Package: normScore 0.99.1
normScore: Evaluation and Ranking of Normalization Methods for Proteomics Data
Provides tools to evaluate and rank normalization methods for omics datasets using a composite score derived from multiple performance metrics. The package is designed to support systematic benchmarking and comparison of normalization strategies across datasets and experimental settings. It also includes utilities for summarizing results and visualizing normalization performance.
Authors:
normScore_0.99.1.tar.gz
normScore_0.99.1.zip(r-4.7-any)normScore_0.99.1.zip(r-4.6-any)normScore_0.99.1.zip(r-4.5-any)
normScore_0.99.1.tgz(r-4.6-any)normScore_0.99.1.tgz(r-4.5-any)
normScore_0.99.1.tar.gz(r-4.7-any)normScore_0.99.1.tar.gz(r-4.6-any)
normScore_0.99.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
normScore/json (API)
| # Install 'normScore' in R: |
| install.packages('normScore', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/juliagcurras/normscore/issues
Pkgdown/docs site:https://juliagcurras.github.io
On BioConductor:normScore-0.99.1(bioc 3.24)
softwareproteomicsnormalizationqualitycontrolpreprocessingmultiplecomparison
Last updated from:38e8616916. Checks:1 WARNING, 9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| bioc-checks | WARNING | 146 | ||
| linux-devel | OK | 249 | ||
| source / vignettes | OK | 267 | ||
| linux-release | OK | 252 | ||
| macos-release | OK | 109 | ||
| macos-oldrel | OK | 161 | ||
| windows-devel | OK | 122 | ||
| windows-release | OK | 122 | ||
| windows-oldrel | OK | 113 | ||
| wasm-release | OK | 127 |
Exports:normScoreplotBootstrapNormScoreplotDiagnosticMAplotDiagnosticMeanSDplotDiagnosticPCVplotDiagnosticRLEplotDiagnosticSystematicBiasplotDiagnosticTotalIntensityplotDiagnosticWithinGroupCorplotNormScoreDiagnosticssimulateDatavalidateNormScoreInput
Dependencies:abindbackportsbootbroomcarcarDataclicolorspacecorrplotcowplotcpp11DerivdoBydplyrfarverforecastFormulafracdiffgenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragtableisobandlabelinglatticelifecyclelme4lmtestmagrittrMASSMatrixMatrixModelsmgcvminqamodelrnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpolynompurrrquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangrstatixS7scalesSparseMstringistringrsurvivaltibbletidyrtidyselecttimeDateurcautf8vctrsviridisLitewithrzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Rank Normalization Methods Using a Composite Scoring Framework | normScore |
| Plot bootstrap normScore results | plotBootstrapNormScore |
| Plot MA diagnostic plots (Item 3) | plotDiagnosticMA |
| Plot mean-SD diagnostic plots (Item 4) | plotDiagnosticMeanSD |
| Plot pooled coefficient of variation (Item 1) | plotDiagnosticPCV |
| Plot RLE distributions (Item 5) | plotDiagnosticRLE |
| Plot functions for original criteria | plotDiagnosticSystematicBias |
| Plot log-intensity distributions (Item 6) | plotDiagnosticTotalIntensity |
| Plot within-group correlations (Item 2) | plotDiagnosticWithinGroupCor |
| Plot normScore diagnostic items | plotNormScoreDiagnostics |
| Simulate label-free proteomics-like two-group data | simulateData |
| Validate and prepare inputs for normScore and diagnostic graphs | validateNormScoreInput |
