R/read_osw.R
In shubham1637/DIAlignR: Dynamic Programming Based Alignment of MS2 Chromatograms
Defines functions
fetchPrecursorsInfo
getOswAnalytes
fetchAnalytesInfo
Documented in
fetchAnalytesInfo
fetchPrecursorsInfo
getOswAnalytes
#' Fetch features of analytes
#'
#' Get a data-frame of analytes' transition_group_ids, their OpenSwath features, chromatogram indices and associated FDR-scores.
#'
#' @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 oswName (char) path to the osw file.
#' @param maxFdrQuery (numeric) A numeric value between 0 and 1. It is used to filter features from osw file which have SCORE_MS2.QVALUE less than itself.
#' @param oswMerged (logical) TRUE for experiment-wide FDR and FALSE for run-specific FDR by pyprophet.
#' @param analytes (vector of strings) transition_group_ids for which features are to be extracted. analyteInGroupLabel must be set according the pattern used here.
#' @param filename (data-frame) Should be from the RUN.FILENAME column from osw files.
#' @param runType (char) This must be one of the strings "DIA_Proteomics", "DIA_Metabolomics".
#' @param analyteInGroupLabel (logical) TRUE for getting analytes as PRECURSOR.GROUP_LABEL from osw file.
#' FALSE for fetching analytes as PEPTIDE.MODIFIED_SEQUENCE and PRECURSOR.CHARGE from osw file.
#' @return (data-frames) Data-frame has following columns:
#' \item{transition_group_id}{(string) it is either fetched from PRECURSOR.GROUP_LABEL or a combination of PEPTIDE.MODIFIED_SEQUENCE and PRECURSOR.CHARGE from osw file.}
#' \item{filename}{(string) as mentioned in RUN table of osw files.}
#' \item{RT}{(numeric) retention time as in FEATURE.EXP_RT of osw files.}
#' \item{delta_rt}{(numeric) as in FEATURE.DELTA_RT of osw files.}
#' \item{assay_RT}{(numeric) library retention time as in PRECURSOR.LIBRARY_RT of osw files.}
#' \item{Intensity}{(numeric) peak intensity as in FEATURE_MS2.AREA_INTENSITY of osw files.}
#' \item{leftWidth}{(numeric) as in FEATURE.LEFT_WIDTH of osw files.}
#' \item{rightWidth}{(numeric) as in FEATURE.RIGHT_WIDTH of osw files.}
#' \item{peak_group_rank}{(integer) rank of each feature associated with transition_group_id.}
#' \item{m_score}{(numeric) q-value of each feature associated with transition_group_id.}
#' \item{transition_id}{(integer) fragment-ion ID associated with transition_group_id. This is matched with chromatogram ID in mzML file.}
#'
#' @seealso \code{\link{getRunNames}}
#' @keywords internal
#' @examples
#' dataPath <- system.file("extdata", package = "DIAlignR")
#' filenames <- getRunNames(dataPath = dataPath)
#' oswName <- paste0(dataPath,"/osw/merged.osw")
#' \dontrun{
#' analytesInfo <- fetchAnalytesInfo(oswName, maxFdrQuery = 0.05, oswMerged = TRUE,
#' analytes = c("19051_KLIVTSEGC[160]FK/2"), filename = filenames$filename[2],
#' runType = "DIA_Proteomics", analyteInGroupLabel = TRUE)
#' analytesInfo <- fetchAnalytesInfo(oswName, maxFdrQuery = 0.05, oswMerged = TRUE,
#' analytes = c("IHFLSPVRPFTLTPGDEEESFIQLITPVR_3"), filename = filenames$filename[3],
#' runType = "DIA_Proteomics", analyteInGroupLabel = FALSE)
#' }
fetchAnalytesInfo <- function(oswName, maxFdrQuery, oswMerged,
analytes, filename, runType = "DIA_Proteomics", analyteInGroupLabel = FALSE){
# Establish a connection of SQLite file.
con <- DBI::dbConnect(RSQLite::SQLite(), dbname = oswName)
# Generate a query.
query <- getQuery(maxFdrQuery, oswMerged, analytes = analytes,
filename = filename, runType = runType,
analyteInGroupLabel = analyteInGroupLabel)
# Run query to get peptides, their coordinates and scores.
analytesInfo <- tryCatch(expr = DBI::dbGetQuery(con, statement = query),
finally = DBI::dbDisconnect(con))
analytesInfo
}
#' Fetch analytes from OSW file
#'
#' Get a data-frame of analytes, their chromatogram indices and associated FDR-scores.
#'
#' @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 dataPath (char) path to xics and osw directory.
#' @param filenames (data-frame) column "filename" contains RUN table from osw files. column "runs" contain respective mzML names without extension.
#' To get filenames use \code{\link{getRunNames}} function.
#' @param oswMerged (logical) TRUE for experiment-wide FDR and FALSE for run-specific FDR by pyprophet.
#' @param analyteInGroupLabel (logical) TRUE for getting analytes as PRECURSOR.GROUP_LABEL from osw file.
#' FALSE for fetching analytes as PEPTIDE.MODIFIED_SEQUENCE and PRECURSOR.CHARGE from osw file.
#' @param maxFdrQuery (numeric) A numeric value between 0 and 1. It is used to filter features from osw file which have SCORE_MS2.QVALUE less than itself.
#' @param runType (char) This must be one of the strings "DIA_Proteomics", "DIA_Metabolomics".
#' @return (A list of data-frames) Each data-frame has following columns:
#' \item{transition_group_id}{(string) it is either fetched from PRECURSOR.GROUP_LABEL or a combination of PEPTIDE.MODIFIED_SEQUENCE and PRECURSOR.CHARGE from osw file.}
#' \item{filename}{(string) as mentioned in RUN table of osw files.}
#' \item{peak_group_rank}{(integer) rank of each feature associated with transition_group_id.}
#' \item{m_score}{(numeric) q-value of each feature associated with transition_group_id.}
#' \item{transition_id}{(integer) fragment-ion ID associated with transition_group_id. This is matched with chromatogram ID in mzML file.}
#'
#' @keywords internal
#' @seealso \code{\link{getRunNames}}
#' @examples
#' dataPath <- system.file("extdata", package = "DIAlignR")
#' filenames <- getRunNames(dataPath = dataPath)
#' \dontrun{
#' oswFiles <- getOswAnalytes(dataPath = dataPath, filenames = filenames,
#' analyteInGroupLabel = TRUE)
#' oswFiles[["run0"]][1,]
#' oswFiles <- getOswAnalytes(dataPath = dataPath, filenames = filenames,
#' analyteInGroupLabel = FALSE)
#' oswFiles[["run0"]][1,]
#' }
getOswAnalytes <- function(fileInfo, oswMerged = TRUE, analyteInGroupLabel = FALSE,
maxFdrQuery = 0.05, runType = "DIA_Proteomics"){
oswFiles <- list()
for(i in 1:nrow(fileInfo)){
# Get a query to search against the osw files.
oswName <- as.character(fileInfo[["featureFile"]][[i]])
# Establish a connection of SQLite file.
con <- DBI::dbConnect(RSQLite::SQLite(), dbname = oswName)
# Generate a query.
query <- getAnalytesQuery(maxFdrQuery = maxFdrQuery, oswMerged = oswMerged,
filename = fileInfo$spectraFile[i], runType = runType,
analyteInGroupLabel = analyteInGroupLabel)
# Run query to get peptides, their coordinates and scores.
oswAnalytes <- tryCatch(expr = DBI::dbGetQuery(con, statement = query),
finally = DBI::dbDisconnect(con))
oswFiles[[i]] <- oswAnalytes
}
names(oswFiles) <- rownames(fileInfo)
oswFiles
}
#' Get precursors from a feature file
#'
#' Get a data-frame of analytes' transition_group_id, transition_ids, peptide_id and amino-acid sequences.
#'
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2019) + GPL-3
#' Date: 2019-04-04
#' @importFrom data.table setDT
#' @inheritParams getPrecursors
#' @param filename (string) Should be from the RUN.FILENAME column from osw files.
#' @param runType (string) This must be one of the strings "DIA_Proteomics", "DIA_IPF", "DIA_Metabolomics".
#' @param selectIDs (integer) a vector of integers.
