rawDiag
an R package supporting rational LC-MS method optimization for bottom-up proteomics on multiple OS platforms
main features:
- multiplatform and fast reading through using The New RawFileReader from Thermo Fisher Scientific.
- uses latest visualization generation through using Rs ggplot2 package.
- provides an R interface to your instrument raw data.
- ships with an example shiny application.
1. System Requirements
A Windows/Linux/MacOSX x64 platform.
1.1 .NET framework
- Linux and MacOSX: https://www.mono-project.com/ (>4.0.22)
- MS Windows: MS .NET Framework 4.8 or higher
1.2 R
You need the R software environment for statistical computing.
- if you want support for Open File Standards install the mzR package.
- U.S. language setting on windows/linux/apple see issue 33
1.3 Platforms and R versions the package has been tested on
The package JPR.R1 release has been tested on the following platforms using RStudio:
| platform | platform version | R version | note |
|---|---|---|---|
| Linux | Debian 8 (jessie) | 3.4.3 | Demo system |
| Linux | Debian 10 (buster) | 3.5.0 | CP |
| Linux | bioconductor/devel_proteomics2 | 2017-12-31 r73996 | dockerhub |
| Windows | 7 x64 | 3.4.1 | CT |
| Windows | 10 x64 | 3.4.4 | CP virtual box |
| Windows | Server 2012 R2 x64 | 3.4.4 | CP |
| Windows | 10 x64 | 3.4.3 | WEW |
| Windows | 10 x64 | R Open 3.5.0 | WEW |
| Windows | 12 | 4.0.0 | CP see also #56 |
| MacOSX | 10.13.5 (17F77) | 3.4.2 | CP |
| MacOSX | 10.11.6 (15G20015) | 3.4.3 | JG |
| MacOSX | 10.13.4 (17E202) | 3.4.4 | CP |
2. Installation guide
Please check the above installation requirements!
2.1 Current release
Please install the latest release from [https://github.com/fgcz/rawDiag/releases] according to the provided instructions.
2.2 From GitHub source
The following code downloads and installs the R package from GitHub without the required third party libraries.
# install.packages("devtools")
library("devtools")
devtools::install_github("fgcz/rawDiag", build_vignettes = FALSE)
!!! Use this option only if you know what you are doing !!!
3. Demonstration
3.1. R commandline code snippet
"Hello; World!" example on the R command line
library(rawDiag)
data(WU163763)
PlotScanFrequency(WU163763, method='overlay')
PlotPrecursorHeatmap(WU163763)
PlotMassDistribution(WU163763)
3.2. An interactive shiny example
in your local R shell
# install.packages("shiny")
# install.packages("DT")
library(shiny)
rawDiag_shiny <- system.file('shiny', 'demo', package = 'rawDiag')
shiny::runApp(rawDiag_shiny, display.mode = 'normal')
using the docker image
source: dockerhub
docker pull cpanse/rawdiag \
&& docker run -it -p 8787:8787 cpanse/rawdiag R -e "library(shiny); \
rawDiag_shiny <- system.file('shiny', 'demo', package = 'rawDiag'); \
shiny::runApp(rawDiag_shiny, display.mode = 'normal', port=8787, host='0.0.0.0')"
connect with your web browser to http://yourdockerhostname:8787
3.3. using the read.raw method
taken from the ?read.raw man page.
(rawfile <- file.path(path.package(package = 'rawDiag'), 'extdata', 'sample.raw'))
system.time(RAW <- read.raw(file = rawfile))
summary.rawDiag(RAW)
PlotScanFrequency(RAW)
dim(RAW)
# now read all dimensions
RAW <- read.raw(file = rawfile, rawDiag = FALSE)
dim(RAW)
INFO <- read.raw.info(rawfile)
head(INFO$`General File Information`)
3.4. FAQ
3.4.1. I would like to load multiple files into a single data.frame to do comparisons; what is the preferred method for doing so?
library(parallel)
library(rawDiag)
# consider all raw files of your working dir
rawFileNames <- list.files()[grep("raw$", list.files())]
# read all the meta data using 4 cores
RAW <- mclapply(rawFileNames, read.raw, mc.cores=4)
# as alternative \code{lapply} instread of \code{mclapply}
# concatenate the list data.frames into one single one
RAW <- plyr::rbind.fill(RAW)
3.4.2. Can I run the rawDiag shiny code as a stand-alone application?
run the rawDiag shiny application
library(rawDiag)
# root defines where your raw files are
rawDiagShiny(root="D:/Data2San/")
Yes, on Microsoft's systems call (through using cmd.exe)
"c:\Program Files\R\R-3.5.1\bin\R.exe" -e "library(rawDiag); rawDiagShiny(root='D:/Downloads', launch.browser=TRUE)"
expecting the raw files in the Downloads folder.
