Exploratory Data Analysis of label-free LC-MS/MS spectral counts


Exploratory data analysis to assess the quality of a set of label-free LC-MS/MS experiments, quantified by spectral counts, and visualize de influence of the involved factors. Visualization tools to assess quality and to discover outliers and eventual confounding.


Package: msmsEDA
Type: Package
Version: 1.2.0
Date: 2014-01-18
License: GPL-2
pp.msms.data data preprocessing
gene.table extract gene symbols from protein description
count.stats summaries by sample
counts.pca principal components analysis
counts.hc hierarchical clustering of samples
norm.counts normalization of spectral counts matrix
counts.heatmap experiment heatmap
disp.estimates dispersion analysis and plots
filter.flags flag informative features
spc.barplots sample sizes barplots
spc.boxplots samples SpC boxplots
spc.densityplot samples SpC density plots
spc.scatterplot scatterplot comparing two conditions
batch.neutralize batch effects correction


Josep Gregori, Alex Sanchez and Josep Villanueva
Maintainer: Josep Gregori <josep.gregori@gmail.com>


Gregori J, Villarreal L, Mendez O, Sanchez A, Baselga J, Villanueva J, "Batch effects correction improves the sensitivity of significance tests in spectral counting-based comparative discovery proteomics." J Proteomics. 2012 Jul 16;75(13):3938-51. doi: 10.1016/j.jprot.2012.05.005. Epub 2012 May 12.

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