#' @param useIdentifying (logical) Set TRUE to use identifying transitions in alignment. (DEFAULT: FALSE)
#' @return (data-frames) Data-frame has following columns:
#' \item{transition_group_id}{(integer) a unique id for each precursor.}
#' \item{transition_id}{(list) fragment-ion ID associated with transition_group_id. This is matched with chromatogram ID in mzML file.}
#' \item{peptide_id}{(integer) a unique id for each peptide. A peptide can have multiple precursors.}
#' \item{sequence}{(string) amino-acid sequence of the precursor with possible modifications.}
#' \item{charge}{(integer) charge on the precursor.}
#' \item{group_label}{(string) TODO Figure it out.}
#'
#' @seealso \code{\link{getRunNames}, \link{getPrecursors}, \link{getPrecursorsQuery}}
#' @keywords internal
#' @examples
#' dataPath <- system.file("extdata", package = "DIAlignR")
#' filename <- paste0(dataPath,"/osw/merged.osw")
#' \dontrun{
#' precursorsInfo <- fetchPrecursorsInfo(filename, runType = "DIA_Proteomics", context = "experiment-wide")
#' dim(precursorsInfo) # 234 6
#' }
fetchPrecursorsInfo <- function(filename, runType = "DIA_Proteomics", selectIDs = NULL,
context = "global", maxPeptideFdr = 0.05, level = "Peptide", useIdentifying=FALSE){
# Establish a connection of SQLite file.
con <- DBI::dbConnect(RSQLite::SQLite(), dbname = as.character(filename))
# Generate a query.
all = FALSE
if(is.null(selectIDs)) all = TRUE
if(all){
query <- getPrecursorsQuery(runType, level)
} else{
query <- getPrecursorsQueryID(selectIDs, runType)
}
# Run query to get peptides, their coordinates and scores.
precursorsInfo <- tryCatch(expr = { output <- DBI::dbSendQuery(con, statement = query)
if(all && !(runType %in% c("DIA_Metabolomics", "DIA_IPF"))) {DBI::dbBind(output, list("CONTEXT"=context, "FDR"=maxPeptideFdr))}
else if(all && runType == "DIA_IPF") {DBI::dbBind(output, list("USE_IDENTIFYING"=useIdentifying, "CONTEXT"=context, "FDR"=maxPeptideFdr))}
DBI::dbFetch(output)},
finally = {DBI::dbClearResult(output)
DBI::dbDisconnect(con)})
# Each precursor has only one row.
setDT(precursorsInfo)
precursorsInfo[, `:=`(transition_ids = list(transition_id)),
by = .(transition_group_id)][, transition_id := NULL]
precursorsInfo <- unique(precursorsInfo, by = c("transition_group_id"))
precursorsInfo
}
#' Get precursors from all feature files
#'
#' Get a data-frame of analytes' transition_group_id, transition_ids, peptide_id and amino-acid sequences.
#'
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2019) + GPL-3
#' Date: 2019-04-06
#' @importFrom data.table data.table setkeyv
#' @param fileInfo (data-frame) Output of \code{\link{getRunNames}} function.
#' @param oswMerged (logical) TRUE for experiment-wide FDR and FALSE for run-specific FDR by pyprophet.
#' @param runType (char) This must be one of the strings "DIA_Proteomics", "DIA_IPF", "DIA_Metabolomics".
#' @param context (string) Context used in pyprophet peptide. Must be either "run-specific", "experiment-wide", or "global".
#' @param maxPeptideFdr (numeric) A numeric value between 0 and 1. It is used to filter peptides from osw file which have SCORE_PEPTIDE.QVALUE less than itself.
#' @param level (string) Apply maxPeptideFDR on Protein as well if specified as "Protein". Default: "Peptide".
#' @param useIdentifying (logical) Set TRUE to use identifying transitions in alignment. (DEFAULT: FALSE)
#' @return (data-frames) A data-frame having following columns:
#' \item{transition_group_id}{(integer) a unique id for each precursor.}
#' \item{peptide_id}{(integer) a unique id for each peptide. A peptide can have multiple precursors.}
#' \item{sequence}{(string) amino-acid sequence of the precursor with possible modifications.}
#' \item{charge}{(integer) charge on the precursor.}
#' \item{group_label}{(string) TODO Figure it out.}
#' \item{transition_ids}{(list) fragment-ion ID associated with transition_group_id. This is matched with chromatogram ID in mzML file.}
#'
#' @seealso \code{\link{getRunNames}, \link{fetchPrecursorsInfo}}
#' @examples
#' dataPath <- system.file("extdata", package = "DIAlignR")
#' fileInfo <- getRunNames(dataPath = dataPath)
#' precursorsInfo <- getPrecursors(fileInfo, oswMerged = TRUE, runType = "DIA_Proteomics",
#' context = "experiment-wide", maxPeptideFdr = 0.05)
#' dim(precursorsInfo) # 234 6
#' @export
getPrecursors <- function(fileInfo, oswMerged = TRUE, runType = "DIA_Proteomics",
context = "global", maxPeptideFdr = 0.05, level = "Peptide", useIdentifying=FALSE){
if(oswMerged == TRUE){
# Get precursor information from merged.osw file
oswName <- unique(fileInfo[["featureFile"]])
precursors <- fetchPrecursorsInfo(oswName, runType, NULL, context, maxPeptideFdr, level, useIdentifying)
} else {
# Iterate over each file and collect precursor information
oswName <- fileInfo[["featureFile"]][[1]]
precursors <- fetchPrecursorsInfo(oswName, runType, NULL, context, maxPeptideFdr, level, useIdentifying)
#for(i in 1:nrow(fileInfo)){
#oswName <- fileInfo[["featureFile"]][[i]]
#temp <- fetchPrecursorsInfo(oswName, runType, NULL, context, maxPeptideFdr, level)
#temp <- temp[!precursors, on = .(transition_group_id)] # Anti-join
#precursors <- rbind(precursors, temp, use.names = FALSE)
#}
}
setkeyv(precursors, c("peptide_id", "transition_group_id"))
message(precursors[,.N], " precursors are found.")
precursors
}
#' Find precursors given their IDs
#'
#' Get a data-frame of analytes' transition_group_id, transition_ids, peptide_id and amino-acid sequences.
#'
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2020) + GPL-3
#' Date: 2019-04-06
#' @importFrom data.table .N
#' @inheritParams getPrecursors
#' @param analytes (integer) a vector of integers.
#' @return (data-frames) A data-frame having following columns:
#' \item{transition_group_id}{(integer) a unique id for each precursor.}
#' \item{transition_id}{(list) fragment-ion ID associated with transition_group_id. This is matched with chromatogram ID in mzML file.}
#' \item{peptide_id}{(integer) a unique id for each peptide. A peptide can have multiple precursors.}
#' \item{sequence}{(string) amino-acid sequence of the precursor with possible modifications.}
#' \item{charge}{(integer) charge on the precursor.}
#' \item{group_label}{(string) TODO Figure it out.}
#'
#' @seealso \code{\link{getRunNames}, \link{fetchPrecursorsInfo}}
#' @examples
#' dataPath <- system.file("extdata", package = "DIAlignR")
#' fileInfo <- getRunNames(dataPath = dataPath, oswMerged = TRUE)
#' precursors <- getPrecursorByID(c(32L, 2474L), fileInfo, oswMerged = TRUE)
#' @export
getPrecursorByID <- function(analytes, fileInfo, oswMerged = TRUE, runType = "DIA_Proteomics"){
if(oswMerged == TRUE){
# Get precursor information from merged.osw file
oswName <- unique(fileInfo[["featureFile"]])
} else {
oswName <- fileInfo[["featureFile"]][[1]]
}
precursors <- fetchPrecursorsInfo(oswName, runType, analytes, maxPeptideFdr = 1.00)
setkeyv(precursors, c("peptide_id", "transition_group_id"))
message(precursors[,.N], " precursors are found.")
precursors
}
#' Get features from a feature file.
#'
#' Get a data-frame of OpenSwath features that contains retention time, intensities, boundaries etc.
#'
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2019) + GPL-3
#' Date: 2019-04-04
#' @importFrom data.table setDT setkey
#' @param filename (string) Path to the feature file.
#' @param runID (string) id in RUN.ID column of the feature file.
#' @param maxFdrQuery (numeric) A numeric value between 0 and 1. It is used to filter features from osw file which have SCORE_MS2.QVALUE less than itself.
#' @param maxIPFFdrQuery (numeric) A numeric value between 0 and 1. It is used to filter features from osw file which have SCORE_IPF.QVALUE less than itself. (For PTM IPF use)
#' @param runType (char) This must be one of the strings "DIA_Proteomics", "DIA_IPF", "DIA_Metabolomics".
