tests/testthat/test_merge_order.R
In Roestlab/DIAlignR: Dynamic Programming Based Alignment of MS2 Chromatograms
context("Hierarchical clustering.")
test_that("test_nrDesc", {
tree <- ape::read.tree(text = "((run1:0.5,run2:0.5)master2:0.5,(run3:0.5,run4:0.5)master3:0.5)master1;")
outData <- nrDesc(tree)
expect_identical(outData, c(1, 1, 1, 1, 4, 2, 2))
})
test_that("test_getTree", {
m <- matrix(c(0,1,2,3, 1,0,1.5,1.5, 2,1.5,0,1, 3,1.5,1,0), byrow = TRUE,
ncol = 4, dimnames = list(c("run1", "run2", "run3", "run4"),
c("run1", "run2", "run3", "run4")))
distMat <- as.dist(m, diag = FALSE, upper = FALSE)
expect_message(outData <- getTree(distMat))
expect_equal(outData,
ape::read.tree(text = "((run1:0.5,run2:0.5)master2:0.5,(run3:0.5,run4:0.5)master3:0.5)master1;")
)
})
test_that("test_getNodeIDs", {
tree <- ape::read.tree(text = "((run1:0.5,run2:0.5)master2:0.5,(run3:0.5,run4:0.5)master3:0.5)master1;")
outData <- getNodeIDs(tree)
expData <- c("run1" = 1L, "run2" = 2L, "run3" = 3L, "run4" = 4L,
"master1" = 5L, "master2" = 6L, "master3" = 7L)
expect_identical(outData, expData)
})
test_that("test_traverseUp", {
skip_if_no_pyopenms()
dataPath <- system.file("extdata", package = "DIAlignR")
params <- paramsDIAlignR()
params[["keepFlanks"]] <- TRUE
params[["XICfilter"]] <- "none"; params[["kernelLen"]] <- 0L
params[["globalAlignmentFdr"]] <- 0.05
params[["globalAlignment"]] <- "loess"
params[["context"]] <- "experiment-wide"
params[["chromFile"]] <- "mzML"
fileInfo <- getRunNames(dataPath = dataPath, params = params)
mzPntrs <- list2env(getMZMLpointers(fileInfo))
precursors <- data.table(transition_group_id = 4618L, peptide_id = 14383L,
sequence = "QFNNTDIVLLEDFQK", charge = 3L,
group_label = "14299_QFNNTDIVLLEDFQK/3",
transition_ids = I(list(27706:27711)), key = c("peptide_id", "transition_group_id"))
peptideIDs <- 14383L
peptideScores <- getPeptideScores(fileInfo, peptides = peptideIDs, TRUE, "DIA_Proteomics", "experiment-wide")
masters <- paste("master", 1:(nrow(fileInfo)-1), sep = "")
peptideScores <- lapply(peptideIDs, function(pep) {x <- peptideScores[.(pep)][,-c(1L)]
x <- rbindlist(list(x, data.table("run" = masters, "score" = NA_real_, "pvalue" = NA_real_,
"qvalue" = NA_real_)), use.names=TRUE)
setkeyv(x, "run"); x})
names(peptideScores) <- as.character(peptideIDs)
features <- getFeatures(fileInfo, maxFdrQuery = 0.05, runType = "DIA_Proteomics")
masterFeatures <- dummyFeatures(precursors, nrow(fileInfo)-1, 1L)
features <- do.call(c, list(features, masterFeatures))
prec2chromIndex <- getChromatogramIndices(fileInfo, precursors, mzPntrs)
masterChromIndex <- dummyChromIndex(precursors, nrow(fileInfo)-1, 1L)
prec2chromIndex <- do.call(c, list(prec2chromIndex, masterChromIndex))
adaptiveRTs <- new.env()
refRuns <- new.env()
multipeptide <- getMultipeptide(precursors, features, numMerge = 0L, startIdx = 1L)
tree <- ape::read.tree(text = "(run1:7,run2:2)master1;")
tree <- ape::reorder.phylo(tree, "postorder")
ropenms <- get_ropenms(condaEnv = envName, useConda=TRUE)
msg <- capture_messages(traverseUp(tree, dataPath, fileInfo, features, mzPntrs, prec2chromIndex, precursors,
params, adaptiveRTs, refRuns, multipeptide, peptideScores, ropenms))
expect_equal(msg, c("run1 + run2 = master1\n",
