Get started with MSdev

MSdev is an LC-MS project shell: one S4 object holds sample metadata, xcms feature results, spectra, annotation, and downstream analysis state. Chemistry / molecule-graph helpers live in companion package MSCC; this package focuses on processing, annotation glue, and stats/export.

For a full lipidomics-style run (params, CompoundDb annotation, DEP figures), see the vignette MSdev untargeted workflow.

1. Install and load

# remotes::install_github("DrRuiLi/MSdev")
library(MSdev)

Bioconductor dependencies such as xcms, Spectra, and ProtGenerics must be available. Install with BiocManager::install() if needed.

2. The `MSdev` object

Create from a directory of raw files (.wiff, .raw, .mzML, .mzXML, …):

object <- MSdev(rawDataDir = "path/to/rawDataDir")
# optional: projectDir = "...", experimentInfo = MS_Exp()

Resume a saved project:

object <- MSdev_load("path/to/MSdev_YYYY_MM_DD.Rdata")

Slot	Role
`projectInfo`	Paths, counts, save file name, acquisition summary
`processingInfo`	xcms / pipeline parameters
`sampleInfo`	Per-file sample table (groups, polarity, msLevels, …)
`experimentInfo`	`MS_Exp` experiment metadata
`xcmsData`	Polarity MS1 `XcmsExperiment`s (`PositiveMS1` / `NegativeMS1`) and chromatograms
`spectra`	On-disk MS1 / MS2 (`sp_id` as `spectraNames`)
`annotation`	Compound / MS2 annotation results
`advancedAna`	Extra analyses (e.g. EIC similarity matrices)

3. Minimal untargeted chain

Stock order used in current project records:

object <- MSdev("path/to/rawDataDir")

object <- MSdev_msConvert(object)
object <- MSdev_checkSampleInfo(object)

object <- MSdev_set_param(
  object,
  findChromPeaks = xcms::CentWaveParam(ppm = 25, peakwidth = c(10, 60)),
  groupChromPeaks = xcms::PeakDensityParam(
    sampleGroups = "A", minFraction = 0.5, bw = 20, ppm = 25
  )
)

object <- MSdev_xcmsProcessing(object)
object <- MSdev_extract_Spectra(object)   # stores spectra + assigns MS2 to features

object <- MSdev_annotation(
  object,
  expand_adduct = TRUE,
  cpdb_path = "path/to/CompoundDb.sqlite"
)

object <- MSdev_get_Stat(object, score_thresh = 0.3)
MSdev_export(object)
MSdev_save(object)

Optional after features exist:

object <- MSdev_xcms_group_features(object)  # RT → abundance → EIC (xcms-style)
object <- MSdev_group_feature_EIC(object)    # custom EIC similarity + stored matrices

4. Preferred accessors

Prefer helpers over digging in slots:

get_MSdev_Spectra(object, msLevel = 2, polarity = 1)
get_MSdev_Chromatogram(object, polarity = 1)
get_MSdev_spectra_target_list(object)

MS1 feature containers:

object@xcmsData$PositiveMS1
object@xcmsData$NegativeMS1
# spectra on MS1 xcms objects: ProtGenerics::spectra(xcms)

Feature–MS2 links use character sp_id vectors in featureDefinitions$ms2_id, matched to MS2 spectraNames.

5. Demo data

If bundled demos are available on your machine:

msdev.demo <- load_demo("MSdev")
xcms.demo <- load_demo("xcms")       # alias for XcmsExperiment

6. Where to go next

Topic	Vignette / article
Full untargeted + DEP figures	MSdev untargeted workflow
Spectra backends	article Spectra backends
Fast chromatogram extraction	article xcms chromatogram extraction
Feature grouping / EIC similarity	article Feature grouping with EicSimilarityParam
Formula candidates (Rdisop)	article Rdisop mass decomposition