#' @return (data-frames) Data-frame has following columns:
#' \item{transition_group_id}{(integer) a unique id for each precursor.}
#' \item{RT}{(numeric) retention time as in FEATURE.EXP_RT of osw files.}
#' \item{Intensity}{(numeric) peak intensity as in FEATURE_MS2.AREA_INTENSITY of osw files.}
#' \item{leftWidth}{(numeric) as in FEATURE.LEFT_WIDTH of osw files.}
#' \item{rightWidth}{(numeric) as in FEATURE.RIGHT_WIDTH of osw files.}
#' \item{peak_group_rank}{(integer) rank of each feature associated with transition_group_id.}
#' \item{m_score}{(numeric) q-value of each feature associated with transition_group_id.}
#'
#' @seealso \code{\link{getRunNames}, \link{getFeatures}, \link{getFeaturesQuery}}
#' @keywords internal
#' @examples
#' dataPath <- system.file("extdata", package = "DIAlignR")
#' fileInfo <- getRunNames(dataPath = dataPath)
#' \dontrun{
#' featuresInfo <- fetchFeaturesFromRun(fileInfo$featureFile[1], fileInfo$spectraFileID[1],
#' maxFdrQuery = 0.05)
#' dim(featuresInfo) # 211 8
#' }
fetchFeaturesFromRun <- function(filename, runID, maxFdrQuery = 1.00, maxIPFFdrQuery = 1.00, runType = "DIA_Proteomics"){
# Establish a connection of SQLite file.
con <- DBI::dbConnect(RSQLite::SQLite(), dbname = as.character(filename))
# Generate a query.
query <- getFeaturesQuery(runType)
# Run query to get peptides, their coordinates and scores.
if (runType=="DIA_IPF"){
featuresInfo <- tryCatch(expr = {output <- DBI::dbSendQuery(con, statement = query)
DBI::dbBind(output, list("FDR"=maxFdrQuery, "IPF_FDR"=maxIPFFdrQuery, "runID" = runID))
DBI::dbFetch(output)},
finally = {DBI::dbClearResult(output)
DBI::dbDisconnect(con)})
} else {
featuresInfo <- tryCatch(expr = {output <- DBI::dbSendQuery(con, statement = query)
DBI::dbBind(output, list("FDR"=maxFdrQuery, "runID" = runID))
DBI::dbFetch(output)},
finally = {DBI::dbClearResult(output)
DBI::dbDisconnect(con)})
}
setDT(featuresInfo)
setkey(featuresInfo, "transition_group_id")
}
#' Get features from all feature files
#'
#' Get a list of data-frame of OpenSwath features that contains retention time, intensities, boundaries etc.
#'
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2019) + GPL-3
#' Date: 2019-04-06
#' @inheritParams alignTargetedRuns
#' @param fileInfo (data-frame) output of \code{\link{getRunNames}} function.
#' @param maxFdrQuery (numeric) a numeric value between 0 and 1. It is used to filter features from osw file which have SCORE_MS2.QVALUE less than itself.
#' @param maxIPFFdrQuery (numeric) A numeric value between 0 and 1. It is used to filter features from osw file which have SCORE_IPF.QVALUE less than itself. (For PTM IPF use)
#' @param runType (char) This must be one of the strings "DIA_Proteomics", "DIA_IPF", "DIA_Metabolomics".
#' @return (list of dataframes) each dataframe has following columns:
#' \item{transition_group_id}{(integer) a unique id for each precursor.}
#' \item{RT}{(numeric) retention time as in FEATURE.EXP_RT of osw files.}
#' \item{Intensity}{(numeric) peak intensity as in FEATURE_MS2.AREA_INTENSITY of osw files.}
#' \item{leftWidth}{(numeric) as in FEATURE.LEFT_WIDTH of osw files.}
#' \item{rightWidth}{(numeric) as in FEATURE.RIGHT_WIDTH of osw files.}
#' \item{peak_group_rank}{(integer) rank of each feature associated with transition_group_id.}
#' \item{m_score}{(numeric) q-value of each feature associated with transition_group_id. (If using 'DIA_IPF' runType, this will represent IPF's QVALUE)}
#' \item{ms2_m_score}{(numeric) MS2 q-value of each feature associated with transition_group_id. (Will only be present if using 'DIA_IPF' runType)}
#'
#' @seealso \code{\link{getRunNames}, \link{fetchPrecursorsInfo}}
#' @examples
#' dataPath <- system.file("extdata", package = "DIAlignR")
#' fileInfo <- getRunNames(dataPath = dataPath)
#' features <- getFeatures(fileInfo, maxFdrQuery = 1.00, runType = "DIA_Proteomics")
#' dim(features[[2]]) # 938 8
#' @export
getFeatures <- function(fileInfo, maxFdrQuery = 0.05, maxIPFFdrQuery = 0.05, runType = "DIA_Proteomics", applyFun = lapply){
features <- applyFun(1:nrow(fileInfo), function(i){
run <- rownames(fileInfo)[i]
oswName <- fileInfo[["featureFile"]][[i]]
runID <- fileInfo[["spectraFileID"]][[i]]
names(runID) <- rownames(fileInfo)[[i]]
df <- fetchFeaturesFromRun(oswName, runID, maxFdrQuery, maxIPFFdrQuery, runType)
if ( runType=="DIA_IPF" ){
message(paste0(nrow(df), " peakgroups are found below ", maxFdrQuery,
" MS2 FDR and ", maxIPFFdrQuery, " IPF FDR in run ", fileInfo[["runName"]][[i]], ", ID = ", runID))
} else{
message(paste0(nrow(df), " peakgroups are found below ", maxFdrQuery,
" FDR in run ", fileInfo[["runName"]][[i]], ", ID = ", runID))
}
df
})
names(features) <- rownames(fileInfo)
features
}
dummyFeatures <- function(precursors, masters, transitionIntensity){
if(transitionIntensity){
intensity = list(c(NA_real_, NA_real_))
} else {
intensity = NA_real_
}
transition_group_ids <- .subset2(precursors, "transition_group_id")
features <- lapply(masters, function(run) {
data.table("transition_group_id" = rep(transition_group_ids, each = 5L),
"feature_id" = bit64::NA_integer64_, "RT" = NA_real_, "intensity" = intensity,
"leftWidth" = NA_real_, "rightWidth" = NA_real_, "peak_group_rank" = NA_integer_,
"m_score" = NA_real_, key = "transition_group_id")
})
names(features) <- masters
features
}
#' Get scores of all peptides
#'
#' Return a scores, pvalues, and qvalues for all peptides from the osw file.
#'
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2020) + GPL-3
#' Date: 2020-07-01
#' @keywords internal
#' @importFrom data.table setDT
#' @inheritParams getPrecursors
#' @param oswName (char) path to the osw file.
#' @return (dataframe) with following columns:
#' \item{peptide_id}{(integer) a unique id for each precursor.}
#' \item{run}{(character) as in SCORE_PEPTIDE.RUN_ID of osw files.}
#' \item{score}{(numeric) as in SCORE_PEPTIDE.SCORE of osw files.}
#' \item{pvalue}{(numeric) as in SCORE_PEPTIDE.PVALUE of osw files.}
#' \item{qvalue}{(numeric) as in SCORE_PEPTIDE.QVALUE of osw files.}
#'
#' @seealso \code{\link{getPeptideQuery}, \link{getPeptideScores}}
#' @examples
#' dataPath <- system.file("extdata", package = "DIAlignR")
#' fileInfo <- getRunNames(dataPath = dataPath)
#' oswName <- fileInfo[["featureFile"]][1]
#' \dontrun{
#' precursorsInfo <- fetchPeptidesInfo(fileInfo, runType = "DIA_Proteomics", context = "experiment-wide")
#' }
fetchPeptidesInfo <- function(oswName, runType, context){
# Establish a connection of SQLite file.
con <- DBI::dbConnect(RSQLite::SQLite(), dbname = oswName)
# Generate a query.
query <- getPeptideQuery(runType)
# Run query to get peptides, their scores and pvalues.
peptidesInfo <- tryCatch(expr = {output <- DBI::dbSendQuery(con, statement = query)
if(runType != "DIA_Metabolomics") DBI::dbBind(output, list("CONTEXT"=context))
DBI::dbFetch(output)},
finally = {DBI::dbClearResult(output)
DBI::dbDisconnect(con)})
peptidesInfo <- peptidesInfo[,c("peptide_id", "run", "score", "pvalue", "qvalue")]
setDT(peptidesInfo)
peptidesInfo
}
#' Get scores of all peptides
#'
#' Return a scores, pvalues, and qvalues for all peptides from the osw file.
#'
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2020) + GPL-3
#' Date: 2020-11-18
#' @keywords internal
#' @inheritParams getPrecursors
#' @param oswName (char) path to the osw file.
#' @return (dataframe) with following columns:
#' \item{peptide_id}{(integer) a unique id for each precursor.}
#' \item{run}{(character) as in SCORE_PEPTIDE.RUN_ID of osw files.}
#' \item{score}{(numeric) as in SCORE_PEPTIDE.SCORE of osw files.}
#' \item{pvalue}{(numeric) as in SCORE_PEPTIDE.PVALUE of osw files.}
#' \item{qvalue}{(numeric) as in SCORE_PEPTIDE.QVALUE of osw files.}
#'
#' @seealso \code{\link{getPeptideQuery}, \link{getPeptideScores}}
#' @examples
#' dataPath <- system.file("extdata", package = "DIAlignR")
#' fileInfo <- getRunNames(dataPath = dataPath)
#' oswName <- fileInfo[["featureFile"]][1]
#' \dontrun{
#' precursorsInfo <- fetchPeptidesInfo(fileInfo, runType = "DIA_Proteomics", context = "experiment-wide")
#' }
fetchPeptidesInfo2 <- function(oswName, runType, context, runID){
# Establish a connection of SQLite file.
con <- DBI::dbConnect(RSQLite::SQLite(), dbname = oswName)
# Generate a query.
query <- getPeptideQuery2(runType)
# Run query to get peptides, their scores and pvalues.
peptidesInfo <- tryCatch(expr = {output <- DBI::dbSendQuery(con, statement = query)
DBI::dbBind(output, list("CONTEXT"=context, "runID" = runID ))
DBI::dbFetch(output)},
finally = {DBI::dbClearResult(output)
DBI::dbDisconnect(con)})
setDT(peptidesInfo)
peptidesInfo
}
#' Get scores of peptide
#'
#' Get a list of dataframes that contains peptide scores, pvalues, and qvalues across all runs.
