simType,
B1p = B1p, B2p = B2p, noBeef = noBeef,
goFactor = goFactor, geFactor = geFactor,
cosAngleThresh = cosAngleThresh, OverlapAlignment = OverlapAlignment,
dotProdThresh = dotProdThresh, gapQuantile = gapQuantile, kerLen = kerLen,
hardConstrain = hardConstrain, samples4gradient = samples4gradient,
objType = objType)
AlignObj
}
getAlignObj2 <- function(XICs.ref, XICs.eXp, globalFit, adaptiveRT, params,
objType = "light"){
XICs.ref1 <- xicIntersect(XICs.ref) # Fixed common time in fragment-ions
XICs.eXp1 <- xicIntersect(XICs.eXp) # Fixed common time in fragment-ions
tVec.ref <- XICs.ref1[[1]][, "time"] # Extracting time component
tVec.eXp <- XICs.eXp1[[1]][, "time"] # Extracting time component
len <- length(tVec.ref)
B1p <- getPredict(globalFit, tVec.ref[1], params[["globalAlignment"]])
B2p <- getPredict(globalFit, tVec.ref[len], params[["globalAlignment"]])
# Set up constraints for penalizing similarity matrix
samplingTime <- (tVec.ref[len] - tVec.ref[1])/(len-1)
noBeef <- ceiling(adaptiveRT/samplingTime)
# Perform dynamic programming for chromatogram alignment
intensityList.ref <- lapply(XICs.ref1, `[`, i =, j = 2) # Extracting intensity values
intensityList.eXp <- lapply(XICs.eXp1, `[`, i =, j = 2) # Extracting intensity values
AlignObj <- alignChromatogramsCpp(intensityList.ref, intensityList.eXp,
params[["alignType"]], tVec.ref, tVec.eXp,
params[["normalization"]], params[["simMeasure"]],
B1p = B1p, B2p = B2p, noBeef = noBeef,
params[["goFactor"]], params[["geFactor"]],
params[["cosAngleThresh"]], params[["OverlapAlignment"]],
params[["dotProdThresh"]], params[["gapQuantile"]], params[["kerLen"]],
params[["hardConstrain"]], params[["samples4gradient"]],
objType = objType)
AlignObj
}
#' Get mapping of reference RT on experiment run.
#'
#' This function aligns XICs of reference and experiment runs. Using alignment, it maps retention time from refernce run on experiment run.
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2019) + GPL-3
#' Date: 2019-12-13
#' @param refRT Peak's retention-time in reference run.
#' @param XICs.ref List of extracted ion chromatograms from reference run.
#' @param XICs.eXp List of extracted ion chromatograms from experiment run.
#' @param globalFit Linear or loess fit object between reference and experiment run.
#' @param alignType Available alignment methods are "global", "local" and "hybrid".
#' @param adaptiveRT (numeric) Similarity matrix is not penalized within adaptive RT.
#' @param normalization (character) Must be selected from "mean", "l2".
#' @param simMeasure (string) Must be selected from dotProduct, cosineAngle, crossCorrelation,
#' cosine2Angle, dotProductMasked, euclideanDist, covariance and correlation.
#' @param goFactor (numeric) Penalty for introducing first gap in alignment. This value is multiplied by base gap-penalty.
#' @param geFactor (numeric) Penalty for introducing subsequent gaps in alignment. This value is multiplied by base gap-penalty.
#' @param cosAngleThresh (numeric) In simType = dotProductMasked mode, angular similarity should be higher than cosAngleThresh otherwise similarity is forced to zero.
#' @param OverlapAlignment (logical) An input for alignment with free end-gaps. False: Global alignment, True: overlap alignment.
#' @param dotProdThresh (numeric) In simType = dotProductMasked mode, values in similarity matrix higher than dotProdThresh quantile are checked for angular similarity.
#' @param gapQuantile (numeric) Must be between 0 and 1. This is used to calculate base gap-penalty from similarity distribution.
#' @param kerLen (integer) In simType = crossCorrelation, length of the kernel used to sum similarity score. Must be an odd number.
#' @param hardConstrain (logical) If FALSE; indices farther from noBeef distance are filled with distance from linear fit line.