using Linux and Apple systems use the Terminal application and type
R -e "library(rawDiag); rawDiagShiny(root='$HOME/Downloads', launch.browser=TRUE)"
and you can add to your alias file, e.g., $HOME/.bashrc
alias rawDiag="R -e \"library(rawDiag); rawDiagShiny(root='$HOME/Downloads', launch.browser=TRUE)\""
3.4.3 How to get all scan attributes assosiated to each scan?
Assuming the raw file name is equal to "20181217_006_autoQC01.raw" the command would be:
AllScanMetaData <- read.raw("20181217_006_autoQC01.raw", rawDiag = FALSE)
> dim(AllScanMetaData)
[1] 21868 82
> names(AllScanMetaData)
[1] "filename" "scanNumber" "ScanEventNumber"
[4] "StartTime" "BasePeakMass" "BasePeakIntensity"
[7] "TIC" "ScanType" "CycleNumber"
[10] "Frequency" "HighMass" "IonizationMode"
[13] "MSOrder" "MassAnalyzer" "Detector"
[16] "Lock" "PrecursorMass" "LastPrecursorMass"
[19] "CollisionEnergy" "IsolationWidth" "MultipleInjection"
[22] "MultiInjectInfo" "AGC" "MicroScanCount"
[25] "ScanSegment" "ScanEvent" "MasterIndex"
[28] "ChargeState" "MonoisotopicmZ" "IonInjectionTimems"
[31] "MaxIonTimems" "FTResolution" "MS2IsolationWidth"
[34] "MS2IsolationOffset" "AGCTarget" "HCDEnergy"
[37] "AnalyzerTemperature" "MassCalibration" "ConversionParameterB"
[40] "ConversionParameterC" "TemperatureCompppm" "RFCompppm"
[43] "SpaceChargeCompppm" "ResolutionCompppm" "NumberofLockMasses"
[46] "LockMass1mZ" "LockMass2mZ" "LockMass3mZ"
[49] "LMSearchWindowppm" "LMSearchWindowmmu" "NumberofLMFound"
[52] "LastLockingsec" "LMmZCorrectionppm" "IonOpticsSettings"
[55] "SLensRFLevel" "SLensVoltageV" "SkimmerVoltageV"
[58] "InjectFlatapoleOffsetV" "BentFlatapoleDCV" "MP2andMP3RFV"
[61] "GateLensVoltageV" "CTrapRFV" "DiagnosticData"
[64] "DynamicRTShiftmin" "IntensCompFactor" "ResDepIntens"
[67] "CTCDNumF" "CTCDComp" "CTCDScScr"
[70] "RawOvFtT" "LCFWHMparameter" "Rod"
[73] "PSInjTimems" "AGCPSMode" "AGCPSDiag"
[76] "HCDEnergyeV" "AGCFill" "Injectiont0"
[79] "t0FLP" "AccessId" "AnalogInput1V"
[82] "AnalogInput2V"
3.4.4 How to read a large number of MS2 scans?
apply the divide-and-conquer algorithm design paradigm
stopifnot(require(rawDiag))
stopifnot(require(parallel))
chunk <- function(x,n) split(x, factor(sort(rank(x)%%n)))
# input: is a given integer vecor of MS2 scanNumber and the raw file name
# output: list of MS2 scans
MS2 <- mclapply(chunk(scanNumberMs2, 100),
function(scans){readScans(rawfilename, scans=scans)},
mc.cores=16)
4. Instructions for use
read the vignettes.
browseVignettes('rawDiag')
the documentation of the functions is available through the R man pages.
5. Useful bookmarks
-
vignette on reading MS/MS2 and extracting XIC example using the
rawDiag::readScansandrawDiag::readXICsfunctions; replication code. -
bioRxiv 304485 rawDiag manuscript. Now published in Journal of Proteome Research doi: 10.1021/acs.jproteome.8b00173.
-
Proteomics Forum 2019 poster
and replication code.
-
EuroBioc2018 slides as PDF (8.5M, md5=cb8da7f05cb6ab197a9339db477e0887).
-
ASMS 2018 poster as PDF (1.8M, md5=dab9388c1a465d931e9d2345119a2827).
-
use rawDiag shiny demonstration on our compute server hosted @ UZH.
-
example data on MassIVE MSV000082389.
-
source code: https://fgcz.github.io/rawDiag/.
6. See also
- Direct Access to Orbitrap Data and Beyond - https://github.com/fgcz/rawrr
- RawBeans: A Simple, Vendor-Independent, Raw-Data Quality-Control Tool - https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00956
- ThermoRawFileParser: modular, scalable and cross-platform RAW file conversion
- MARMoSET – Extracting Publication-Ready Mass Spectrometry Metadata from RAW Files