#'
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2020) + GPL-3
#' Date: 2020-07-01
#' @importFrom data.table setnames setcolorder setkeyv
#' @inheritParams getPrecursors
#' @param peptides (integer) Ids of peptides for which scores are required.
#' @return (list of dataframes) dataframe has following columns:
#' \item{peptide_id}{(integer) a unique id for each precursor.}
#' \item{run}{(character) as in SCORE_PEPTIDE.RUN_ID of osw files.}
#' \item{score}{(numeric) as in SCORE_PEPTIDE.SCORE of osw files.}
#' \item{pvalue}{(numeric) as in SCORE_PEPTIDE.PVALUE of osw files.}
#' \item{qvalue}{(numeric) as in SCORE_PEPTIDE.QVALUE of osw files.}
#'
#' @seealso \code{\link{getRunNames}, \link{fetchPeptidesInfo}}
#' @examples
#' dataPath <- system.file("extdata", package = "DIAlignR")
#' fileInfo <- getRunNames(dataPath = dataPath)
#' precursorsInfo <- getPrecursors(fileInfo, oswMerged = TRUE, runType = "DIA_Proteomics",
#' context = "experiment-wide", maxPeptideFdr = 0.05)
#' peptidesInfo <- getPeptideScores(fileInfo, unique(precursorsInfo$peptide_id))
#' dim(peptidesInfo) # 684 5
#' @export
getPeptideScores <- function(fileInfo, peptides, oswMerged = TRUE, runType = "DIA_Proteomics", context = "global"){
if(context == "global") context <- "experiment-wide"
if(oswMerged == TRUE){
# Get precursor information from merged.osw file
oswName <- unique(fileInfo[["featureFile"]])
peptidesInfo <- fetchPeptidesInfo(oswName, runType, context)
} else {
oswName <- fileInfo[["featureFile"]][[1]]
peptidesInfo <- fetchPeptidesInfo(oswName, runType, context)
}
ids <- bit64::as.integer64(fileInfo$spectraFileID)
peptidesInfo <- peptidesInfo[list(ids), on = "run"]
peptidesInfo[, col2 := rownames(fileInfo)[match(run, ids)]][,run:= NULL]
setnames(peptidesInfo, "col2", "run")
setcolorder(peptidesInfo, c("peptide_id","run"))
peptidesInfo <- peptidesInfo[list(peptides), on = "peptide_id"]
setkeyv(peptidesInfo, "peptide_id")
message(peptidesInfo[,.N], " peptides scores are fetched.")
peptidesInfo
}
#' Get transitions from a feature file
#'
#' Get a data-frame of OpenSwath features that contains retention time, transition intensities, boundaries etc.
#'
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2020) + GPL-3
#' Date: 2020-11-15
#' @importFrom data.table setnames setDT setcolorder .SD ":="
#' @param filename (string) Path to the feature file.
#' @param runID (string) id in RUN.ID column of the feature file.
#' @param maxFdrQuery (numeric) A numeric value between 0 and 1. It is used to filter features from osw file which have SCORE_MS2.QVALUE less than itself.
#' @param runType (char) This must be one of the strings "DIA_Proteomics", "DIA_Metabolomics".
#' @return (data-frames) Data-frame has following columns:
#' \item{transition_group_id}{(integer) a unique id for each precursor.}
#' \item{RT}{(numeric) retention time as in FEATURE.EXP_RT of osw files.}
#' \item{intensity}{(list) of peak intensities as in FEATURE_TRANSITION.AREA_INTENSITY of osw files.}
#' \item{leftWidth}{(numeric) as in FEATURE.LEFT_WIDTH of osw files.}
#' \item{rightWidth}{(numeric) as in FEATURE.RIGHT_WIDTH of osw files.}
#' \item{peak_group_rank}{(integer) rank of each feature associated with transition_group_id.}
#' \item{m_score}{(numeric) q-value of each feature associated with transition_group_id.}
#'
#' @seealso \code{\link{getRunNames}, \link{getTransitions}, \link{getTransitionsQuery}}
#' @keywords internal
#' @examples
#' dataPath <- system.file("extdata", package = "DIAlignR")
#' fileInfo <- getRunNames(dataPath = dataPath)
#' \dontrun{
#' transitionsInfo <- fetchTransitionsFromRun(fileInfo$featureFile[1], fileInfo$spectraFileID[1],
#' maxFdrQuery = 0.05)
#' dim(transitionsInfo) # 211 8
#' }
fetchTransitionsFromRun <- function(filename, runID, maxFdrQuery = 1.00, runType = "DIA_Proteomics"){
# Establish a connection of SQLite file.
con <- DBI::dbConnect(RSQLite::SQLite(), dbname = filename)
# Generate a query.
query <- getTransitionsQuery(runType)
# Run query to get peptides, their coordinates and scores.
transitionInfo <- tryCatch(expr = {output <- DBI::dbSendQuery(con, statement = query)
DBI::dbBind(output, list("FDR"=maxFdrQuery, "runID" = runID))
DBI::dbFetch(output)},
finally = {DBI::dbClearResult(output)
DBI::dbDisconnect(con)})
setDT(transitionInfo)
transitionInfo <- transitionInfo[, `:=`(intensity2 = list(intensity)),
keyby = .(transition_group_id, peak_group_rank)][
,intensity := NULL][
, head(.SD, 1), by=.(transition_group_id, peak_group_rank),
.SDcols = c("feature_id", "RT", "intensity2", "leftWidth", "rightWidth", "m_score")]
setnames(transitionInfo, "intensity2", "intensity")
setcolorder(transitionInfo, c("transition_group_id", "feature_id", "RT", "intensity", "leftWidth",
"rightWidth", "peak_group_rank", "m_score"))
transitionInfo
}
#' Get transitions from all feature files
#'
#' Get a list of data-frame of OpenSwath features that contains retention time, intensities, boundaries etc.
#'
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2020) + GPL-3
#' Date: 2020-11-15
#' @inheritParams alignTargetedRuns
#' @param fileInfo (data-frame) output of \code{\link{getRunNames}} function.
#' @param maxFdrQuery (numeric) a numeric value between 0 and 1. It is used to filter features from osw file which have SCORE_MS2.QVALUE less than itself.
#' @param runType (char) yhis must be one of the strings "DIA_Proteomics", "DIA_Metabolomics".
#' @return (list of dataframes) each dataframe has following columns:
#' \item{transition_group_id}{(integer) a unique id for each precursor.}
#' \item{RT}{(numeric) retention time as in FEATURE.EXP_RT of osw files.}
#' \item{intensity}{(list) of peak intensities as in FEATURE_TRANSITION.AREA_INTENSITY of osw files.}
#' \item{leftWidth}{(numeric) as in FEATURE.LEFT_WIDTH of osw files.}
#' \item{rightWidth}{(numeric) as in FEATURE.RIGHT_WIDTH of osw files.}
#' \item{peak_group_rank}{(integer) rank of each feature associated with transition_group_id.}
#' \item{m_score}{(numeric) q-value of each feature associated with transition_group_id.}
#'
#' @seealso \code{\link{getRunNames}, \link{fetchTransitionsFromRun}}
#' @examples
#' dataPath <- system.file("extdata", package = "DIAlignR")
#' fileInfo <- getRunNames(dataPath = dataPath)
#' transitions <- getTransitions(fileInfo, maxFdrQuery = 1.00, runType = "DIA_Proteomics")
#' dim(transitions[[2]]) # 938 8
#' @export
getTransitions <- function(fileInfo, maxFdrQuery = 0.05, runType = "DIA_Proteomics", applyFun = lapply){
transitions <- applyFun(1:nrow(fileInfo), function(i){
run <- rownames(fileInfo)[i]
oswName <- fileInfo[["featureFile"]][[i]]
runID <- fileInfo[["spectraFileID"]][[i]]
names(runID) <- rownames(fileInfo)[[i]]
df <- fetchTransitionsFromRun(oswName, runID, maxFdrQuery, runType)
message(paste0(nrow(df), " peakgroups are founds below ", maxFdrQuery,
" FDR in run ", fileInfo[["runName"]][[i]], ", ID = ", runID))
df
})
names(transitions) <- rownames(fileInfo)
transitions
}
shubham1637/DIAlignR documentation built on March 29, 2023, 8:45 p.m.
