Package: msmsEDA 1.45.0

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:Josep Gregori, Alex Sanchez, and Josep Villanueva

msmsEDA_1.45.0.tar.gz
msmsEDA_1.45.0.zip(r-4.5)msmsEDA_1.45.0.zip(r-4.4)msmsEDA_1.45.0.zip(r-4.3)
msmsEDA_1.45.0.tgz(r-4.4-any)msmsEDA_1.45.0.tgz(r-4.3-any)
msmsEDA_1.45.0.tar.gz(r-4.5-noble)msmsEDA_1.45.0.tar.gz(r-4.4-noble)
msmsEDA_1.45.0.tgz(r-4.4-emscripten)msmsEDA_1.45.0.tgz(r-4.3-emscripten)
msmsEDA.pdf |msmsEDA.html✨
msmsEDA/json (API)

# Install 'msmsEDA' in R:

install.packages('msmsEDA', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

msms.dataset - LC-MS/MS dataset
pnms - Accessions and gene symbols

On BioConductor:msmsEDA-1.45.0(bioc 3.21)msmsEDA-1.44.0(bioc 3.20)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

immunooncology software massspectrometry proteomics

4.38 score 2 packages 4 scripts 553 downloads 2 mentions 14 exports 132 dependencies

Last updated 2 months agofrom:42fe18df12. Checks:OK: 1 NOTE: 6. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 29 2024
R-4.5-win	NOTE	Nov 29 2024
R-4.5-linux	NOTE	Nov 29 2024
R-4.4-win	NOTE	Nov 29 2024
R-4.4-mac	NOTE	Nov 29 2024
R-4.3-win	NOTE	Nov 29 2024
R-4.3-mac	NOTE	Nov 29 2024

Exports:batch.neutralize count.stats counts.hc counts.heatmap counts.pca disp.estimates filter.flags gene.table norm.counts pp.msms.data spc.barplots spc.boxplots spc.densityplots spc.scatterplot

Dependencies:abind affy affyio AnnotationFilter askpass base64enc BH Biobase BiocBaseUtils BiocGenerics BiocManager BiocParallel bitops bslib cachem caTools cli clue cluster codetools colorspace cpp11 crayon crosstalk curl data.table DelayedArray digest doParallel dplyr evaluate fansi farver fastmap fontawesome foreach formatR fs futile.logger futile.options generics GenomeInfoDb GenomeInfoDbData GenomicRanges ggplot2 glue gplots gtable gtools highr htmltools htmlwidgets httr igraph impute IRanges isoband iterators jquerylib jsonlite KernSmooth knitr labeling lambda.r later lattice lazyeval lifecycle limma magrittr MALDIquant MASS Matrix MatrixGenerics matrixStats memoise mgcv mime MsCoreUtils MSnbase MultiAssayExperiment munsell mzID mzR ncdf4 nlme openssl pcaMethods pillar pkgconfig plotly plyr preprocessCore promises ProtGenerics PSMatch purrr QFeatures R6 rappdirs RColorBrewer Rcpp reshape2 Rhdf5lib rlang rmarkdown S4Arrays S4Vectors sass scales snow SparseArray statmod stringi stringr SummarizedExperiment sys tibble tidyr tidyselect tinytex UCSC.utils utf8 vctrs viridisLite vsn withr xfun XML XVector yaml zlibbioc

msmsEDA: Batch effects detection in LC-MSMS experiments

Rendered frommsmsData-Vignette.Rnwusingutils::Sweaveon Nov 29 2024.

Last update: 2014-01-19
Started: 2013-11-01

Citation

Development and contributors

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

Usage by other packages (reverse dependencies)