"Getting merged chromatograms for run master1\n",
"Geting global alignment of run1 and run2,",
" n = 150\n",
"Geting global alignment of run2 and run1,",
" n = 150\n",
"Getting merged features for run master1\n",
"Created a child run: master1\n",
"Created all master runs.\n"))
expect_setequal(ls(mzPntrs), c("run0", "run1", "run2", "master1"))
expect_is(mzPntrs[["master1"]], "mzRpwiz")
expect_equal(features$master1[1,], data.table(transition_group_id = 4618L,
feature_id = bit64::as.integer64(7675762503084486466),
RT = 5237.8, intensity = 229.707813, leftWidth = 5217.35, rightWidth = 5261.7,
peak_group_rank = 1L, m_score = 5.692e-05, key = "transition_group_id"), tolerance = 1e-04)
expect_identical(fileInfo["master1", "chromatogramFile"], file.path(dataPath, "xics", "master1.chrom.mzML"))
expect_identical(fileInfo["master1", "runName"], "master1")
expect_identical(prec2chromIndex$master1[,"transition_group_id"][[1]], 4618L)
expect_identical(prec2chromIndex$master1[,"chromatogramIndex"][[1]][[1]], 1:6)
expect_equal(adaptiveRTs[["run1_run2"]], 77.0036, tolerance = 1e-04)
expect_equal(adaptiveRTs[["run2_run1"]], 76.25354, tolerance = 1e-04)
expect_identical(refRuns[["master1"]][[1]], 1L)
expect_identical(refRuns[["master1"]][[2]], "4618")
data(masterXICs_DIAlignR, package="DIAlignR")
outData <- mzR::chromatograms(mzR::openMSfile(file.path(dataPath, "xics", "master1.chrom.mzML"), backend = "pwiz"))
for(i in seq_along(outData)){
expect_equal(outData[[i]][[1]], masterXICs_DIAlignR[[1]][[i]][[1]], tolerance = 1e-04)
expect_equal(outData[[i]][[2]], masterXICs_DIAlignR[[1]][[i]][[2]], tolerance = 1e-04)
}
outData <- readRDS(file.path(dataPath, "master1_av.rds"), refhook = NULL)
for(i in 1:3) expect_equal(outData[[1]][,i], masterXICs_DIAlignR[[2]][,i+2], tolerance = 1e-04)
file.remove(file.path(dataPath, "master1_av.rds"))
file.remove(file.path(dataPath, "xics", "master1.chrom.mzML"))
})
test_that("test_traverseDown", {
skip_if_no_pyopenms()
dataPath <- system.file("extdata", package = "DIAlignR")
params <- paramsDIAlignR()
params[["maxPeptideFdr"]] <- 0.05
params[["keepFlanks"]] <- TRUE
params[["XICfilter"]] <- "none"; params[["kernelLen"]] <- 0L
params[["globalAlignmentFdr"]] <- 0.05
params[["globalAlignment"]] <- "loess"
params[["context"]] <- "experiment-wide"
params[["chromFile"]] <- "mzML"
fileInfo <- getRunNames(dataPath = dataPath, params = params)
mzPntrs <- list2env(getMZMLpointers(fileInfo))
precursors <- getPrecursors(fileInfo, oswMerged = TRUE, params[["runType"]], params[["context"]], params[["maxPeptideFdr"]])
precursors <- precursors[precursors$peptide_id %in% c("7040", "9861", "14383"),]
peptideIDs <- c(7040L, 9861L, 14383L)
peptideScores <- getPeptideScores(fileInfo, peptides = peptideIDs, TRUE, "DIA_Proteomics", "experiment-wide")
masters <- paste("master", 1:(nrow(fileInfo)-1), sep = "")
peptideScores <- lapply(peptideIDs, function(pep) {x <- peptideScores[.(pep)][,-c(1L)]
x <- rbindlist(list(x, data.table("run" = masters, "score" = NA_real_, "pvalue" = NA_real_,
"qvalue" = NA_real_)), use.names=TRUE)
setkeyv(x, "run"); x})
names(peptideScores) <- as.character(peptideIDs)
features <- getFeatures(fileInfo, maxFdrQuery = 0.05, runType = "DIA_Proteomics")
masterFeatures <- dummyFeatures(precursors, nrow(fileInfo)-1, 1L)