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Defines functions fetchPrecursorsInfo getOswAnalytes fetchAnalytesInfo
Documented in fetchAnalytesInfo fetchPrecursorsInfo getOswAnalytes
#' Fetch features of analytes
#'
#' Get a data-frame of analytes' transition_group_ids, their OpenSwath features, chromatogram indices and associated FDR-scores.
#'
#' @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 oswName (char) path to the osw file.
#' @param maxFdrQuery (numeric) A numeric value between 0 and 1. It is used to filter features from osw file which have SCORE_MS2.QVALUE less than itself.
#' @param oswMerged (logical) TRUE for experiment-wide FDR and FALSE for run-specific FDR by pyprophet.
#' @param analytes (vector of strings) transition_group_ids for which features are to be extracted. analyteInGroupLabel must be set according the pattern used here.
#' @param filename (data-frame) Should be from the RUN.FILENAME column from osw files.
#' @param runType (char) This must be one of the strings "DIA_Proteomics", "DIA_Metabolomics".
#' @param analyteInGroupLabel (logical) TRUE for getting analytes as PRECURSOR.GROUP_LABEL from osw file.
#' FALSE for fetching analytes as PEPTIDE.MODIFIED_SEQUENCE and PRECURSOR.CHARGE from osw file.
#' @return (data-frames) Data-frame has following columns:
#' \item{transition_group_id}{(string) it is either fetched from PRECURSOR.GROUP_LABEL or a combination of PEPTIDE.MODIFIED_SEQUENCE and PRECURSOR.CHARGE from osw file.}
#' \item{filename}{(string) as mentioned in RUN table of osw files.}
#' \item{RT}{(numeric) retention time as in FEATURE.EXP_RT of osw files.}
#' \item{delta_rt}{(numeric) as in FEATURE.DELTA_RT of osw files.}
#' \item{assay_RT}{(numeric) library retention time as in PRECURSOR.LIBRARY_RT of osw files.}
#' \item{Intensity}{(numeric) peak intensity as in FEATURE_MS2.AREA_INTENSITY of osw files.}
#' \item{leftWidth}{(numeric) as in FEATURE.LEFT_WIDTH of osw files.}
#' \item{rightWidth}{(numeric) as in FEATURE.RIGHT_WIDTH of osw files.}
#' \item{peak_group_rank}{(integer) rank of each feature associated with transition_group_id.}
#' \item{m_score}{(numeric) q-value of each feature associated with transition_group_id.}
#' \item{transition_id}{(integer) fragment-ion ID associated with transition_group_id. This is matched with chromatogram ID in mzML file.}
#'
#' @seealso \code{\link{getRunNames}}
#' @keywords internal
#' @examples
#' dataPath <- system.file("extdata", package = "DIAlignR")
#' filenames <- getRunNames(dataPath = dataPath)
#' oswName <- paste0(dataPath,"/osw/merged.osw")
#' \dontrun{
#' analytesInfo <- fetchAnalytesInfo(oswName, maxFdrQuery = 0.05, oswMerged = TRUE,
#' analytes = c("19051_KLIVTSEGC[160]FK/2"), filename = filenames$filename[2],
#' runType = "DIA_Proteomics", analyteInGroupLabel = TRUE)
#' analytesInfo <- fetchAnalytesInfo(oswName, maxFdrQuery = 0.05, oswMerged = TRUE,
#' analytes = c("IHFLSPVRPFTLTPGDEEESFIQLITPVR_3"), filename = filenames$filename[3],
#' runType = "DIA_Proteomics", analyteInGroupLabel = FALSE)
#' }
fetchAnalytesInfo <- function(oswName, maxFdrQuery, oswMerged,
analytes, filename, runType = "DIA_Proteomics", analyteInGroupLabel = FALSE){
# Establish a connection of SQLite file.
con <- DBI::dbConnect(RSQLite::SQLite(), dbname = oswName)
# Generate a query.
query <- getQuery(maxFdrQuery, oswMerged, analytes = analytes,
filename = filename, runType = runType,
analyteInGroupLabel = analyteInGroupLabel)
# Run query to get peptides, their coordinates and scores.
analytesInfo <- tryCatch(expr = DBI::dbGetQuery(con, statement = query),
finally = DBI::dbDisconnect(con))
analytesInfo
}
#' Fetch analytes from OSW file
#'
#' Get a data-frame of analytes, their chromatogram indices and associated FDR-scores.
#'
#' @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 dataPath (char) path to xics and osw directory.
#' @param filenames (data-frame) column "filename" contains RUN table from osw files. column "runs" contain respective mzML names without extension.
#' To get filenames use \code{\link{getRunNames}} function.
#' @param oswMerged (logical) TRUE for experiment-wide FDR and FALSE for run-specific FDR by pyprophet.
#' @param analyteInGroupLabel (logical) TRUE for getting analytes as PRECURSOR.GROUP_LABEL from osw file.
#' FALSE for fetching analytes as PEPTIDE.MODIFIED_SEQUENCE and PRECURSOR.CHARGE from osw file.
#' @param maxFdrQuery (numeric) A numeric value between 0 and 1. It is used to filter features from osw file which have SCORE_MS2.QVALUE less than itself.
#' @param runType (char) This must be one of the strings "DIA_Proteomics", "DIA_Metabolomics".
#' @return (A list of data-frames) Each data-frame has following columns:
#' \item{transition_group_id}{(string) it is either fetched from PRECURSOR.GROUP_LABEL or a combination of PEPTIDE.MODIFIED_SEQUENCE and PRECURSOR.CHARGE from osw file.}
#' \item{filename}{(string) as mentioned in RUN table of osw files.}
#' \item{peak_group_rank}{(integer) rank of each feature associated with transition_group_id.}
#' \item{m_score}{(numeric) q-value of each feature associated with transition_group_id.}
#' \item{transition_id}{(integer) fragment-ion ID associated with transition_group_id. This is matched with chromatogram ID in mzML file.}
#'
#' @keywords internal
#' @seealso \code{\link{getRunNames}}
#' @examples
#' dataPath <- system.file("extdata", package = "DIAlignR")
#' filenames <- getRunNames(dataPath = dataPath)
#' \dontrun{
#' oswFiles <- getOswAnalytes(dataPath = dataPath, filenames = filenames,
#' analyteInGroupLabel = TRUE)
#' oswFiles[["run0"]][1,]
#' oswFiles <- getOswAnalytes(dataPath = dataPath, filenames = filenames,
#' analyteInGroupLabel = FALSE)
#' oswFiles[["run0"]][1,]
#' }
getOswAnalytes <- function(fileInfo, oswMerged = TRUE, analyteInGroupLabel = FALSE,
maxFdrQuery = 0.05, runType = "DIA_Proteomics"){
oswFiles <- list()
for(i in 1:nrow(fileInfo)){
# Get a query to search against the osw files.
oswName <- as.character(fileInfo[["featureFile"]][[i]])
# Establish a connection of SQLite file.
con <- DBI::dbConnect(RSQLite::SQLite(), dbname = oswName)
# Generate a query.
query <- getAnalytesQuery(maxFdrQuery = maxFdrQuery, oswMerged = oswMerged,
filename = fileInfo$spectraFile[i], runType = runType,
analyteInGroupLabel = analyteInGroupLabel)
# Run query to get peptides, their coordinates and scores.
oswAnalytes <- tryCatch(expr = DBI::dbGetQuery(con, statement = query),
finally = DBI::dbDisconnect(con))
oswFiles[[i]] <- oswAnalytes
}
names(oswFiles) <- rownames(fileInfo)
oswFiles
}
#' Get precursors from a feature file
#'
#' Get a data-frame of analytes' transition_group_id, transition_ids, peptide_id and amino-acid sequences.
#'
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2019) + GPL-3
#' Date: 2019-04-04
#' @importFrom data.table setDT
#' @inheritParams getPrecursors
#' @param filename (string) Should be from the RUN.FILENAME column from osw files.
#' @param runType (string) This must be one of the strings "DIA_Proteomics", "DIA_IPF", "DIA_Metabolomics".
#' @param selectIDs (integer) a vector of integers.