#' @param samples4gradient (numeric) This parameter modulates penalization of masked indices.
#' @param objType (char) Must be selected from light, medium and heavy.
#' @return (numeric)
#' @seealso \code{\link{alignChromatogramsCpp}}
#' @keywords internal
#' @examples
#' data(XIC_QFNNTDIVLLEDFQK_3_DIAlignR, package="DIAlignR")
#' data(oswFiles_DIAlignR, package="DIAlignR")
#' run1 <- "hroest_K120809_Strep0%PlasmaBiolRepl2_R04_SW_filt"
#' run2 <- "hroest_K120809_Strep10%PlasmaBiolRepl2_R04_SW_filt"
#' XICs.ref <- XIC_QFNNTDIVLLEDFQK_3_DIAlignR[[run1]][["4618"]]
#' XICs.eXp <- XIC_QFNNTDIVLLEDFQK_3_DIAlignR[[run2]][["4618"]]
#' RUNS_RT <- getRTdf(oswFiles_DIAlignR, ref = "run1", eXp = "run2", maxFdrGlobal = 0.05)
#' globalFit <- loess(RT.eXp ~ RT.ref, data = RUNS_RT, span = 0.1, control=loess.control(surface="direct"))
#' adaptiveRT <- 77.82315 #3.5*globalFit$s
#' \dontrun{
#' getMappedRT(refRT = 5238.35, XICs.ref, XICs.eXp, globalFit, alignType = "hybrid",
#' adaptiveRT = adaptiveRT, normalization = "mean",
#' simMeasure = "dotProductMasked", goFactor = 0.125, geFactor = 40, cosAngleThresh = 0.3,
#' OverlapAlignment = TRUE, dotProdThresh = 0.96, gapQuantile = 0.5, kerLen = 9L, hardConstrain = FALSE,
#' samples4gradient = 100)
#' }
getMappedRT <- function(refRT, XICs.ref, XICs.eXp, globalFit, alignType, adaptiveRT,
normalization, simMeasure, goFactor, geFactor, cosAngleThresh,
OverlapAlignment, dotProdThresh, gapQuantile, kerLen, hardConstrain,
samples4gradient, objType = "light"){
AlignObj <- getAlignObj(XICs.ref, XICs.eXp, globalFit, alignType, adaptiveRT,
normalization, simType = simMeasure, goFactor, geFactor,
cosAngleThresh, OverlapAlignment, dotProdThresh, gapQuantile,
kerLen, hardConstrain, samples4gradient, objType)
tVec.ref <- XICs.ref[[1]][["time"]] # Extracting time component
tVec.eXp <- XICs.eXp[[1]][["time"]] # Extracting time component
eXpRT <- mappedRTfromAlignObj(refRT, tVec.ref, tVec.eXp, AlignObj)
eXpRT
}
#' Get aligned Retention times.
#'
#' This function aligns XICs of reference and experiment runs.
#' It produces aligned retention times between refernce run and experiment run.
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2019) + GPL-3
#' Date: 2019-12-13
#' @param XICs.ref List of extracted ion chromatograms from reference run.
#' @param XICs.eXp List of extracted ion chromatograms from experiment run.
#' @param globalFit Linear or loess fit object between reference and experiment run.
#' @param alignType Available alignment methods are "global", "local" and "hybrid".
#' @param adaptiveRT (numeric) Similarity matrix is not penalized within adaptive RT.
#' @param normalization (character) Must be selected from "mean", "l2".
#' @param simMeasure (string) Must be selected from dotProduct, cosineAngle, crossCorrelation,
#' cosine2Angle, dotProductMasked, euclideanDist, covariance and correlation.
#' @param goFactor (numeric) Penalty for introducing first gap in alignment. This value is multiplied by base gap-penalty.
#' @param geFactor (numeric) Penalty for introducing subsequent gaps in alignment. This value is multiplied by base gap-penalty.
#' @param cosAngleThresh (numeric) In simType = dotProductMasked mode, angular similarity should be higher than cosAngleThresh otherwise similarity is forced to zero.
#' @param OverlapAlignment (logical) An input for alignment with free end-gaps. False: Global alignment, True: overlap alignment.
