Estimating the proportion of MCAR values in biological conditions using the method of Karpievitch (2009).

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Description

This function allows estimating the proportion of MCAR values in biological conditions using the method of Karpievitch (2009).

Usage

1

Arguments

tab

A data matrix containing numeric and missing values. Each column of this matrix is assumed to correspond to an experimental sample, and each row to an identified peptide.

conditions

A vector of factors indicating the biological condition to which each column (experimental sample) belongs.

Value

A list composed of:

pi.mcar

The proportion of MCAR values in each biological condition.

prop.na

The proportion of missing values for each peptide in each condition.

moy

The average of observed values for each peptide in each condition.

Author(s)

Quentin Giai Gianetto <quentin2g@yahoo.fr>

References

Karpievitch, Y., Stanley, J., Taverner, T., Huang, J., Adkins, J. N., Ansong, C., ... & Smith, R. D. (2009). A statistical framework for protein quantitation in bottom-up MS-based proteomics. Bioinformatics, 25(16), 2028-2034.

See Also

estim.mix

Examples

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#Simulating data
res.sim=sim.data(nb.pept=2000,nb.miss=600,pi.mcar=0.2,para=10,nb.cond=2,nb.repbio=3,
nb.sample=5,m.c=25,sd.c=2,sd.rb=0.5,sd.r=0.2);

#Deleting rows without any observed value in a condition
result=delete.na.rows(tab=res.sim$dat.obs, tab.c=res.sim$dat.comp, conditions=res.sim$conditions,
list.MCAR=res.sim$list.MCAR);

#Proportion of MCAR values in each condition
pi.mcar.karpievitch(tab=result$tab.mod,conditions=res.sim$conditions)

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