#' @param useIdentifying (logical) Set TRUE to use identifying transitions in alignment. (DEFAULT: FALSE)
#' @return (data-frames) Data-frame has following columns:
#' \item{transition_group_id}{(integer) a unique id for each precursor.}
#' \item{transition_id}{(list) fragment-ion ID associated with transition_group_id. This is matched with chromatogram ID in mzML file.}
#' \item{peptide_id}{(integer) a unique id for each peptide. A peptide can have multiple precursors.}
#' \item{sequence}{(string) amino-acid sequence of the precursor with possible modifications.}
#' \item{charge}{(integer) charge on the precursor.}
#' \item{group_label}{(string) TODO Figure it out.}
#'
#' @seealso \code{\link{getRunNames}, \link{getPrecursors}, \link{getPrecursorsQuery}}
#' @keywords internal
#' @examples
#' dataPath <- system.file("extdata", package = "DIAlignR")
#' filename <- paste0(dataPath,"/osw/merged.osw")
#' \dontrun{
#' precursorsInfo <- fetchPrecursorsInfo(filename, runType = "DIA_Proteomics", context = "experiment-wide")
#' dim(precursorsInfo) # 234 6
#' }
fetchPrecursorsInfo <- function(filename, runType = "DIA_Proteomics", selectIDs = NULL,
context = "global", maxPeptideFdr = 0.05, level = "Peptide", useIdentifying=FALSE){
# Establish a connection of SQLite file.
con <- DBI::dbConnect(RSQLite::SQLite(), dbname = as.character(filename))
# Generate a query.
all = FALSE
if(is.null(selectIDs)) all = TRUE
if(all){
query <- getPrecursorsQuery(runType, level)
} else{
query <- getPrecursorsQueryID(selectIDs, runType)
}
# Run query to get peptides, their coordinates and scores.
precursorsInfo <- tryCatch(expr = { output <- DBI::dbSendQuery(con, statement = query)
if(all && !(runType %in% c("DIA_Metabolomics", "DIA_IPF"))) {DBI::dbBind(output, list("CONTEXT"=context, "FDR"=maxPeptideFdr))}
else if(all && runType == "DIA_IPF") {DBI::dbBind(output, list("USE_IDENTIFYING"=useIdentifying, "CONTEXT"=context, "FDR"=maxPeptideFdr))}
DBI::dbFetch(output)},
finally = {DBI::dbClearResult(output)
DBI::dbDisconnect(con)})
# Each precursor has only one row.
setDT(precursorsInfo)
precursorsInfo[, `:=`(transition_ids = list(transition_id)),
by = .(transition_group_id)][, transition_id := NULL]
precursorsInfo <- unique(precursorsInfo, by = c("transition_group_id"))
precursorsInfo
}
#' Get precursors from all feature files
#'
#' Get a data-frame of analytes' transition_group_id, transition_ids, peptide_id and amino-acid sequences.
#'
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2019) + GPL-3
#' Date: 2019-04-06
#' @importFrom data.table data.table setkeyv
#' @param fileInfo (data-frame) Output of \code{\link{getRunNames}} function.
#' @param oswMerged (logical) TRUE for experiment-wide FDR and FALSE for run-specific FDR by pyprophet.
#' @param runType (char) This must be one of the strings "DIA_Proteomics", "DIA_IPF", "DIA_Metabolomics".
#' @param context (string) Context used in pyprophet peptide. Must be either "run-specific", "experiment-wide", or "global".
#' @param maxPeptideFdr (numeric) A numeric value between 0 and 1. It is used to filter peptides from osw file which have SCORE_PEPTIDE.QVALUE less than itself.
#' @param level (string) Apply maxPeptideFDR on Protein as well if specified as "Protein". Default: "Peptide".
#' @param useIdentifying (logical) Set TRUE to use identifying transitions in alignment. (DEFAULT: FALSE)
#' @return (data-frames) A data-frame having following columns:
#' \item{transition_group_id}{(integer) a unique id for each precursor.}
#' \item{peptide_id}{(integer) a unique id for each peptide. A peptide can have multiple precursors.}
#' \item{sequence}{(string) amino-acid sequence of the precursor with possible modifications.}
#' \item{charge}{(integer) charge on the precursor.}
#' \item{group_label}{(string) TODO Figure it out.}
#' \item{transition_ids}{(list) fragment-ion ID associated with transition_group_id. This is matched with chromatogram ID in mzML file.}
#'
#' @seealso \code{\link{getRunNames}, \link{fetchPrecursorsInfo}}
#' @examples
#' dataPath <- system.file("extdata", package = "DIAlignR")
#' fileInfo <- getRunNames(dataPath = dataPath)
#' precursorsInfo <- getPrecursors(fileInfo, oswMerged = TRUE, runType = "DIA_Proteomics",
#' context = "experiment-wide", maxPeptideFdr = 0.05)
#' dim(precursorsInfo) # 234 6
#' @export
getPrecursors <- function(fileInfo, oswMerged = TRUE, runType = "DIA_Proteomics",
context = "global", maxPeptideFdr = 0.05, level = "Peptide", useIdentifying=FALSE){
if(oswMerged == TRUE){
# Get precursor information from merged.osw file
oswName <- unique(fileInfo[["featureFile"]])
precursors <- fetchPrecursorsInfo(oswName, runType, NULL, context, maxPeptideFdr, level, useIdentifying)
} else {
# Iterate over each file and collect precursor information
oswName <- fileInfo[["featureFile"]][[1]]
precursors <- fetchPrecursorsInfo(oswName, runType, NULL, context, maxPeptideFdr, level, useIdentifying)
#for(i in 1:nrow(fileInfo)){
#oswName <- fileInfo[["featureFile"]][[i]]
#temp <- fetchPrecursorsInfo(oswName, runType, NULL, context, maxPeptideFdr, level)
#temp <- temp[!precursors, on = .(transition_group_id)] # Anti-join
#precursors <- rbind(precursors, temp, use.names = FALSE)
#}
}
setkeyv(precursors, c("peptide_id", "transition_group_id"))
message(precursors[,.N], " precursors are found.")
precursors
}
#' Find precursors given their IDs
#'
#' Get a data-frame of analytes' transition_group_id, transition_ids, peptide_id and amino-acid sequences.
#'
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2020) + GPL-3
#' Date: 2019-04-06
#' @importFrom data.table .N
#' @inheritParams getPrecursors
#' @param analytes (integer) a vector of integers.
#' @return (data-frames) A data-frame having following columns:
#' \item{transition_group_id}{(integer) a unique id for each precursor.}
#' \item{transition_id}{(list) fragment-ion ID associated with transition_group_id. This is matched with chromatogram ID in mzML file.}
#' \item{peptide_id}{(integer) a unique id for each peptide. A peptide can have multiple precursors.}
#' \item{sequence}{(string) amino-acid sequence of the precursor with possible modifications.}
#' \item{charge}{(integer) charge on the precursor.}
#' \item{group_label}{(string) TODO Figure it out.}
#'
#' @seealso \code{\link{getRunNames}, \link{fetchPrecursorsInfo}}
#' @examples
#' dataPath <- system.file("extdata", package = "DIAlignR")
#' fileInfo <- getRunNames(dataPath = dataPath, oswMerged = TRUE)
#' precursors <- getPrecursorByID(c(32L, 2474L), fileInfo, oswMerged = TRUE)
#' @export
getPrecursorByID <- function(analytes, fileInfo, oswMerged = TRUE, runType = "DIA_Proteomics"){
if(oswMerged == TRUE){
# Get precursor information from merged.osw file
oswName <- unique(fileInfo[["featureFile"]])
} else {
oswName <- fileInfo[["featureFile"]][[1]]
}
precursors <- fetchPrecursorsInfo(oswName, runType, analytes, maxPeptideFdr = 1.00)
setkeyv(precursors, c("peptide_id", "transition_group_id"))
message(precursors[,.N], " precursors are found.")
precursors
}
#' Get features from a feature file.
#'
#' Get a data-frame of OpenSwath features that contains retention time, intensities, boundaries etc.
#'
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2019) + GPL-3
#' Date: 2019-04-04
#' @importFrom data.table setDT setkey
#' @param filename (string) Path to the feature file.
#' @param runID (string) id in RUN.ID column of the feature file.
#' @param maxFdrQuery (numeric) A numeric value between 0 and 1. It is used to filter features from osw file which have SCORE_MS2.QVALUE less than itself.
#' @param maxIPFFdrQuery (numeric) A numeric value between 0 and 1. It is used to filter features from osw file which have SCORE_IPF.QVALUE less than itself. (For PTM IPF use)
#' @param runType (char) This must be one of the strings "DIA_Proteomics", "DIA_IPF", "DIA_Metabolomics".
