createExperimentsFragmentOptimisation: Create fragment optimisation experiment

In sgibb/topdownr: Investigation of Fragmentation Conditions in Top-Down Proteomics

Create fragment optimisation experiment

Description

This function is used to create a tree-like list of all combinations of a user-given set of MS1 and TMS2 settings for an fragment optimisation experiment. The list could be written to an Orbitrap Fusion Lumos method xml file using writeMethodXmls().

Usage

createExperimentsFragmentOptimisation(
  ms1,
  ...,
  groupBy = c("AgcTarget", "replication"),
  nMs2perMs1 = 10,
  scanDuration = 0,
  replications = 2,
  randomise = TRUE
)

Arguments

ms1	data.frame, MS1 settings.
...	further named arguments with data.frames containing the TMS2 settings.
groupBy	character, group experiments by columns in the TMS2 data.frames. The columns have to be present in all data.frames. Each group will be written to its own XML file.
nMs2perMs1	integer, how many TMS2 scans should be run after a MS1 scan?
scanDuration	double, if greater than zero (e.g. scanDuration=0.5) the Start/EndTimeMin are overwritten with a duration of scanDuration. If scanDuration is zero (default) Start/EndTimeMin are not overwritten.
replications	integer, number of replications.
randomise	logical, should the TMS2 scan settings randomised?

Value

list, able to be written via xml2::as_xml_document()

See Also

writeMethodXmls(), expandMs1Conditions(), expandTms2Conditions()

Examples

## build experiments within R
ms1 <- expandMs1Conditions(
    FirstMass=400,
    LastMass=1200,
    Microscans=as.integer(10)
)

targetMz <- cbind(mz=c(560.6, 700.5, 933.7), z=rep(1, 3))
common <- list(
    OrbitrapResolution="R120K",
    IsolationWindow=1,
    MaxITTimeInMS=200,
    Microscans=as.integer(40),
    AgcTarget=c(1e5, 5e5, 1e6)
)

cid <- expandTms2Conditions(
    MassList=targetMz,
    common,
    ActivationType="CID",
    CIDCollisionEnergy=seq(7, 35, 7)
)
hcd <- expandTms2Conditions(
    MassList=targetMz,
    common,
    ActivationType="HCD",
    HCDCollisionEnergy=seq(7, 35, 7)
)
etd <- expandTms2Conditions(
    MassList=targetMz,
    common,
    ActivationType="ETD",
    ETDReactionTime=as.double(1:2)
)
etcid <- expandTms2Conditions(
    MassList=targetMz,
    common,
    ActivationType="ETD",
    ETDReactionTime=as.double(1:2),
    ETDSupplementalActivation="ETciD",
    ETDSupplementalActivationEnergy=as.double(1:2)
)
uvpd <- expandTms2Conditions(
    MassList=targetMz,
    common,
    ActivationType="UVPD"
)

exps <- createExperimentsFragmentOptimisation(
    ms1=ms1, cid, hcd, etd, etcid, uvpd,
    groupBy=c("AgcTarget", "replication"), nMs2perMs1=10, scanDuration=0.5,
    replications=2, randomise=TRUE
)

## use different settings for CID
cid560 <- expandTms2Conditions(
    MassList=cbind(560.6, 1),
    common,
    ActivationType="CID",
    CIDCollisionEnergy=seq(7, 21, 7)
)
cid700 <- expandTms2Conditions(
    MassList=cbind(700.5, 1),
    common,
    ActivationType="CID",
    CIDCollisionEnergy=seq(21, 35, 7)
)

exps <- createExperimentsFragmentOptimisation(
    ms1=ms1, cid560, cid700,
    groupBy=c("AgcTarget", "replication"), nMs2perMs1=10, scanDuration=0.5,
    replications=2, randomise=TRUE
)

## use a CSV (or excel) file as input
myCsvContent <- "
ActivationType, ETDReactionTime, UVPDActivationTime
UVPD,,1000
ETD,1000,
"
myCsvSettings <- read.csv(text=myCsvContent, stringsAsFactors=FALSE)
myCsvSettings
#   ActivationType ETDReactionTime UVPDActivationTime
# 1           UVPD              NA               1000
# 2            ETD            1000                 NA

exps <- createExperimentsFragmentOptimisation(
    ms1 = data.frame(FirstMass=500, LastMass=1000),
    ## TMS2
    myCsvSettings,
    ## other arguments
    groupBy="ActivationType"
)

sgibb/topdownr documentation built on Jan. 16, 2024, 12:14 a.m.