Computes the SpC matrix where the fixed effects of a blocking factor are substracted.
● Data Source:
BioConductor
● Keywords: manip
● Alias: batch.neutralize
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3 images
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Computes the number of proteins identified, the total spectral counts, and a summary of each sample
● Data Source:
BioConductor
● Keywords: univar
● Alias: count.stats
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0 images
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Hierarchical clustering of samples in an spectral counts matrix, coloring tree branches according to factor levels.
● Data Source:
BioConductor
● Keywords: hplot, multivariate
● Alias: counts.hc
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2 images
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Heatmap showing the clustering of proteins and samples in a matrix of spectral counts
● Data Source:
BioConductor
● Keywords: hplot, multivariate
● Alias: counts.heatmap
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1 images
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A summary and different plots are given as a result of principal components analysis of an spectral counts matrix.
● Data Source:
BioConductor
● Keywords: hplot, multivariate
● Alias: counts.pca
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2 images
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Estimates the residual dispersion of each row of a spectral counts matrix as the ratio residual variance to mean of mean values by level, for each factor in facs. Different plots are drawn to help in the interpretation of the results.
● Data Source:
BioConductor
● Keywords: distribution, hplot
● Alias: disp.estimates
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4 images
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In general the spectral counts (SpC) matrix of a LC-MS/MS experiment is a sparse matrix, where most of the features have very low signal. Besides, the features with low variance to mean ratio (dispersion) will be scarcely informative in a biomarker discovery experiment. Given a minimum number of spectral counts and/or a fraction of the features to be excluded by low dispersion, this function returns a vector of logicals flagging all features with values above the given thresholds.
● Data Source:
BioConductor
● Keywords: manip
● Alias: filter.flags
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0 images
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Given a character vector with protein accessions, and a character vector with protein descriptions including gene symbols, returns a character vector with gene symbols whose names are the protein accessions. A character pattern should also be given to match the gene symbols.
● Data Source:
BioConductor
● Keywords: manip
● Alias: gene.table
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0 images
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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.
● Data Source:
BioConductor
● Keywords: cluster, hplot, multivariate, package
● Alias: msmsEDA, msmsEDA-package
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0 images
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An spectral counts matrix is normalized by means of a set of samples divisors.
● Data Source:
BioConductor
● Keywords: manip
● Alias: norm.counts
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0 images
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providing 
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