features <- do.call(c, list(features, masterFeatures))
prec2chromIndex <- getChromatogramIndices(fileInfo, precursors, mzPntrs)
masterChromIndex <- dummyChromIndex(precursors, nrow(fileInfo)-1, 1L)
prec2chromIndex <- do.call(c, list(prec2chromIndex, masterChromIndex))
adaptiveRTs <- new.env()
refRuns <- new.env()
multipeptide <- getMultipeptide(precursors, features, numMerge = 0L, startIdx = 1L)
tree <- ape::read.tree(text = "(run1:7,run2:2)master1;")
tree <- ape::reorder.phylo(tree, "postorder")
ropenms <- get_ropenms(condaEnv = envName, useConda=TRUE)
expect_warning(traverseUp(tree, dataPath, fileInfo, features, mzPntrs, prec2chromIndex, precursors,
params, adaptiveRTs, refRuns, multipeptide, peptideScores, ropenms))
df1 <- data.table::copy(multipeptide[["7040"]])
df2 <- data.table::copy(multipeptide[["9861"]])
df3 <- data.table::copy(multipeptide[["14383"]])
expect_message(traverseDown(tree, dataPath, fileInfo, multipeptide, prec2chromIndex, mzPntrs, precursors,
adaptiveRTs, refRuns, params),
("Mapping peaks from master1 to run1 and run2.\n|run1 has been aligned to master1.\n|run2 has been aligned to master1.\n|master1 run has been propagated to all parents."),
all = TRUE)
df3$alignment_rank[c(15L, 17L)] <- 1L
df3$alignment_rank[which(df3$run == "master1")[1]] <- 1L
expect_equal(multipeptide[["14383"]], df3)
df2$alignment_rank[c(29L, 30L, 33L, 34L)] <- 1L
df2$alignment_rank[which(df2$run == "master1")[1:2]] <- 1L
expect_equal(multipeptide[["9861"]][-33L,], df2[-33L,])
expect_equal(multipeptide[["9861"]][33L,], data.table(transition_group_id = 9719L, feature_id = bit64::NA_integer64_,
RT = 2607.05, intensity = 11.80541, leftWidth = 2591.431, rightWidth = 2625.569,
peak_group_rank = NA_integer_, m_score = NA_real_, run = "run2", alignment_rank = 1L, key = "run"),
tolerance = 1e-06)
expect_equal(multipeptide[["7040"]], df1)
file.remove(file.path(dataPath, "master1_av.rds"))
file.remove(file.path(dataPath, "xics", "master1.chrom.mzML"))
})
test_that("test_alignToMaster", {
skip_if_no_pyopenms()
dataPath <- system.file("extdata", package = "DIAlignR")
params <- paramsDIAlignR()
params[["keepFlanks"]] <- TRUE
params[["XICfilter"]] <- "none"; params[["kernelLen"]] <- 0L
params[["globalAlignmentFdr"]] <- 0.05
params[["chromFile"]] <- "mzML"
fileInfo <- getRunNames(dataPath = dataPath, params = params)
mzPntrs <- list2env(getMZMLpointers(fileInfo))
precursors <- data.table(transition_group_id = 4618L, peptide_id = 14383L,
sequence = "QFNNTDIVLLEDFQK", charge = 3L,
group_label = "14299_QFNNTDIVLLEDFQK/3",
transition_ids = I(list(27706:27711)), key = c("peptide_id", "transition_group_id"))
peptideIDs <- 14383L
peptideScores <- getPeptideScores(fileInfo, peptides = peptideIDs, TRUE, "DIA_Proteomics", "experiment-wide")
masters <- paste("master", 1:(nrow(fileInfo)-1), sep = "")
peptideScores <- lapply(peptideIDs, function(pep) {x <- peptideScores[.(pep)][,-c(1L)]
x <- rbindlist(list(x, data.table("run" = masters, "score" = NA_real_, "pvalue" = NA_real_,
"qvalue" = NA_real_)), use.names=TRUE)
setkeyv(x, "run"); x})
names(peptideScores) <- as.character(peptideIDs)
features <- getFeatures(fileInfo, maxFdrQuery = 0.05, runType = "DIA_Proteomics")
masterFeatures <- dummyFeatures(precursors, nrow(fileInfo)-1, 1L)
