Package: squallms 1.6.0

William Kumler

squallms: Speedy quality assurance via lasso labeling for LC-MS data

squallms is a Bioconductor R package that implements a "semi-labeled" approach to untargeted mass spectrometry data. It pulls in raw data from mass-spec files to calculate several metrics that are then used to label MS features in bulk as high or low quality. These metrics of peak quality are then passed to a simple logistic model that produces a fully-labeled dataset suitable for downstream analysis.

Authors:William Kumler [aut, cre, cph]

squallms_1.6.0.tar.gz
squallms_1.6.0.zip(r-4.7)squallms_1.6.0.zip(r-4.6)squallms_1.6.0.zip(r-4.5)
squallms_1.6.0.tgz(r-4.6-any)squallms_1.6.0.tgz(r-4.5-any)
squallms_1.6.0.tar.gz(r-4.7-any)squallms_1.6.0.tar.gz(r-4.6-any)
squallms_1.6.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
squallms/json (API)
NEWS

# Install 'squallms' in R:
install.packages('squallms', repos = c('https://bioc-release.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/wkumler/squallms/issues

On BioConductor:squallms-1.7.0(bioc 3.24)squallms-1.6.0(bioc 3.23)

massspectrometrymetabolomicsproteomicslipidomicsshinyappsclassificationclusteringfeatureextractionprincipalcomponentregressionpreprocessingqualitycontrolvisualization

4.48 score 3 stars 7 scripts 235 downloads 8 exports 176 dependencies

Last updated from:6109ffc498 (on RELEASE_3_23). Checks:1 NOTE, 9 OK. Indexed: no.

TargetResultTimeFilesSyslog
bioc-checksNOTE208
linux-devel-x86_64OK570
source / vignettesOK383
linux-release-x86_64OK593
macos-release-arm64OK381
macos-oldrel-arm64OK372
windows-develOK550
windows-releaseOK470
windows-oldrelOK477
wasm-releaseOK178

Exports:extractChromMetricslabelFeatsLassolabelFeatsManuallogModelFeatProblogModelFeatQualitymakeXcmsObjFlatpickyPCAupdateXcmsObjFeats

Dependencies:abindaffyaffyioAnnotationFilteraskpassbase64encBHBiobaseBiocBaseUtilsBiocGenericsbiocmakeBiocManagerBiocParallelbslibcachemcaretclasscliclockclueclustercodetoolscommonmarkcpp11crayoncrosstalkcurldata.tableDBIDelayedArraydiagramdigestdir.expirydoParalleldplyre1071evaluatefarverfastmapfilelockfontawesomeforeachformatRfsfutile.loggerfutile.optionsfuturefuture.applygenericsGenomicRangesggplot2globalsgluegowergtablehardhathighrhmshtmltoolshtmlwidgetshttpuvhttrigraphimputeipredIRangesisobanditeratorsjquerylibjsonliteKernSmoothkeysknitrlabelinglambda.rlaterlatticelavalazyevallifecyclelimmalistenvlubridatemagrittrMALDIquantMASSMassSpecWaveletMatrixMatrixGenericsmatrixStatsmemoiseMetaboCoreUtilsmimeModelMetricsMsCoreUtilsMsExperimentMsFeaturesMSnbaseMultiAssayExperimentmzIDmzRncdf4nlmennetnumDerivopensslotelparallellypcaMethodspillarpkgconfigplotlyplyrpreprocessCoreprettyunitspROCprodlimprogressprogressrpromisesProtGenericsproxyPSMatchPTModspurrrQFeaturesR6RaMSrappdirsRColorBrewerRcpprecipesreshape2Rhdf5librlangrmarkdownrpartS4ArraysS4VectorsS7sassscalesSeqinfoshapeshinysnowsourcetoolsSparseArraysparsevctrsSpectraSQUAREMstatmodstringistringrSummarizedExperimentsurvivalsystibbletidyrtidyselecttimechangetimeDatetinytextzdbutf8vctrsviridisLitevsnwithrxcmsxfunXMLxml2xtableXVectoryaml

Introduction to squallms

William Kumler

Rendered fromintro_to_squallms.Rmdusingknitr::rmarkdownon Jun 09 2026.

Last update: 2024-06-07
Started: 2024-03-27