iMISS is a package for integrative missing value estimation of microarray data sets with limited number of samples, high level of noise, or high rate of missing values. iMISS works by incorporating information from multiple reference microarray datasets to screen reliable neighbor genes used in basic local estimation algorithms such as the Local Least Squares (LLS) algorithm. In addition to the integrative algorithms, implementations of KNN and LLS based imputation algorithms are also provided.
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Integrative Missing Value Estimation for Microarray Data
Jianjun Hu, Haifeng Li, Michael S. Waterman, Xianghong Jasmine Zhou
BMC Bioinformatics, 2006