features <- do.call(c, list(features, masterFeatures))
prec2chromIndex <- getChromatogramIndices(fileInfo, precursors, mzPntrs)
masterChromIndex <- dummyChromIndex(precursors, nrow(fileInfo)-1, 1L)
prec2chromIndex <- do.call(c, list(prec2chromIndex, masterChromIndex))
adaptiveRTs <- new.env()
refRuns <- new.env()
multipeptide <- getMultipeptide(precursors, features, numMerge = 0L, startIdx = 1L)
tree <- ape::reorder.phylo(ape::read.tree(text = "(run1:7,run2:2)master1;"), "postorder")
ropenms <- get_ropenms(condaEnv = envName, useConda=TRUE)
traverseUp(tree, dataPath, fileInfo, features, mzPntrs, prec2chromIndex, precursors, params,
adaptiveRTs, refRuns, multipeptide, peptideScores, ropenms)
alignedVecs <- readRDS(file = file.path(dataPath, "master1_av.rds"))
adaptiveRT <- max(adaptiveRTs[["run1_run2"]], adaptiveRTs[["run2_run1"]])
multipeptide[["14383"]]$alignment_rank[which(multipeptide[["14383"]]$run == "master1")[1]] <- 1L
df <- data.table::copy(multipeptide[["14383"]])
alignToMaster(ref = "master1", eXp = "run1", alignedVecs, 1L, adaptiveRT,
multipeptide, prec2chromIndex, mzPntrs, fileInfo, precursors, params)
df$alignment_rank[which(df$run == "run1")[1]] <- 1L
expect_equal(multipeptide[["14383"]], df)
alignToMaster(ref = "master1", eXp = "run2", alignedVecs, 2L, adaptiveRT,
multipeptide, prec2chromIndex, mzPntrs, fileInfo, precursors, params)
df$alignment_rank[which(df$run == "run2")[1]] <- 1L
expect_equal(multipeptide[["14383"]], df)
file.remove(file.path(dataPath, "master1_av.rds"))
file.remove(file.path(dataPath, "xics", "master1.chrom.mzML"))
})
Roestlab/DIAlignR documentation built on March 3, 2021, 9:09 a.m.
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context("Hierarchical clustering.")
test_that("test_nrDesc", {
tree <- ape::read.tree(text = "((run1:0.5,run2:0.5)master2:0.5,(run3:0.5,run4:0.5)master3:0.5)master1;")
outData <- nrDesc(tree)
expect_identical(outData, c(1, 1, 1, 1, 4, 2, 2))
})
test_that("test_getTree", {
m <- matrix(c(0,1,2,3, 1,0,1.5,1.5, 2,1.5,0,1, 3,1.5,1,0), byrow = TRUE,
ncol = 4, dimnames = list(c("run1", "run2", "run3", "run4"),
c("run1", "run2", "run3", "run4")))
distMat <- as.dist(m, diag = FALSE, upper = FALSE)
expect_message(outData <- getTree(distMat))
expect_equal(outData,
ape::read.tree(text = "((run1:0.5,run2:0.5)master2:0.5,(run3:0.5,run4:0.5)master3:0.5)master1;")
)
})
test_that("test_getNodeIDs", {
tree <- ape::read.tree(text = "((run1:0.5,run2:0.5)master2:0.5,(run3:0.5,run4:0.5)master3:0.5)master1;")
outData <- getNodeIDs(tree)
expData <- c("run1" = 1L, "run2" = 2L, "run3" = 3L, "run4" = 4L,
"master1" = 5L, "master2" = 6L, "master3" = 7L)
expect_identical(outData, expData)
})
test_that("test_traverseUp", {
skip_if_no_pyopenms()
dataPath <- system.file("extdata", package = "DIAlignR")
params <- paramsDIAlignR()
params[["keepFlanks"]] <- TRUE
params[["XICfilter"]] <- "none"; params[["kernelLen"]] <- 0L
params[["globalAlignmentFdr"]] <- 0.05
params[["globalAlignment"]] <- "loess"
params[["context"]] <- "experiment-wide"
params[["chromFile"]] <- "mzML"
fileInfo <- getRunNames(dataPath = dataPath, params = params)
mzPntrs <- list2env(getMZMLpointers(fileInfo))
precursors <- data.table(transition_group_id = 4618L, peptide_id = 14383L,
