Package: squallms 1.1.0
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:
squallms_1.1.0.tar.gz
squallms_1.1.0.zip(r-4.5)squallms_1.1.0.zip(r-4.4)squallms_1.1.0.zip(r-4.3)
squallms_1.1.0.tgz(r-4.4-any)squallms_1.1.0.tgz(r-4.3-any)
squallms_1.1.0.tar.gz(r-4.5-noble)squallms_1.1.0.tar.gz(r-4.4-noble)
squallms_1.1.0.tgz(r-4.4-emscripten)squallms_1.1.0.tgz(r-4.3-emscripten)
squallms.pdf |squallms.html✨
squallms/json (API)
NEWS
| # Install 'squallms' in R: |
| install.packages('squallms', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/wkumler/squallms/issues
On BioConductor:squallms-1.1.0(bioc 3.21)squallms-1.0.0(bioc 3.20)
massspectrometrymetabolomicsproteomicslipidomicsshinyappsclassificationclusteringfeatureextractionprincipalcomponentregressionpreprocessingqualitycontrolvisualization
Last updated 2 months agofrom:c717511c2b. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 30 2024 |
| R-4.5-win | OK | Nov 30 2024 |
| R-4.5-linux | OK | Nov 30 2024 |
| R-4.4-win | OK | Nov 30 2024 |
| R-4.4-mac | OK | Nov 30 2024 |
| R-4.3-win | OK | Nov 30 2024 |
| R-4.3-mac | OK | Nov 30 2024 |
Exports:extractChromMetricslabelFeatsLassolabelFeatsManuallogModelFeatProblogModelFeatQualitymakeXcmsObjFlatpickyPCAupdateXcmsObjFeats
Dependencies:abindaffyaffyioAnnotationFilteraskpassbase64encBHBiobaseBiocBaseUtilsBiocGenericsBiocManagerBiocParallelbslibcachemcaretclasscliclockclueclustercodetoolscolorspacecommonmarkcpp11crayoncrosstalkcurldata.tableDBIDelayedArraydiagramdigestdoParalleldplyre1071evaluatefansifarverfastmapfontawesomeforeachformatRfsfutile.loggerfutile.optionsfuturefuture.applygenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2globalsgluegowergtablehardhathighrhmshtmltoolshtmlwidgetshttpuvhttrigraphimputeipredIRangesisobanditeratorsjquerylibjsonliteKernSmoothkeysknitrlabelinglambda.rlaterlatticelavalazyevallifecyclelimmalistenvlubridatemagrittrMALDIquantMASSMassSpecWaveletMatrixMatrixGenericsmatrixStatsmemoiseMetaboCoreUtilsmgcvmimeModelMetricsMsCoreUtilsMsExperimentMsFeaturesMSnbaseMultiAssayExperimentmunsellmzIDmzRncdf4nlmennetnumDerivopensslparallellypcaMethodspillarpkgconfigplotlyplyrpreprocessCoreprettyunitspROCprodlimprogressprogressrpromisesProtGenericsproxyPSMatchpurrrQFeaturesR6RaMSrappdirsRColorBrewerRcpprecipesreshape2Rhdf5librlangrmarkdownrpartS4ArraysS4VectorssassscalesshapeshinysnowsourcetoolsSparseArraySpectraSQUAREMstatmodstringistringrSummarizedExperimentsurvivalsystibbletidyrtidyselecttimechangetimeDatetinytextzdbUCSC.utilsutf8vctrsviridisLitevsnwithrxcmsxfunXMLxml2xtableXVectoryamlzlibbioc
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Extract metrics of chromatographic peak quality | extractChromMetrics |
| Label similar chromatographic features in bulk via interactive selection | labelFeatsLasso |
| Label chromatographic features manually one at a time via interactive interface | labelFeatsManual |
| Model feature quality using a logistic regression | logModelFeatProb |
| Turn 0-1 likelihood values into categorical (good/bad) classifications | logModelFeatQuality |
| Make an XCMS object flat | makeXcmsObjFlat |
| Perform a PCA on multi-file chromatographic data | pickyPCA |
| Update features in an XCMS object | updateXcmsObjFeats |
