Package: msmsEDA 1.50.0
Josep Gregori
msmsEDA: Exploratory Data Analysis of LC-MS/MS data by spectral counts
Exploratory data analysis to assess the quality of a set of LC-MS/MS experiments, and visualize de influence of the involved factors.
Authors:
msmsEDA_1.50.0.tar.gz
msmsEDA_1.50.0.zip(r-4.7)msmsEDA_1.50.0.zip(r-4.6)msmsEDA_1.50.0.zip(r-4.5)
msmsEDA_1.50.0.tgz(r-4.6-any)msmsEDA_1.50.0.tgz(r-4.5-any)
msmsEDA_1.50.0.tar.gz(r-4.7-any)msmsEDA_1.50.0.tar.gz(r-4.6-any)
msmsEDA_1.50.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
msmsEDA/json (API)
| # Install 'msmsEDA' in R: |
| install.packages('msmsEDA', repos = c('https://bioc-release.r-universe.dev', 'https://cloud.r-project.org')) |
- msms.dataset - LC-MS/MS dataset
- pnms - Accessions and gene symbols
On BioConductor:msmsEDA-1.51.0(bioc 3.24)msmsEDA-1.50.0(bioc 3.23)
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
immunooncologysoftwaremassspectrometryproteomics
Last updated from:76eaebdc10 (on RELEASE_3_23). Checks:1 ERROR, 7 NOTE, 2 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| bioc-checks | ERROR | 186 | ||
| linux-devel-x86_64 | NOTE | 278 | ||
| source / vignettes | OK | 299 | ||
| linux-release-x86_64 | NOTE | 374 | ||
| macos-release-arm64 | NOTE | 170 | ||
| macos-oldrel-arm64 | NOTE | 167 | ||
| windows-devel | NOTE | 250 | ||
| windows-release | NOTE | 211 | ||
| windows-oldrel | NOTE | 232 | ||
| wasm-release | OK | 152 |
Exports:batch.neutralizecount.statscounts.hccounts.heatmapcounts.pcadisp.estimatesfilter.flagsgene.tablenorm.countspp.msms.dataspc.barplotsspc.boxplotsspc.densityplotsspc.scatterplot
Dependencies:abindaffyaffyioAnnotationFilteraskpassbase64encBHBiobaseBiocBaseUtilsBiocGenericsbiocmakeBiocManagerBiocParallelbitopsbslibcachemcaToolscliclueclustercodetoolscpp11crosstalkcurldata.tableDelayedArraydigestdir.expirydoParalleldplyrevaluatefarverfastmapfilelockfontawesomeforeachformatRfsfutile.loggerfutile.optionsgenericsGenomicRangesggplot2gluegplotsgtablegtoolshighrhtmltoolshtmlwidgetshttrigraphimputeIRangesisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglambda.rlaterlatticelazyevallifecyclelimmamagrittrMALDIquantMASSMatrixMatrixGenericsmatrixStatsmemoiseMetaboCoreUtilsmimeMsCoreUtilsMSnbaseMultiAssayExperimentmzIDmzRncdf4opensslotelpcaMethodspillarpkgconfigplotlyplyrpreprocessCorepromisesProtGenericsPSMatchPTModspurrrQFeaturesR6rappdirsRColorBrewerRcppreshape2Rhdf5librlangrmarkdownS4ArraysS4VectorsS7sassscalesSeqinfosnowSparseArraySpectrastatmodstringistringrSummarizedExperimentsystibbletidyrtidyselecttinytexutf8vctrsviridisLitevsnwithrxfunXMLXVectoryaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Exploratory Data Analysis of label-free LC-MS/MS spectral counts | msmsEDA-package msmsEDA |
| Batch effects correction | batch.neutralize |
| Summary of statistics of spectral counts by sample in the dataset | count.stats |
| Hierarchical clustering on an spectral counts matrix. | counts.hc |
| Heatmap of an spectral counts matrix. | counts.heatmap |
| Principal components analysis of an spectral counts matrix. | counts.pca |
| Residual dispersion estimates | disp.estimates |
| Flag proteins with a minimum signal and/or sufficient dispersion. | filter.flags |
| Gene symbols associated to protein accessions | gene.table |
| LC-MS/MS dataset | msms.dataset |
| Spectral counts matrix normalization | norm.counts |
| Accessions and gene symbols | pnms |
| Spectral counts matrix pre-processing | pp.msms.data |
| Set of SpC barplots by sample | spc.barplots |
| Set of SpC boxplots by sample | spc.boxplots |
| SpC density plots of a SpC matrix | spc.densityplots |
| Scatterplot of SpC means comparing two conditions | spc.scatterplot |