#' @return (data-frames) Data-frame has following columns:
#' \item{transition_group_id}{(integer) a unique id for each precursor.}
#' \item{RT}{(numeric) retention time as in FEATURE.EXP_RT of osw files.}
#' \item{Intensity}{(numeric) peak intensity as in FEATURE_MS2.AREA_INTENSITY of osw files.}
#' \item{leftWidth}{(numeric) as in FEATURE.LEFT_WIDTH of osw files.}
#' \item{rightWidth}{(numeric) as in FEATURE.RIGHT_WIDTH of osw files.}
#' \item{peak_group_rank}{(integer) rank of each feature associated with transition_group_id.}
#' \item{m_score}{(numeric) q-value of each feature associated with transition_group_id.}
#'
#' @seealso \code{\link{getRunNames}, \link{getFeatures}, \link{getFeaturesQuery}}
#' @keywords internal
#' @examples
#' dataPath <- system.file("extdata", package = "DIAlignR")
#' fileInfo <- getRunNames(dataPath = dataPath)
#' \dontrun{
#' featuresInfo <- fetchFeaturesFromRun(fileInfo$featureFile[1], fileInfo$spectraFileID[1],
#' maxFdrQuery = 0.05)
#' dim(featuresInfo) # 211 8
#' }
fetchFeaturesFromRun <- function(filename, runID, maxFdrQuery = 1.00, maxIPFFdrQuery = 1.00, runType = "DIA_Proteomics"){
# Establish a connection of SQLite file.
con <- DBI::dbConnect(RSQLite::SQLite(), dbname = as.character(filename))
# Generate a query.
query <- getFeaturesQuery(runType)
# Run query to get peptides, their coordinates and scores.
if (runType=="DIA_IPF"){
featuresInfo <- tryCatch(expr = {output <- DBI::dbSendQuery(con, statement = query)
DBI::dbBind(output, list("FDR"=maxFdrQuery, "IPF_FDR"=maxIPFFdrQuery, "runID" = runID))
DBI::dbFetch(output)},
finally = {DBI::dbClearResult(output)
DBI::dbDisconnect(con)})
} else {
featuresInfo <- tryCatch(expr = {output <- DBI::dbSendQuery(con, statement = query)
DBI::dbBind(output, list("FDR"=maxFdrQuery, "runID" = runID))
DBI::dbFetch(output)},
finally = {DBI::dbClearResult(output)
DBI::dbDisconnect(con)})
}
setDT(featuresInfo)
setkey(featuresInfo, "transition_group_id")
}
#' Get features from all feature files
#'
#' Get a list of data-frame of OpenSwath features that contains retention time, intensities, boundaries etc.
#'
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2019) + GPL-3
#' Date: 2019-04-06
#' @inheritParams alignTargetedRuns
#' @param fileInfo (data-frame) output of \code{\link{getRunNames}} function.
#' @param maxFdrQuery (numeric) a numeric value between 0 and 1. It is used to filter features from osw file which have SCORE_MS2.QVALUE less than itself.
#' @param maxIPFFdrQuery (numeric) A numeric value between 0 and 1. It is used to filter features from osw file which have SCORE_IPF.QVALUE less than itself. (For PTM IPF use)
#' @param runType (char) This must be one of the strings "DIA_Proteomics", "DIA_IPF", "DIA_Metabolomics".
#' @return (list of dataframes) each dataframe has following columns:
#' \item{transition_group_id}{(integer) a unique id for each precursor.}
#' \item{RT}{(numeric) retention time as in FEATURE.EXP_RT of osw files.}
#' \item{Intensity}{(numeric) peak intensity as in FEATURE_MS2.AREA_INTENSITY of osw files.}
#' \item{leftWidth}{(numeric) as in FEATURE.LEFT_WIDTH of osw files.}
#' \item{rightWidth}{(numeric) as in FEATURE.RIGHT_WIDTH of osw files.}
#' \item{peak_group_rank}{(integer) rank of each feature associated with transition_group_id.}
#' \item{m_score}{(numeric) q-value of each feature associated with transition_group_id. (If using 'DIA_IPF' runType, this will represent IPF's QVALUE)}
#' \item{ms2_m_score}{(numeric) MS2 q-value of each feature associated with transition_group_id. (Will only be present if using 'DIA_IPF' runType)}
#'
#' @seealso \code{\link{getRunNames}, \link{fetchPrecursorsInfo}}
#' @examples
#' dataPath <- system.file("extdata", package = "DIAlignR")
#' fileInfo <- getRunNames(dataPath = dataPath)
#' features <- getFeatures(fileInfo, maxFdrQuery = 1.00, runType = "DIA_Proteomics")
#' dim(features[[2]]) # 938 8
#' @export
getFeatures <- function(fileInfo, maxFdrQuery = 0.05, maxIPFFdrQuery = 0.05, runType = "DIA_Proteomics", applyFun = lapply){
features <- applyFun(1:nrow(fileInfo), function(i){
run <- rownames(fileInfo)[i]
oswName <- fileInfo[["featureFile"]][[i]]
runID <- fileInfo[["spectraFileID"]][[i]]
names(runID) <- rownames(fileInfo)[[i]]
df <- fetchFeaturesFromRun(oswName, runID, maxFdrQuery, maxIPFFdrQuery, runType)
if ( runType=="DIA_IPF" ){
message(paste0(nrow(df), " peakgroups are found below ", maxFdrQuery,
" MS2 FDR and ", maxIPFFdrQuery, " IPF FDR in run ", fileInfo[["runName"]][[i]], ", ID = ", runID))
} else{
message(paste0(nrow(df), " peakgroups are found below ", maxFdrQuery,
" FDR in run ", fileInfo[["runName"]][[i]], ", ID = ", runID))
}
df
})
names(features) <- rownames(fileInfo)
features
}
dummyFeatures <- function(precursors, masters, transitionIntensity){
if(transitionIntensity){
intensity = list(c(NA_real_, NA_real_))
} else {
intensity = NA_real_
}
transition_group_ids <- .subset2(precursors, "transition_group_id")
features <- lapply(masters, function(run) {
data.table("transition_group_id" = rep(transition_group_ids, each = 5L),
"feature_id" = bit64::NA_integer64_, "RT" = NA_real_, "intensity" = intensity,
"leftWidth" = NA_real_, "rightWidth" = NA_real_, "peak_group_rank" = NA_integer_,
"m_score" = NA_real_, key = "transition_group_id")
})
names(features) <- masters
features
}
#' Get scores of all peptides
#'
#' Return a scores, pvalues, and qvalues for all peptides from the osw file.
#'
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2020) + GPL-3
#' Date: 2020-07-01
#' @keywords internal
#' @importFrom data.table setDT
#' @inheritParams getPrecursors
#' @param oswName (char) path to the osw file.
#' @return (dataframe) with following columns:
#' \item{peptide_id}{(integer) a unique id for each precursor.}
#' \item{run}{(character) as in SCORE_PEPTIDE.RUN_ID of osw files.}
#' \item{score}{(numeric) as in SCORE_PEPTIDE.SCORE of osw files.}
#' \item{pvalue}{(numeric) as in SCORE_PEPTIDE.PVALUE of osw files.}
#' \item{qvalue}{(numeric) as in SCORE_PEPTIDE.QVALUE of osw files.}
#'
#' @seealso \code{\link{getPeptideQuery}, \link{getPeptideScores}}
#' @examples
#' dataPath <- system.file("extdata", package = "DIAlignR")
#' fileInfo <- getRunNames(dataPath = dataPath)
#' oswName <- fileInfo[["featureFile"]][1]
#' \dontrun{
#' precursorsInfo <- fetchPeptidesInfo(fileInfo, runType = "DIA_Proteomics", context = "experiment-wide")
#' }
fetchPeptidesInfo <- function(oswName, runType, context){
# Establish a connection of SQLite file.
con <- DBI::dbConnect(RSQLite::SQLite(), dbname = oswName)
# Generate a query.
query <- getPeptideQuery(runType)
# Run query to get peptides, their scores and pvalues.
peptidesInfo <- tryCatch(expr = {output <- DBI::dbSendQuery(con, statement = query)
if(runType != "DIA_Metabolomics") DBI::dbBind(output, list("CONTEXT"=context))
DBI::dbFetch(output)},
finally = {DBI::dbClearResult(output)
DBI::dbDisconnect(con)})
peptidesInfo <- peptidesInfo[,c("peptide_id", "run", "score", "pvalue", "qvalue")]
setDT(peptidesInfo)
peptidesInfo
}
#' Get scores of all peptides
#'
#' Return a scores, pvalues, and qvalues for all peptides from the osw file.
#'
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2020) + GPL-3
#' Date: 2020-11-18
#' @keywords internal
#' @inheritParams getPrecursors
#' @param oswName (char) path to the osw file.
#' @return (dataframe) with following columns:
#' \item{peptide_id}{(integer) a unique id for each precursor.}
#' \item{run}{(character) as in SCORE_PEPTIDE.RUN_ID of osw files.}
#' \item{score}{(numeric) as in SCORE_PEPTIDE.SCORE of osw files.}
#' \item{pvalue}{(numeric) as in SCORE_PEPTIDE.PVALUE of osw files.}
#' \item{qvalue}{(numeric) as in SCORE_PEPTIDE.QVALUE of osw files.}
#'
#' @seealso \code{\link{getPeptideQuery}, \link{getPeptideScores}}
#' @examples
#' dataPath <- system.file("extdata", package = "DIAlignR")
#' fileInfo <- getRunNames(dataPath = dataPath)
#' oswName <- fileInfo[["featureFile"]][1]
#' \dontrun{
#' precursorsInfo <- fetchPeptidesInfo(fileInfo, runType = "DIA_Proteomics", context = "experiment-wide")
#' }
fetchPeptidesInfo2 <- function(oswName, runType, context, runID){
# Establish a connection of SQLite file.
con <- DBI::dbConnect(RSQLite::SQLite(), dbname = oswName)
# Generate a query.
query <- getPeptideQuery2(runType)
# Run query to get peptides, their scores and pvalues.
peptidesInfo <- tryCatch(expr = {output <- DBI::dbSendQuery(con, statement = query)
DBI::dbBind(output, list("CONTEXT"=context, "runID" = runID ))
DBI::dbFetch(output)},
finally = {DBI::dbClearResult(output)
DBI::dbDisconnect(con)})
setDT(peptidesInfo)
peptidesInfo
}
#' Get scores of peptide
#'
#' Get a list of dataframes that contains peptide scores, pvalues, and qvalues across all runs.
