Missing value imputation improves clustering and interpretation of gene expression microarray data

Background: Missing values frequently pose problems in gene expression microarray experiments as they can hinder downstream analysis of the datasets. While several missing value imputation approaches are available to the microarray users and new ones are constantly being developed, there is no general consensus on how to choose between the different methods since their performance seems to vary drastically depending on the dataset being used. Results: We show that this discrepancy can mostly be attributed to the way in which imputation methods have traditionally been developed and evaluated. By comparing a number of advanced imputation methods on recent microarray datasets, we show that even when there are marked differences in the measurement-level imputation accuracies across the datasets, these differences become negligible when the methods are evaluated in terms of how well they can reproduce the original gene clusters or their biological interpretations. Regardless of the evaluat...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Johannes Tuikkala, Laura Elo, Olli Nevalainen, Tero Aittokallio
• Keywords: Advanced Imputation Methods, BMCBI 2008, Gene Expression, Imputation Methods