createDatabase: Create database

In computational-metabolomics/msPurity: Automated Evaluation of Precursor Ion Purity for Mass Spectrometry Based Fragmentation in Metabolomics

Create database

Description

General

Create an SQLite database of an LC-MS(/MS) experiment (replaces the create_database function).

Schema details can be found here.

Example LC-MS/MS processing workflow

• Purity assessments

  • (mzML files) -> purityA -> (pa)

• XCMS processing

  • (mzML files) -> xcms.findChromPeaks -> (optionally) xcms.adjustRtime -> xcms.groupChromPeaks -> (xcmsObj)

  • — Older versions of XCMS — (mzML files) -> xcms.xcmsSet -> xcms.group -> xcms.retcor -> xcms.group -> (xcmsObj)

• Fragmentation processing

  • (xcmsObj, pa) -> frag4feature -> filterFragSpectra -> averageAllFragSpectra -> createDatabase -> spectralMatching -> (sqlite spectral database)

Usage

createDatabase(
  pa,
  xcmsObj,
  xsa = NULL,
  outDir = ".",
  grpPeaklist = NA,
  dbName = NA,
  metadata = NA,
  xset = NA
)

Arguments

pa	purityA object; Needs to be the same used for frag4feature function
xcmsObj	xcms object of class XCMSnExp or xcmsSet; Needs to be the same used for frag4feature function (this will be ignored when using xsa parameter)
xsa	CAMERA object (optional); if CAMERA object is used, we ignore the xset parameter input and obtain all information from the xset object nested with the CAMERA xsa object. Adduct and isotope information will be included into the database when using this parameter. The underlying xset object must be the one used for the frag4feature function
outDir	character; Out directory for the SQLite result database
grpPeaklist	dataframe (optional); Can use any peak dataframe. Still needs to be derived from the xset object though
dbName	character (optional); Name of the result database
metadata	list; A list of metadata to add to the s_peak_meta table
xset	xcms object of class XCMSnExp or xcmsSet; (Deprecated - if provided, will replace variable 'obj')

Value

path to SQLite database and database name

Examples

library(xcms)
library(MSnbase)
library(magrittr)
#====== XCMS =================================
## Read in MS data
msmsPths <- list.files(system.file("extdata", "lcms", "mzML",
           package="msPurityData"), full.names = TRUE, pattern = "MSMS")
ms_data = readMSData(msmsPths, mode = 'onDisk', msLevel. = 1)

## Find peaks in each file
cwp <- CentWaveParam(snthresh = 5, noise = 100, ppm = 10, peakwidth = c(3, 30))
xcmsObj  <- xcms::findChromPeaks(ms_data, param = cwp)

## Optionally adjust retention time
xcmsObj  <- adjustRtime(xcmsObj , param = ObiwarpParam(binSize = 0.6))

## Group features across samples
pdp <- PeakDensityParam(sampleGroups = c(1, 1), minFraction = 0, bw = 30)
xcmsObj <- groupChromPeaks(xcmsObj , param = pdp)

#====== msPurity ============================
pa  <- purityA(msmsPths)
pa <- frag4feature(pa = pa, xcmsObj = xcmsObj)
pa <- filterFragSpectra(pa, allfrag=TRUE)
pa <- averageAllFragSpectra(pa)
dbPth <- createDatabase(pa, xcmsObj, metadata=list('polarity'='positive','instrument'='Q-Exactive'))

td <- tempdir()
db_pth = createDatabase(pa = pa, xcmsObj = xcmsObj, outDir = td)

computational-metabolomics/msPurity documentation built on May 13, 2024, 7:36 p.m.