#'
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2020) + GPL-3
#' Date: 2020-07-01
#' @importFrom data.table setnames setcolorder setkeyv
#' @inheritParams getPrecursors
#' @param peptides (integer) Ids of peptides for which scores are required.
#' @return (list of dataframes) dataframe has following columns:
#' \item{peptide_id}{(integer) a unique id for each precursor.}
#' \item{run}{(character) as in SCORE_PEPTIDE.RUN_ID of osw files.}
#' \item{score}{(numeric) as in SCORE_PEPTIDE.SCORE of osw files.}
#' \item{pvalue}{(numeric) as in SCORE_PEPTIDE.PVALUE of osw files.}
#' \item{qvalue}{(numeric) as in SCORE_PEPTIDE.QVALUE of osw files.}
#'
#' @seealso \code{\link{getRunNames}, \link{fetchPeptidesInfo}}
#' @examples
#' dataPath <- system.file("extdata", package = "DIAlignR")
#' fileInfo <- getRunNames(dataPath = dataPath)
#' precursorsInfo <- getPrecursors(fileInfo, oswMerged = TRUE, runType = "DIA_Proteomics",
#' context = "experiment-wide", maxPeptideFdr = 0.05)
#' peptidesInfo <- getPeptideScores(fileInfo, unique(precursorsInfo$peptide_id))
#' dim(peptidesInfo) # 684 5
#' @export
getPeptideScores <- function(fileInfo, peptides, oswMerged = TRUE, runType = "DIA_Proteomics", context = "global"){
if(context == "global") context <- "experiment-wide"
if(oswMerged == TRUE){
# Get precursor information from merged.osw file
oswName <- unique(fileInfo[["featureFile"]])
peptidesInfo <- fetchPeptidesInfo(oswName, runType, context)
} else {
oswName <- fileInfo[["featureFile"]][[1]]
peptidesInfo <- fetchPeptidesInfo(oswName, runType, context)
}
ids <- bit64::as.integer64(fileInfo$spectraFileID)
peptidesInfo <- peptidesInfo[list(ids), on = "run"]
peptidesInfo[, col2 := rownames(fileInfo)[match(run, ids)]][,run:= NULL]
setnames(peptidesInfo, "col2", "run")
setcolorder(peptidesInfo, c("peptide_id","run"))
peptidesInfo <- peptidesInfo[list(peptides), on = "peptide_id"]
setkeyv(peptidesInfo, "peptide_id")
message(peptidesInfo[,.N], " peptides scores are fetched.")
peptidesInfo
}
#' Get transitions from a feature file
#'
#' Get a data-frame of OpenSwath features that contains retention time, transition intensities, boundaries etc.
#'
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2020) + GPL-3
#' Date: 2020-11-15
#' @importFrom data.table setnames setDT setcolorder .SD ":="
#' @param filename (string) Path to the feature file.
#' @param runID (string) id in RUN.ID column of the feature file.
#' @param maxFdrQuery (numeric) A numeric value between 0 and 1. It is used to filter features from osw file which have SCORE_MS2.QVALUE less than itself.
#' @param runType (char) This must be one of the strings "DIA_Proteomics", "DIA_Metabolomics".
#' @return (data-frames) Data-frame has following columns:
#' \item{transition_group_id}{(integer) a unique id for each precursor.}
#' \item{RT}{(numeric) retention time as in FEATURE.EXP_RT of osw files.}
#' \item{intensity}{(list) of peak intensities as in FEATURE_TRANSITION.AREA_INTENSITY of osw files.}
#' \item{leftWidth}{(numeric) as in FEATURE.LEFT_WIDTH of osw files.}
#' \item{rightWidth}{(numeric) as in FEATURE.RIGHT_WIDTH of osw files.}
#' \item{peak_group_rank}{(integer) rank of each feature associated with transition_group_id.}
#' \item{m_score}{(numeric) q-value of each feature associated with transition_group_id.}
#'
#' @seealso \code{\link{getRunNames}, \link{getTransitions}, \link{getTransitionsQuery}}
#' @keywords internal
#' @examples
#' dataPath <- system.file("extdata", package = "DIAlignR")
#' fileInfo <- getRunNames(dataPath = dataPath)
#' \dontrun{
#' transitionsInfo <- fetchTransitionsFromRun(fileInfo$featureFile[1], fileInfo$spectraFileID[1],
#' maxFdrQuery = 0.05)
#' dim(transitionsInfo) # 211 8
#' }
fetchTransitionsFromRun <- function(filename, runID, maxFdrQuery = 1.00, runType = "DIA_Proteomics"){
# Establish a connection of SQLite file.
con <- DBI::dbConnect(RSQLite::SQLite(), dbname = filename)
# Generate a query.
query <- getTransitionsQuery(runType)
# Run query to get peptides, their coordinates and scores.
transitionInfo <- tryCatch(expr = {output <- DBI::dbSendQuery(con, statement = query)
DBI::dbBind(output, list("FDR"=maxFdrQuery, "runID" = runID))
DBI::dbFetch(output)},
finally = {DBI::dbClearResult(output)
DBI::dbDisconnect(con)})
setDT(transitionInfo)
transitionInfo <- transitionInfo[, `:=`(intensity2 = list(intensity)),
keyby = .(transition_group_id, peak_group_rank)][
,intensity := NULL][
, head(.SD, 1), by=.(transition_group_id, peak_group_rank),
.SDcols = c("feature_id", "RT", "intensity2", "leftWidth", "rightWidth", "m_score")]
setnames(transitionInfo, "intensity2", "intensity")
setcolorder(transitionInfo, c("transition_group_id", "feature_id", "RT", "intensity", "leftWidth",
"rightWidth", "peak_group_rank", "m_score"))
transitionInfo
}
#' Get transitions from all feature files
#'
#' Get a list of data-frame of OpenSwath features that contains retention time, intensities, boundaries etc.
#'
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2020) + GPL-3
#' Date: 2020-11-15
#' @inheritParams alignTargetedRuns
#' @param fileInfo (data-frame) output of \code{\link{getRunNames}} function.
#' @param maxFdrQuery (numeric) a numeric value between 0 and 1. It is used to filter features from osw file which have SCORE_MS2.QVALUE less than itself.
#' @param runType (char) yhis must be one of the strings "DIA_Proteomics", "DIA_Metabolomics".
#' @return (list of dataframes) each dataframe has following columns:
#' \item{transition_group_id}{(integer) a unique id for each precursor.}
#' \item{RT}{(numeric) retention time as in FEATURE.EXP_RT of osw files.}
#' \item{intensity}{(list) of peak intensities as in FEATURE_TRANSITION.AREA_INTENSITY of osw files.}
#' \item{leftWidth}{(numeric) as in FEATURE.LEFT_WIDTH of osw files.}
#' \item{rightWidth}{(numeric) as in FEATURE.RIGHT_WIDTH of osw files.}
#' \item{peak_group_rank}{(integer) rank of each feature associated with transition_group_id.}
#' \item{m_score}{(numeric) q-value of each feature associated with transition_group_id.}
#'
#' @seealso \code{\link{getRunNames}, \link{fetchTransitionsFromRun}}
#' @examples
#' dataPath <- system.file("extdata", package = "DIAlignR")
#' fileInfo <- getRunNames(dataPath = dataPath)
#' transitions <- getTransitions(fileInfo, maxFdrQuery = 1.00, runType = "DIA_Proteomics")
#' dim(transitions[[2]]) # 938 8
#' @export
getTransitions <- function(fileInfo, maxFdrQuery = 0.05, runType = "DIA_Proteomics", applyFun = lapply){
transitions <- applyFun(1:nrow(fileInfo), function(i){
run <- rownames(fileInfo)[i]
oswName <- fileInfo[["featureFile"]][[i]]
runID <- fileInfo[["spectraFileID"]][[i]]
names(runID) <- rownames(fileInfo)[[i]]
df <- fetchTransitionsFromRun(oswName, runID, maxFdrQuery, runType)
message(paste0(nrow(df), " peakgroups are founds below ", maxFdrQuery,
" FDR in run ", fileInfo[["runName"]][[i]], ", ID = ", runID))
df
})
names(transitions) <- rownames(fileInfo)
transitions
}
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