Numeric Matrices K-NN and PCA Imputation
Calculate Matrix Column Variance
Compute Prediction Accuracy Metrics
Group Features for Imputation
Grouped K-NN or PCA Imputation
K-Nearest Neighbor Imputation for Numeric Matrices
Column Mean Imputation
Impute dataset with PCA
Print ImputedMatrix
Simulate Methylation Beta Values with Metadata
Sliding Window K-NN or PCA Imputation
Tune Parameters for Imputation Methods
Fast k-nearest neighbors (K-NN) and principal component analysis (PCA) imputation algorithms for missing values in high-dimensional numeric matrices, i.e., epigenetic data. For extremely high-dimensional data with ordered features, a sliding window approach for K-NN or PCA imputation is provided. Additional features include group-wise imputation (e.g., by chromosome), hyperparameter tuning with repeated cross-validation, multi-core parallelization, and optional subset imputation. The K-NN algorithm is described in: Hastie, T., Tibshirani, R., Sherlock, G., Eisen, M., Brown, P. and Botstein, D. (1999) "Imputing Missing Data for Gene Expression Arrays". The PCA imputation is an optimized version of the imputePCA() function from the 'missMDA' package described in: Josse, J. and Husson, F. (2016) <doi:10.18637/jss.v070.i01> "missMDA: A Package for Handling Missing Values in Multivariate Data Analysis".