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

BMC Bioinformatics
Johannes TuikkalaTero Aittokallio

Abstract

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. 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 evaluation approach, however, imputation always gave better results than ignoring missing data points or replacing them with zeros or average values, emphasizing the continued importance of using more advanced imputation methods. The results demonstrate that, while missing va...Continue Reading

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Citations

Jun 21, 2011·Comparative and Functional Genomics·Feng LiWeiwen Zhang
Nov 14, 2013·Advances in Bioinformatics·Sreevidya Sadananda Sadasiva RaoJeffrey C Miecznikowski
Sep 29, 2012·Computer Methods and Programs in Biomedicine·D UrdaJ M Jerez
Apr 19, 2015·BMC Bioinformatics·Marcilio C P de SoutoIvan G Costa
Jun 9, 2017·Statistical Applications in Genetics and Molecular Biology·Shahla Faisal, Gerhard Tutz
Oct 20, 2020·Nucleic Acids Research·Christopher A MancusoArjun Krishnan
Nov 4, 2021·Nature Reviews. Rheumatology·Kathryn M KingsmorePeter E Lipsky

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Software Mentioned

SVR
Matlab
LinCmb
Nimp

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