#' @param dotProdThresh (numeric) In simType = dotProductMasked mode, values in similarity matrix higher than dotProdThresh quantile are checked for angular similarity.
#' @param gapQuantile (numeric) Must be between 0 and 1. This is used to calculate base gap-penalty from similarity distribution.
#' @param kerLen (integer) In simType = crossCorrelation, length of the kernel used to sum similarity score. Must be an odd number.
#' @param hardConstrain (logical) If FALSE; indices farther from noBeef distance are filled with distance from linear fit line.
#' @param samples4gradient (numeric) This parameter modulates penalization of masked indices.
#' @param objType (char) Must be selected from light, medium and heavy.
#' @return (list) the first element corresponds to the aligned reference time, the second element is the aligned experiment time.
#' @seealso \code{\link{alignChromatogramsCpp}, \link{getAlignObj}}
#' @examples
#' data(XIC_QFNNTDIVLLEDFQK_3_DIAlignR, package="DIAlignR")
#' data(oswFiles_DIAlignR, package="DIAlignR")
#' run1 <- "hroest_K120809_Strep0%PlasmaBiolRepl2_R04_SW_filt"
#' run2 <- "hroest_K120809_Strep10%PlasmaBiolRepl2_R04_SW_filt"
#' XICs.ref <- XIC_QFNNTDIVLLEDFQK_3_DIAlignR[[run1]][["4618"]]
#' XICs.eXp <- XIC_QFNNTDIVLLEDFQK_3_DIAlignR[[run2]][["4618"]]
#' RUNS_RT <- getRTdf(oswFiles_DIAlignR, ref = "run1", eXp = "run2", maxFdrGlobal = 0.05)
#' globalFit <- loess(RT.eXp ~ RT.ref, data = RUNS_RT, span = 0.1, control=loess.control(surface="direct"))
#' adaptiveRT <- 77.82315 #3.5*globalFit$s
#' getAlignedTimes(XICs.ref, XICs.eXp, globalFit, alignType = "hybrid",
#' adaptiveRT = adaptiveRT, normalization = "mean",
#' simMeasure = "dotProductMasked", goFactor = 0.125, geFactor = 40, cosAngleThresh = 0.3,
#' OverlapAlignment = TRUE, dotProdThresh = 0.96, gapQuantile = 0.5, kerLen = 9L, hardConstrain = FALSE,
#' samples4gradient = 100)
#' @export
getAlignedTimes <- function(XICs.ref, XICs.eXp, globalFit, alignType, adaptiveRT,
normalization, simMeasure, goFactor, geFactor, cosAngleThresh,
OverlapAlignment, dotProdThresh, gapQuantile, kerLen, hardConstrain,
samples4gradient, objType = "light"){
alignedIndices <- getAlignedIndices(XICs.ref, XICs.eXp, globalFit, alignType, adaptiveRT,
normalization, simMeasure, goFactor, geFactor,
cosAngleThresh, OverlapAlignment, dotProdThresh, gapQuantile,
kerLen, hardConstrain, samples4gradient, objType)
keep <- !is.na(alignedIndices[,"indexAligned.ref"])
tVec.ref <- XICs.ref[[1]][,"time"] # Extracting time component
tVec.eXp <- XICs.eXp[[1]][, "time"] # Extracting time component
tAligned.ref <- mapIdxToTime(tVec.ref, alignedIndices[,"indexAligned.ref"])
tAligned.eXp <- mapIdxToTime(tVec.eXp, alignedIndices[,"indexAligned.eXp"])
tAligned.ref <- tAligned.ref[keep]
tAligned.eXp <- tAligned.eXp[keep]
list(tAligned.ref, tAligned.eXp)
}
#' Get aligned Retention times.
#'
#' This function aligns XICs of reference and experiment runs.
#' It produces aligned retention times between refernce run and experiment run.
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2021) + GPL-3
#' Date: 2021-01-02
#' @inheritParams checkParams
#' @param XICs.ref List of extracted ion chromatograms from reference run.
#' @param XICs.eXp List of extracted ion chromatograms from experiment run.
#' @param globalFit Linear or loess fit object between reference and experiment run.
#' @param adaptiveRT (numeric) Similarity matrix is not penalized within adaptive RT.
