protViz (version 0.7.9)

pgLFQtNpq: Label Free Quantification using the top N peptide approach

Description

This Function implements the recently emerged TopN strategy which uses only the top N intense features for calculating the proteinVolume. This approach should reveal a quantitative protein value, which should make the protein itself comparable within one condition. This allows to estimate protein stochiometries and simplifies modelling and calculations with copy numbers per cell.

Usage

pgLFQtNpq(QuantitativeValue, 
    peptide, protein, N=3, plot=TRUE, FUN=asinh)

Arguments

QuantitativeValue

a data set like pgLFQfeature$"Normalized abundance".

peptide

a vector of peptide sequences.

protein

a vector of protein information.

N

top N peptide flyers.

plot

logical. If 'TRUE' (non default), a boxplot is drawn.

FUN

function for doing the data transformation for the correlation matrix for the image plot, default transformation is asinh.

Author

Christian Panse, Jonas Grossmann 2012

Details

The approach has first been described by Silva et al. in 2005 for Waters Q-tof instruments running in the MSe mode. Grossmann et al, showed in 2010 that this approach also works for more widely spread instruments such as Orbitrap-Velos or FTICR instruments from Thermo.

todo: additional columns (or additonal object) for 'protein names' and the total number of features assigned to protein in the master map. The length should be the same as for how many Ns chosen in the assembly method. Double check, if 'empty' protein names.. - basically - not assigned features.. are also in the list! - get rid of it.

References

tools:::Rd_expr_doi("10.1074/mcp.M500230-MCP200") tools:::Rd_expr_doi("10.1016/j.jprot.2010.05.011")

Examples

    data(pgLFQfeature)
    par(mfrow=c(2,4), mar=c(4,4,4,1))
    for (i in c(1, 2, 3, 4)){
        tNpq<-pgLFQtNpq(QuantitativeValue=pgLFQfeature$"Normalized abundance",
            peptide=pgLFQfeature$peptideInfo$Sequence,
            protein=pgLFQfeature$peptideInfo$Protein, 
            N=i)
    }

    for (i in c(1, 2, 3, 4)){
        tNpq<-pgLFQtNpq(QuantitativeValue=pgLFQfeature$"Normalized abundance",
            peptide=pgLFQfeature$peptideInfo$Sequence,
            protein=pgLFQfeature$peptideInfo$Protein, 
            plot=FALSE,
            N=i)

        boxplot(t(tNpq), xlab='proteins', ylab='protein value')
    }