Imputing missing values for genetic interaction data

Abstract

BackgroundEpistatic Miniarray Profiles (EMAP) enable the research of genetic interaction as an important method to construct large-scale genetic interaction networks. However, a high proportion of missing values frequently poses problems in EMAP data analysis since such missing values hinder downstream analysis. While some imputation approaches have been available to EMAP data, we adopted an improved SVD modeling procedure to impute the missing values in EMAP data which has resulted in a higher accuracy rate compared with existing methods. ResultsThe improved SVD imputation method adopts an effective soft-threshold to the SVD approach which has been shown to be the best model to impute genetic interaction data when compared with a number of advanced imputation methods. Imputation methods also improve the clustering results of EMAP datasets. Thus, after applying our imputation method on the EMAP dataset, more meaningful modules, known pathways and protein complexes could be detected. ConclusionWhile the phenomenon of missing data unavoidably complicates EMAP data, our results showed that we could complete the original dataset by the Soft-SVD approach to accurately recover genetic interactions.