imp4p (version 1.2)

impute.mi: Imputation of data sets containing peptide intensities with a multiple imputation strategy.

Description

This function allows imputing data sets containing peptide intensities with a multiple imputation strategy distinguishing MCAR and MNAR values. For details, see Giai Gianetto Q. et al. (2020) (doi: 10.1101/2020.05.29.122770).

Usage

impute.mi(tab, conditions, repbio=NULL, reptech=NULL, nb.iter=3, nknn=15, selec=1000,
siz=900, weight=1, ind.comp=1, progress.bar=TRUE, x.step.mod=300, x.step.pi=300,
nb.rei=100, q=0.95, methodMCAR="mle",ncp.max=5,
maxiter = 10, ntree = 100, variablewise = FALSE,
decreasing = FALSE, verbose = FALSE, mtry = floor(sqrt(ncol(tab))),
replace = TRUE, classwt = NULL, cutoff = NULL, strata = NULL, sampsize = NULL,
nodesize = NULL, maxnodes = NULL, xtrue = NA,
parallelize = c('no', 'variables', 'forests'),
methodMNAR="igcda",q.min = 0.025, q.norm = 3, eps = 0,
distribution = "unif", param1 = 3, param2 = 1, R.q.min=1);

Arguments

tab

A data matrix containing only 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.

repbio

A vector of factors indicating the biological replicate to which each column belongs. Default is NULL (no experimental design is considered).

reptech

A vector of factors indicating the technical replicate to which each column belongs. Default is NULL (no experimental design is considered).

nb.iter

The number of iterations used for the multiple imputation method (see mi.mix).

methodMCAR

The method used for imputing MCAR data. If methodMCAR="mle" (default), then the impute.mle function is used (imputation using an EM algorithm). If methodMCAR="pca", then the impute.PCA function is used (imputation using Principal Component Analysis). If methodMCAR="rf", then the impute.RF function is used (imputation using Random Forest). Else, the impute.slsa function is used (imputation using Least Squares on nearest neighbours).

methodMNAR

The method used for imputing MNAR data. If methodMNAR="igcda" (default), then the impute.igcda function is used. Else, the impute.pa function is used.

nknn

The number of nearest neighbours used in the SLSA algorithm (see impute.slsa).

selec

A parameter to select a part of the dataset to find nearest neighbours between rows. This can be useful for big data sets (see impute.slsa).

siz

A parameter to select a part of the dataset to perform imputations with the MCAR-devoted algorithm. This can be useful for big data sets (see mi.mix).

weight

The way of weighting in the algorithm (see impute.slsa).

ind.comp

If ind.comp=1, only nearest neighbours without missing values are selected to fit linear models (see impute.slsa). Else, they can contain missing values.

progress.bar

If TRUE, a progress bar is displayed.

x.step.mod

The number of points in the intervals used for estimating the cumulative distribution functions of the mixing model in each column (see estim.mix).

x.step.pi

The number of points in the intervals used for estimating the proportion of MCAR values in each column (see estim.mix).

nb.rei

The number of initializations of the minimization algorithm used to estimate the proportion of MCAR values (see Details) (see estim.mix).

q

A quantile value (see impute.igcda).

ncp.max

parameter of the impute.PCA function.

maxiter

parameter of the impute.RF function.

ntree

parameter of the impute.RF function.

variablewise

parameter of the impute.RF function.

decreasing

parameter of the impute.RF function.

verbose

parameter of the impute.RF function.

mtry

parameter of the impute.RF function.

replace

parameter of the impute.RF function.

classwt

parameter of the impute.RF function.

cutoff

parameter of the impute.RF function.

strata

parameter of the impute.RF function.

sampsize

parameter of the impute.RF function.

nodesize

parameter of the impute.RF function.

maxnodes

parameter of the impute.RF function.

xtrue

parameter of the impute.RF function.

parallelize

parameter of the impute.RF function.

q.min

parameter of the impute.pa function.

q.norm

parameter of the impute.pa function.

eps

parameter of the impute.pa function.

distribution

parameter of the impute.pa function.

param1

parameter of the impute.pa function.

param2

parameter of the impute.pa function.

R.q.min

parameter of the impute.pa function.

Value

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

Details

First, a mixture model of MCAR and MNAR values is estimated in each column of tab. This model is used to estimate probabilities that each missing value is MCAR. Then, these probabilities are used to perform a multiple imputation strategy (see mi.mix). Rows with no value in a condition are imputed using the impute.pa function. More details and explanations can be bound in Giai Gianetto (2020).

References

Giai Gianetto, Q., Wieczorek S., Cout<U+00E9> Y., Burger, T. (2020). A peptide-level multiple imputation strategy accounting for the different natures of missing values in proteomics data. bioRxiv 2020.05.29.122770; doi: 10.1101/2020.05.29.122770

Examples

# NOT RUN {
#Simulating data
res.sim=sim.data(nb.pept=2000,nb.miss=600,nb.cond=2);

#Imputation of the dataset noting the conditions to which the samples belong.
result=impute.mi(tab=res.sim$dat.obs, conditions=res.sim$conditions);

#Imputation of the dataset noting the conditions to which the samples belong
#and also their biological replicate, and using the SLSA method for the MCAR values
result=impute.mi(tab=res.sim$dat.obs, conditions=res.sim$conditions,
repbio=res.sim$repbio, methodMCAR = "slsa");

#For large data sets, the SLSA imputation can be accelerated thanks to the selec parameter
#and the siz parameter (see impute.slsa and mi.mix)
#but it may result in a less accurate data imputation. Note that selec has to be greater than siz.
#Here, nb.iter is fixed to 3
result1=impute.mi(tab=res.sim$dat.obs, conditions=res.sim$conditions, progress.bar=TRUE,
selec=400, siz=300, nb.iter=3, methodMCAR = "slsa", methodMNAR = "igcda");
# }