Package: squallms 0.99.6

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]

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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-0.99.6(bioc 3.20)

bioconductor-package

8 exports 1.00 score 177 dependencies

Last updated 1 months agofrom:defbc6a84a

Exports:extractChromMetrics labelFeatsLasso labelFeatsManual logModelFeatProb logModelFeatQuality makeXcmsObjFlat pickyPCA updateXcmsObjFeats

Dependencies:abind affy affyio AnnotationFilter askpass base64enc BH Biobase BiocBaseUtils BiocGenerics BiocManager BiocParallel bslib cachem caret class cli clock clue cluster codetools colorspace commonmark cpp11 crayon crosstalk curl data.table DBI DelayedArray diagram digest doParallel dplyr e1071 ellipsis evaluate fansi farver fastmap fontawesome foreach formatR fs futile.logger futile.options future future.apply generics GenomeInfoDb GenomeInfoDbData GenomicRanges ggplot2 globals glue gower gtable hardhat highr hms htmltools htmlwidgets httpuv httr igraph impute ipred IRanges isoband iterators jquerylib jsonlite KernSmooth keys knitr labeling lambda.r later lattice lava lazyeval lifecycle limma listenv lubridate magrittr MALDIquant MASS MassSpecWavelet Matrix MatrixGenerics matrixStats memoise MetaboCoreUtils mgcv mime ModelMetrics MsCoreUtils MsExperiment MsFeatures MSnbase MultiAssayExperiment munsell mzID mzR ncdf4 nlme nnet numDeriv openssl parallelly pcaMethods pillar pkgconfig plotly plyr preprocessCore prettyunits pROC prodlim progress progressr promises ProtGenerics proxy PSMatch purrr QFeatures R6 RaMS rappdirs RColorBrewer Rcpp recipes reshape2 Rhdf5lib rlang rmarkdown rpart S4Arrays S4Vectors sass scales shape shiny snow sourcetools SparseArray Spectra SQUAREM statmod stringi stringr SummarizedExperiment survival sys tibble tidyr tidyselect timechange timeDate tinytex tzdb UCSC.utils utf8 vctrs viridisLite vsn withr xcms xfun XML xml2 xtable XVector yaml zlibbioc

Introduction to squallms

William Kumler

Rendered fromintro_to_squallms.Rmdusingknitr::rmarkdownon Jun 16 2024.

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

Citation

Development and contributors

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