imputeLCMD (version 2.0)

impute.QRILC: Imputation of left-censored missing data using QRILC method.

Description

This function implements QRILC, a missing data imputation method that performs the imputation of left-censored missing data using random draws from a truncated distribution with parameters estimated using quantile regression.

Usage

impute.QRILC(dataSet.mvs, tune.sigma = 1)

Arguments

dataSet.mvs

A data matrix containing left-censored missing data.

tune.sigma

A scalar used to tune the standard deviation (if the complete data distribution is not Gaussian). The default value is tune.sigma = 1, and it corresponds to the case where the complete data distribution is Gaussian.

Value

A list including elements:

dataSet.imputed

A complete expression data matrix with missing values imputed.

QR.obj

A lm object that contains the results of the QQ regression, denoting the mean and the standard deviation of the complete data distribution.

References

QRILC: a quantile regression approach for the imputation of left-censored missing data in quantitative proteomics, Cosmin Lazar et al. - to be submitted.

See Also

impute.MinDet, impute.MinProb, impute.ZERO

Examples

# NOT RUN {
# generate expression data matrix
exprsDataObj = generate.ExpressionData(nSamples1 = 6, nSamples2 = 6,
                          meanSamples = 0, sdSamples = 0.2,
                          nFeatures = 1000, nFeaturesUp = 50, nFeaturesDown = 50,
                          meanDynRange = 20, sdDynRange = 1,
                          meanDiffAbund = 1, sdDiffAbund = 0.2)
exprsData = exprsDataObj[[1]]
  
# insert 15% missing data with 100% missing not at random
m.THR = quantile(exprsData, probs = 0.15)
sd.THR = 0.1
MNAR.rate = 100
exprsData.MD.obj = insertMVs(exprsData,m.THR,sd.THR,MNAR.rate)
exprsData.MD = exprsData.MD.obj[[2]]

# perform missing data imputation
obj.QRILC = impute.QRILC(exprsData.MD)
exprsData.imputed = obj.QRILC[[1]]

# }
# NOT RUN {
hist(exprsData[,1])
hist(exprsData.MD[,1])
hist(exprsData.imputed[,1])
# }
# NOT RUN {
## The function is currently defined as
function (dataSet.mvs, tune.sigma = 1) 
{
    nFeatures = dim(dataSet.mvs)[1]
    nSamples = dim(dataSet.mvs)[2]
    dataSet.imputed = dataSet.mvs
    QR.obj = list()
    for (i in 1:nSamples) {
        curr.sample = dataSet.mvs[, i]
        pNAs = length(which(is.na(curr.sample)))/length(curr.sample)
        upper.q = 0.95
        q.normal = qnorm(seq(pNAs, upper.q, (upper.q - pNAs)/(upper.q * 
            10000)), mean = 0, sd = 1)
        q.curr.sample = quantile(curr.sample, probs = seq(0, 
            upper.q, 1e-04), na.rm = T)
        temp.QR = lm(q.curr.sample ~ q.normal)
        QR.obj[[i]] = temp.QR
        mean.CDD = temp.QR$coefficients[1]
        sd.CDD = as.numeric(temp.QR$coefficients[2])
        data.to.imp = rtmvnorm(n = nFeatures, mean = mean.CDD, 
            sigma = sd.CDD * tune.sigma, upper = qnorm(pNAs, 
                mean = mean.CDD, sd = sd.CDD), algorithm = c("gibbs"))
        curr.sample.imputed = curr.sample
        curr.sample.imputed[which(is.na(curr.sample))] = data.to.imp[which(is.na(curr.sample))]
        dataSet.imputed[, i] = curr.sample.imputed
    }
    results = list(dataSet.imputed, QR.obj)
    return(results)
  }
# }