sequence = "QFNNTDIVLLEDFQK", charge = 3L,
group_label = "14299_QFNNTDIVLLEDFQK/3",
transition_ids = I(list(27706:27711)), key = c("peptide_id", "transition_group_id"))
peptideIDs <- 14383L
peptideScores <- getPeptideScores(fileInfo, peptides = peptideIDs, TRUE, "DIA_Proteomics", "experiment-wide")
masters <- paste("master", 1:(nrow(fileInfo)-1), sep = "")
peptideScores <- lapply(peptideIDs, function(pep) {x <- peptideScores[.(pep)][,-c(1L)]
x <- rbindlist(list(x, data.table("run" = masters, "score" = NA_real_, "pvalue" = NA_real_,
"qvalue" = NA_real_)), use.names=TRUE)
setkeyv(x, "run"); x})
names(peptideScores) <- as.character(peptideIDs)
features <- getFeatures(fileInfo, maxFdrQuery = 0.05, runType = "DIA_Proteomics")
masterFeatures <- dummyFeatures(precursors, nrow(fileInfo)-1, 1L)
features <- do.call(c, list(features, masterFeatures))
prec2chromIndex <- getChromatogramIndices(fileInfo, precursors, mzPntrs)
masterChromIndex <- dummyChromIndex(precursors, nrow(fileInfo)-1, 1L)
prec2chromIndex <- do.call(c, list(prec2chromIndex, masterChromIndex))
adaptiveRTs <- new.env()
refRuns <- new.env()
multipeptide <- getMultipeptide(precursors, features, numMerge = 0L, startIdx = 1L)
tree <- ape::read.tree(text = "(run1:7,run2:2)master1;")
tree <- ape::reorder.phylo(tree, "postorder")
ropenms <- get_ropenms(condaEnv = envName, useConda=TRUE)
msg <- capture_messages(traverseUp(tree, dataPath, fileInfo, features, mzPntrs, prec2chromIndex, precursors,
params, adaptiveRTs, refRuns, multipeptide, peptideScores, ropenms))
expect_equal(msg, c("run1 + run2 = master1\n",
"Getting merged chromatograms for run master1\n",
"Geting global alignment of run1 and run2,",
" n = 150\n",
"Geting global alignment of run2 and run1,",
" n = 150\n",
"Getting merged features for run master1\n",
"Created a child run: master1\n",
"Created all master runs.\n"))
expect_setequal(ls(mzPntrs), c("run0", "run1", "run2", "master1"))
expect_is(mzPntrs[["master1"]], "mzRpwiz")
expect_equal(features$master1[1,], data.table(transition_group_id = 4618L,
feature_id = bit64::as.integer64(7675762503084486466),
RT = 5237.8, intensity = 229.707813, leftWidth = 5217.35, rightWidth = 5261.7,
peak_group_rank = 1L, m_score = 5.692e-05, key = "transition_group_id"), tolerance = 1e-04)
expect_identical(fileInfo["master1", "chromatogramFile"], file.path(dataPath, "xics", "master1.chrom.mzML"))
expect_identical(fileInfo["master1", "runName"], "master1")
expect_identical(prec2chromIndex$master1[,"transition_group_id"][[1]], 4618L)
expect_identical(prec2chromIndex$master1[,"chromatogramIndex"][[1]][[1]], 1:6)
expect_equal(adaptiveRTs[["run1_run2"]], 77.0036, tolerance = 1e-04)
expect_equal(adaptiveRTs[["run2_run1"]], 76.25354, tolerance = 1e-04)
expect_identical(refRuns[["master1"]][[1]], 1L)
expect_identical(refRuns[["master1"]][[2]], "4618")
data(masterXICs_DIAlignR, package="DIAlignR")
outData <- mzR::chromatograms(mzR::openMSfile(file.path(dataPath, "xics", "master1.chrom.mzML"), backend = "pwiz"))
for(i in seq_along(outData)){
expect_equal(outData[[i]][[1]], masterXICs_DIAlignR[[1]][[i]][[1]], tolerance = 1e-04)
expect_equal(outData[[i]][[2]], masterXICs_DIAlignR[[1]][[i]][[2]], tolerance = 1e-04)
}
outData <- readRDS(file.path(dataPath, "master1_av.rds"), refhook = NULL)
