Imputation of peptides having no value in a biological condition (present in a condition / absent in another).

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Description

This function imputes missing values by small values.

Usage

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impute.pa(tab, conditions, q.min=0, q.norm=3, eps=2)

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.

q.min

A quantile value of the observed values allowing defining the maximal value which can be generated. This maximal value is defined by the quantile q.min of the observed values distribution minus eps. Default is 0 (the maximal value is the minimum of observed values minus eps).

q.norm

A quantile value of a normal distribution allowing defining the minimal value which can be generated. Default is 3 (the minimal value is the maximal value minus qn*median(sd(observed values)) where sd is the standard deviation of a row in a condition).

eps

A value allowing defining the maximal value which can be generated. This maximal value is defined by the quantile q.min of the observed values distribution minus eps. Default is 2.

Details

This function replaces the missing values of the rows by random draws from an uniform distribution between the defined minimal value and maximal value. The value of eps can be interpreted as a minimal fold-change value above which the present/absent peptides appear.

Value

The input matrix tab with imputed values instead of missing values.

Author(s)

Quentin Giai Gianetto <quentin2g@yahoo.fr>

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);

#Imputation of the simulated data set with small values
data.small.val=impute.pa(res.sim$dat.obs,res.sim$conditions);

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