impute_pa: Imputation of peptides having no value in a biological...
In imp4p: Imputation for Proteomics

Description Usage Arguments Details Value Author(s) Examples

This function imputes missing values by small values.

1 2	impute.pa(tab, conditions, q.min = 0.025, q.norm = 3, eps = 0, distribution = "unif", param1 = 3, param2 = 1, R.q.min=1)

`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 0.025 (the maximal value is the 2.5 percentile 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 0.
`distribution`	Distribution used to generated missing values. You have the choice between "unif" for the uniform distribution, "beta" for the Beta distribution or "dirac" for the Dirac distribution. Default is "unif".
`param1`	Parameter `shape1` of the Beta distribution.
`param2`	Parameter `shape2` of the Beta distribution.
`R.q.min`	Parameter used for the Dirac distribution. In this case, all the missing values are imputed by a single value which is equal to `R.q.min*quantile(tab[,j], probs=q.min, na.rm=T)`. Default is 1 : the imputed value is the `qmin` quantile of observed values.

This function replaces the missing values in a column by random draws from a specified distribution. The value of eps can be interpreted as a minimal fold-change value above which the present/absent peptides appear.

A list composed of :

- tab.imp : the input matrix tab with imputed values instead of missing values.

- para : the parameters of the distribution which has been used to impute.

Quentin Giai Gianetto <quentin2g@yahoo.fr>

#Simulating data
res.sim=sim.data(nb.pept=2000,nb.miss=600);

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