for(i in 1:3) expect_equal(outData[[1]][,i], masterXICs_DIAlignR[[2]][,i+2], tolerance = 1e-04)
file.remove(file.path(dataPath, "master1_av.rds"))
file.remove(file.path(dataPath, "xics", "master1.chrom.mzML"))
})
test_that("test_traverseDown", {
skip_if_no_pyopenms()
dataPath <- system.file("extdata", package = "DIAlignR")
params <- paramsDIAlignR()
params[["maxPeptideFdr"]] <- 0.05
params[["keepFlanks"]] <- TRUE
params[["XICfilter"]] <- "none"; params[["kernelLen"]] <- 0L
params[["globalAlignmentFdr"]] <- 0.05
params[["globalAlignment"]] <- "loess"
params[["context"]] <- "experiment-wide"
params[["chromFile"]] <- "mzML"
fileInfo <- getRunNames(dataPath = dataPath, params = params)
mzPntrs <- list2env(getMZMLpointers(fileInfo))
precursors <- getPrecursors(fileInfo, oswMerged = TRUE, params[["runType"]], params[["context"]], params[["maxPeptideFdr"]])
precursors <- precursors[precursors$peptide_id %in% c("7040", "9861", "14383"),]
peptideIDs <- c(7040L, 9861L, 14383L)
peptideScores <- getPeptideScores(fileInfo, peptides = peptideIDs, TRUE, "DIA_Proteomics", "experiment-wide")
masters <- paste("master", 1:(nrow(fileInfo)-1), sep = "")
peptideScores <- lapply(peptideIDs, function(pep) {x <- peptideScores[.(pep)][,-c(1L)]
x <- rbindlist(list(x, data.table("run" = masters, "score" = NA_real_, "pvalue" = NA_real_,
"qvalue" = NA_real_)), use.names=TRUE)
setkeyv(x, "run"); x})
names(peptideScores) <- as.character(peptideIDs)
features <- getFeatures(fileInfo, maxFdrQuery = 0.05, runType = "DIA_Proteomics")
masterFeatures <- dummyFeatures(precursors, nrow(fileInfo)-1, 1L)
features <- do.call(c, list(features, masterFeatures))
prec2chromIndex <- getChromatogramIndices(fileInfo, precursors, mzPntrs)
masterChromIndex <- dummyChromIndex(precursors, nrow(fileInfo)-1, 1L)
prec2chromIndex <- do.call(c, list(prec2chromIndex, masterChromIndex))
adaptiveRTs <- new.env()
refRuns <- new.env()
multipeptide <- getMultipeptide(precursors, features, numMerge = 0L, startIdx = 1L)
tree <- ape::read.tree(text = "(run1:7,run2:2)master1;")
tree <- ape::reorder.phylo(tree, "postorder")
ropenms <- get_ropenms(condaEnv = envName, useConda=TRUE)
expect_warning(traverseUp(tree, dataPath, fileInfo, features, mzPntrs, prec2chromIndex, precursors,
params, adaptiveRTs, refRuns, multipeptide, peptideScores, ropenms))
df1 <- data.table::copy(multipeptide[["7040"]])
df2 <- data.table::copy(multipeptide[["9861"]])
df3 <- data.table::copy(multipeptide[["14383"]])
expect_message(traverseDown(tree, dataPath, fileInfo, multipeptide, prec2chromIndex, mzPntrs, precursors,
adaptiveRTs, refRuns, params),
("Mapping peaks from master1 to run1 and run2.\n|run1 has been aligned to master1.\n|run2 has been aligned to master1.\n|master1 run has been propagated to all parents."),
all = TRUE)
df3$alignment_rank[c(15L, 17L)] <- 1L
df3$alignment_rank[which(df3$run == "master1")[1]] <- 1L
expect_equal(multipeptide[["14383"]], df3)
df2$alignment_rank[c(29L, 30L, 33L, 34L)] <- 1L
df2$alignment_rank[which(df2$run == "master1")[1:2]] <- 1L
expect_equal(multipeptide[["9861"]][-33L,], df2[-33L,])
expect_equal(multipeptide[["9861"]][33L,], data.table(transition_group_id = 9719L, feature_id = bit64::NA_integer64_,
