Wrapper function for KNN imputation.
This function is a wrapper around the impute.knn
function from impute package that performs KNN imputation for a data matrix containing missing entries.
impute.wrapper.KNN(dataSet.mvs, K)
dataSet.mvs |
A data matrix containing left-censored missing entries. |
K |
T he number of neighbors used to infer the missing data. |
Requires impute package.
A complete expression data matrix with missing values imputed.
Cosmin Lazar
Hastie, T., Tibshirani, R., Sherlock, G., Eisen, M., Brown, P. and Botstein, D., Imputing Missing Data for Gene Expression Arrays, Stanford University Statistics Department Technical report (1999), http://www-stat.stanford.edu/~hastie/Papers/missing.pdf
Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown, Trevor Hastie, Robert Tibshirani, David Botstein and Russ B. Altman, Missing value estimation methods for DNA microarrays BIOINFORMATICS Vol. 17 no. 6, 2001 Pages 520-525
# 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 exprsData.imputed = impute.wrapper.KNN(exprsData.MD,15) ## The function is currently defined as function (dataSet.mvs, K) { resultKNN = impute.knn(dataSet.mvs, k = K, rowmax = 0.99, colmax = 0.99, maxp = 1500, rng.seed = sample(1:1000, 1)) dataSet.imputed = resultKNN[[1]] return(dataSet.imputed) }
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