#' @return (matrix) the first column corresponds to the aligned reference time, the second column is the aligned experiment time.
#' @seealso \code{\link{alignChromatogramsCpp}, \link{getAlignObj}}
#' @examples
#' data(XIC_QFNNTDIVLLEDFQK_3_DIAlignR, package="DIAlignR")
#' data(oswFiles_DIAlignR, package="DIAlignR")
#' run1 <- "hroest_K120809_Strep0%PlasmaBiolRepl2_R04_SW_filt"
#' run2 <- "hroest_K120809_Strep10%PlasmaBiolRepl2_R04_SW_filt"
#' XICs.ref <- lapply(XIC_QFNNTDIVLLEDFQK_3_DIAlignR[[run1]][["4618"]], as.matrix)
#' XICs.eXp <- lapply(XIC_QFNNTDIVLLEDFQK_3_DIAlignR[[run2]][["4618"]], as.matrix)
#' params <- paramsDIAlignR()
#' params[["globalAlignment"]] <- "linear"
#' globalFit <- getGlobalAlignment(oswFiles_DIAlignR, ref = "run2", eXp = "run0",
#' fitType = params[["globalAlignment"]], maxFdrGlobal = 0.05, spanvalue = 0.1)
#' adaptiveRT <- 77.82315 #3.5*getRSE(globalFit, params[["globalAlignment"]])
#' globalFit <- coef(globalFit)
#' getAlignedTimesFast(XICs.ref, XICs.eXp, globalFit, adaptiveRT, params)
#' @export
getAlignedTimesFast <- function(XICs.ref, XICs.eXp, globalFit, adaptiveRT, params){
B1p <- getPredict(globalFit, XICs.ref[[1]][1,1], params[["globalAlignment"]])
len <- nrow(XICs.ref[[1]])
B2p <- getPredict(globalFit, XICs.ref[[1]][len,1], params[["globalAlignment"]])
if(is.na(B1p) || is.na(B2p)){
B1p <- XICs.eXp[[1]][1,1]
B2p <- XICs.eXp[[1]][nrow(XICs.eXp[[1]]),1]
}
tAligned <- getAlignedTimesCpp(XICs.ref, XICs.eXp, params[["kernelLen"]], params[["polyOrd"]], params[["alignType"]],
adaptiveRT, params[["normalization"]], params[["simMeasure"]],
B1p = B1p, B2p = B2p, params[["goFactor"]], params[["geFactor"]], params[["cosAngleThresh"]],
params[["OverlapAlignment"]], params[["dotProdThresh"]], params[["gapQuantile"]], 9L,
params[["hardConstrain"]], params[["samples4gradient"]])
tAligned
}
#' Get aligned indices.
#'
#' This function aligns XICs of reference and experiment runs.
#' It produces aligned indices between refernce run and experiment run.
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2020) + GPL-3
#' Date: 2020-06-07
#' @param XICs.ref List of extracted ion chromatograms from reference run.
#' @param XICs.eXp List of extracted ion chromatograms from experiment run.
#' @param globalFit Linear or loess fit object between reference and experiment run.
#' @param alignType Available alignment methods are "global", "local" and "hybrid".
#' @param adaptiveRT (numeric) Similarity matrix is not penalized within adaptive RT.
#' @param normalization (character) Must be selected from "mean", "l2".
#' @param simMeasure (string) Must be selected from dotProduct, cosineAngle, crossCorrelation,
#' cosine2Angle, dotProductMasked, euclideanDist, covariance and correlation.
#' @param goFactor (numeric) Penalty for introducing first gap in alignment. This value is multiplied by base gap-penalty.
#' @param geFactor (numeric) Penalty for introducing subsequent gaps in alignment. This value is multiplied by base gap-penalty.
#' @param cosAngleThresh (numeric) In simType = dotProductMasked mode, angular similarity should be higher than cosAngleThresh otherwise similarity is forced to zero.
#' @param OverlapAlignment (logical) An input for alignment with free end-gaps. False: Global alignment, True: overlap alignment.
#' @param dotProdThresh (numeric) In simType = dotProductMasked mode, values in similarity matrix higher than dotProdThresh quantile are checked for angular similarity.
