Package: msqrob2 1.15.0
Lieven Clement
msqrob2: Robust statistical inference for quantitative LC-MS proteomics
msqrob2 provides a robust linear mixed model framework for assessing differential abundance in MS-based Quantitative proteomics experiments. Our workflows can start from raw peptide intensities or summarised protein expression values. The model parameter estimates can be stabilized by ridge regression, empirical Bayes variance estimation and robust M-estimation. msqrob2's hurde workflow can handle missing data without having to rely on hard-to-verify imputation assumptions, and, outcompetes state-of-the-art methods with and without imputation for both high and low missingness. It builds on QFeature infrastructure for quantitative mass spectrometry data to store the model results together with the raw data and preprocessed data.
Authors:
msqrob2_1.15.0.tar.gz
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msqrob2_1.15.0.tgz(r-4.4-any)msqrob2_1.15.0.tgz(r-4.3-any)
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msqrob2.pdf |msqrob2.html✨
msqrob2/json (API)
NEWS
| # Install 'msqrob2' in R: |
| install.packages('msqrob2', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/statomics/msqrob2/issues
- pe - Example data for 100 proteins
On BioConductor:msqrob2-1.13.0(bioc 3.20)msqrob2-1.12.0(bioc 3.19)
proteomicsmassspectrometrydifferentialexpressionmultiplecomparisonregressionexperimentaldesignsoftwareimmunooncologynormalizationtimecoursepreprocessing
Last updated 25 days agofrom:1d90849d96. Checks:OK: 1 NOTE: 6. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 30 2024 |
| R-4.5-win | NOTE | Oct 31 2024 |
| R-4.5-linux | NOTE | Oct 30 2024 |
| R-4.4-win | NOTE | Oct 31 2024 |
| R-4.4-mac | NOTE | Oct 31 2024 |
| R-4.3-win | NOTE | Oct 31 2024 |
| R-4.3-mac | NOTE | Oct 31 2024 |
Exports:.StatModelgetCoefgetContrastgetDFgetDfPosteriorgetFitMethodgetModelgetSigmagetSigmaPosteriorgetVargetVarPosteriorgetVcovUnscaledhypothesisTesthypothesisTestHurdlemakeContrastmsqrobmsqrobAggregatemsqrobGlmmsqrobHurdlemsqrobLmmsqrobLmermsqrobQBshowsmallestUniqueGroupsStatModeltopFeaturesvarContrast
Dependencies:abindAnnotationFilteraskpassbase64encBHBiobaseBiocBaseUtilsBiocGenericsBiocParallelbootbslibcachemcliclueclustercodetoolscolorspacecpp11crayoncrosstalkcurldata.tableDelayedArraydigestdplyrevaluatefansifarverfastmapfontawesomeformatRfsfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2gluegtablehighrhtmltoolshtmlwidgetshttrigraphIRangesisobandjquerylibjsonliteknitrlabelinglambda.rlaterlatticelazyevallifecyclelimmalme4magrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemgcvmimeminqaMsCoreUtilsMultiAssayExperimentmunsellnlmenloptropensslpillarpkgconfigplotlyplyrpromisesProtGenericspurrrQFeaturesR6rappdirsRColorBrewerRcppRcppEigenreshape2rlangrmarkdownS4ArraysS4VectorssassscalessnowSparseArraystatmodstringistringrSummarizedExperimentsystibbletidyrtidyselecttinytexUCSC.utilsutf8vctrsviridisLitewithrxfunXVectoryamlzlibbioc
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Methods for StatModel class | getContrast getContrast,StatModel-method StatModel-method statModelMethods varContrast varContrast,StatModel-method |
| Accessor functions for StatModel class | getCoef getCoef,StatModel-method getDF getDF,StatModel-method getDfPosterior getDfPosterior,StatModel-method getFitMethod getFitMethod,StatModel-method getModel getModel,StatModel-method getSigma getSigma,StatModel-method getSigmaPosterior getSigmaPosterior,StatModel-method getVar getVar,StatModel-method getVarPosterior getVarPosterior,StatModel-method getVcovUnscaled getVcovUnscaled,StatModel-method statModelAccessors |
| Parameter estimates, standard errors and statistical inference on differential expression analysis | hypothesisTest hypothesisTest,QFeatures-method hypothesisTest,SummarizedExperiment-method hypothesisTestHurdle hypothesisTestHurdle,QFeatures-method hypothesisTestHurdle,SummarizedExperiment-method |
| Make contrast matrix | makeContrast |
| Methods to fit msqrob models with ridge regression and/or random effects using lme4 | msqrob msqrob,QFeatures-method msqrob,SummarizedExperiment-method |
| Method to fit msqrob models with robust regression and/or ridge regression and/or random effects It models multiple features simultaneously, e.g. multiple peptides from the same protein. | msqrobAggregate msqrobAggregate,QFeatures-method |
| Function to fit msqrob models to peptide counts using glm | msqrobGlm |
| Function to fit msqrob hurdle models | msqrobHurdle msqrobHurdle,QFeatures-method msqrobHurdle,SummarizedExperiment-method |
| Function to fit msqrob models using lm and rlm | msqrobLm |
| Function to fit msqrob models with ridge regression and/or random effects using lme4 | msqrobLmer |
| Function to fit msqrob models to peptide counts using glm | msqrobQB msqrobQB,QFeatures-method msqrobQB,SummarizedExperiment-method |
| Example data for 100 proteins | data pe |
| Smallest unique protein groups | smallestUniqueGroups |
| The StatModel class for msqrob | .StatModel show,StatModel-method StatModel StatModel-class |
| Toplist of DE proteins, peptides or features | topFeatures |