RT = 2607.05, intensity = 11.80541, leftWidth = 2591.431, rightWidth = 2625.569,
peak_group_rank = NA_integer_, m_score = NA_real_, run = "run2", alignment_rank = 1L, key = "run"),
tolerance = 1e-06)
expect_equal(multipeptide[["7040"]], df1)
file.remove(file.path(dataPath, "master1_av.rds"))
file.remove(file.path(dataPath, "xics", "master1.chrom.mzML"))
})
test_that("test_alignToMaster", {
skip_if_no_pyopenms()
dataPath <- system.file("extdata", package = "DIAlignR")
params <- paramsDIAlignR()
params[["keepFlanks"]] <- TRUE
params[["XICfilter"]] <- "none"; params[["kernelLen"]] <- 0L
params[["globalAlignmentFdr"]] <- 0.05
params[["chromFile"]] <- "mzML"
fileInfo <- getRunNames(dataPath = dataPath, params = params)
mzPntrs <- list2env(getMZMLpointers(fileInfo))
precursors <- data.table(transition_group_id = 4618L, peptide_id = 14383L,
sequence = "QFNNTDIVLLEDFQK", charge = 3L,
group_label = "14299_QFNNTDIVLLEDFQK/3",
transition_ids = I(list(27706:27711)), key = c("peptide_id", "transition_group_id"))
peptideIDs <- 14383L
peptideScores <- getPeptideScores(fileInfo, peptides = peptideIDs, TRUE, "DIA_Proteomics", "experiment-wide")
masters <- paste("master", 1:(nrow(fileInfo)-1), sep = "")
peptideScores <- lapply(peptideIDs, function(pep) {x <- peptideScores[.(pep)][,-c(1L)]
x <- rbindlist(list(x, data.table("run" = masters, "score" = NA_real_, "pvalue" = NA_real_,
"qvalue" = NA_real_)), use.names=TRUE)
setkeyv(x, "run"); x})
names(peptideScores) <- as.character(peptideIDs)
features <- getFeatures(fileInfo, maxFdrQuery = 0.05, runType = "DIA_Proteomics")
masterFeatures <- dummyFeatures(precursors, nrow(fileInfo)-1, 1L)
features <- do.call(c, list(features, masterFeatures))
prec2chromIndex <- getChromatogramIndices(fileInfo, precursors, mzPntrs)
masterChromIndex <- dummyChromIndex(precursors, nrow(fileInfo)-1, 1L)
prec2chromIndex <- do.call(c, list(prec2chromIndex, masterChromIndex))
adaptiveRTs <- new.env()
refRuns <- new.env()
multipeptide <- getMultipeptide(precursors, features, numMerge = 0L, startIdx = 1L)
tree <- ape::reorder.phylo(ape::read.tree(text = "(run1:7,run2:2)master1;"), "postorder")
ropenms <- get_ropenms(condaEnv = envName, useConda=TRUE)
traverseUp(tree, dataPath, fileInfo, features, mzPntrs, prec2chromIndex, precursors, params,
adaptiveRTs, refRuns, multipeptide, peptideScores, ropenms)
alignedVecs <- readRDS(file = file.path(dataPath, "master1_av.rds"))
adaptiveRT <- max(adaptiveRTs[["run1_run2"]], adaptiveRTs[["run2_run1"]])
multipeptide[["14383"]]$alignment_rank[which(multipeptide[["14383"]]$run == "master1")[1]] <- 1L
df <- data.table::copy(multipeptide[["14383"]])
alignToMaster(ref = "master1", eXp = "run1", alignedVecs, 1L, adaptiveRT,
multipeptide, prec2chromIndex, mzPntrs, fileInfo, precursors, params)
df$alignment_rank[which(df$run == "run1")[1]] <- 1L
expect_equal(multipeptide[["14383"]], df)
alignToMaster(ref = "master1", eXp = "run2", alignedVecs, 2L, adaptiveRT,
multipeptide, prec2chromIndex, mzPntrs, fileInfo, precursors, params)
df$alignment_rank[which(df$run == "run2")[1]] <- 1L
expect_equal(multipeptide[["14383"]], df)
file.remove(file.path(dataPath, "master1_av.rds"))
file.remove(file.path(dataPath, "xics", "master1.chrom.mzML"))
})
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