#' @param gapQuantile (numeric) Must be between 0 and 1. This is used to calculate base gap-penalty from similarity distribution.
#' @param kerLen (integer) In simType = crossCorrelation, length of the kernel used to sum similarity score. Must be an odd number.
#' @param hardConstrain (logical) If FALSE; indices farther from noBeef distance are filled with distance from linear fit line.
#' @param samples4gradient (numeric) This parameter modulates penalization of masked indices.
#' @param objType (char) Must be selected from light, medium and heavy.
#' @return (data-frame) Aligned indices of reference and experiment runs. Gaps are introduced as NA.
#' @seealso \code{\link{alignChromatogramsCpp}, \link{getAlignObj}}
#' @keywords internal
#' @examples
#' data(XIC_QFNNTDIVLLEDFQK_3_DIAlignR, package="DIAlignR")
#' data(oswFiles_DIAlignR, package="DIAlignR")
#' run1 <- "hroest_K120809_Strep0%PlasmaBiolRepl2_R04_SW_filt"
#' run2 <- "hroest_K120809_Strep10%PlasmaBiolRepl2_R04_SW_filt"
#' XICs.ref <- XIC_QFNNTDIVLLEDFQK_3_DIAlignR[[run1]][["4618"]]
#' XICs.eXp <- XIC_QFNNTDIVLLEDFQK_3_DIAlignR[[run2]][["4618"]]
#' globalFit <- getGlobalAlignment(oswFiles_DIAlignR, ref = "run1", eXp = "run2",
#' fitType = "loess", maxFdrGlobal = 0.05, spanvalue = 0.1)
#' adaptiveRT <- 77.82315 #3.5*globalFit$s
#' \dontrun{
#' getAlignedIndices(XICs.ref, XICs.eXp, globalFit, alignType = "hybrid",
#' adaptiveRT = adaptiveRT, normalization = "mean",
#' simMeasure = "dotProductMasked", goFactor = 0.125, geFactor = 40, cosAngleThresh = 0.3,
#' OverlapAlignment = TRUE, dotProdThresh = 0.96, gapQuantile = 0.5, kerLen = 9L, hardConstrain = FALSE,
#' samples4gradient = 100)
#' }
getAlignedIndices <- function(XICs.ref, XICs.eXp, globalFit, alignType, adaptiveRT,
normalization, simMeasure, goFactor, geFactor, cosAngleThresh,
OverlapAlignment, dotProdThresh, gapQuantile, kerLen, hardConstrain,
samples4gradient, objType = "light"){
alignObj <- getAlignObj(XICs.ref, XICs.eXp, globalFit, alignType, adaptiveRT,
normalization, simType = simMeasure, goFactor, geFactor,
cosAngleThresh, OverlapAlignment, dotProdThresh, gapQuantile,
kerLen, hardConstrain, samples4gradient, objType)
alignedIndices <- cbind(alignObj@indexA_aligned,
alignObj@indexB_aligned,
alignObj@score)
colnames(alignedIndices) <- c("indexAligned.ref", "indexAligned.eXp", "score")
alignedIndices[, 1:2][alignedIndices[, 1:2] == 0] <- NA_integer_
alignedIndices
}
alignedTimes2 <- function(alignObj, XICs.ref, XICs.eXp){
alignedIndices <- cbind(alignObj@indexA_aligned,
alignObj@indexB_aligned,
alignObj@score)
colnames(alignedIndices) <- c("indexAligned.ref", "indexAligned.eXp", "score")
alignedIndices[, 1:2][alignedIndices[, 1:2] == 0] <- NA_integer_
keep <- !is.na(alignedIndices[,"indexAligned.ref"])
tVec.ref <- XICs.ref[[1]][["time"]] # Extracting time component
tVec.eXp <- XICs.eXp[[1]][["time"]] # Extracting time component
tAligned.ref <- mapIdxToTime(tVec.ref, alignedIndices[,"indexAligned.ref"])
tAligned.eXp <- mapIdxToTime(tVec.eXp, alignedIndices[,"indexAligned.eXp"])
tAligned.ref <- tAligned.ref[keep]
tAligned.eXp <- tAligned.eXp[keep]
data.frame(tAligned.ref, tAligned.eXp)
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.