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Bioinformatics Advance Access published online on October 10, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti708
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received June 9, 2005
Revised September 20, 2005
Accepted October 5, 2005

Article

The influence of missing value imputation on detection of differentially expressed genes from microarray data

Ida Scheel 1*, Magne Aldrin 2, Ingrid K. Glad 1, Ragnhild Sørum 1, Heidi Lyng 3, and Arnoldo Frigessi 4

1 Department of Mathematics, University of Oslo, PO Box 1053, Blindern, NO-0316 Oslo, Norway
2 Department of Statistical Analysis, Image Analysis and Pattern Recognition, Norwegian Computing Center, NO-0314 Oslo, Norway
3 Department of Radiation Biology, The Norwegian Radium Hospital, NO-0310 Oslo, Norway
4 Department of Statistical Analysis, Image Analysis and Pattern Recognition, Norwegian Computing Center, NO-0314 Oslo, Norway; Department of Biostatistics, University of Oslo, NO-0317 Oslo, Norway

* To whom correspondence should be addressed.
Ida Scheel, E-mail: idasch{at}math.uio.no


  Abstract

Motivation: Missing values are problematic for the analysis of microarray data. Imputation methods have been compared in terms of the similarity between imputed and true values in simulation experiments, and not of their influence on the final analysis. The focus has been on missing at random, while entries are missing also not at random.

Results: We investigate the influence of imputation on the detection of differentially expressed genes from cDNA microarray data. We apply ANOVA for microarrays and SAM and look to the differentially expressed genes that are lost because of imputation. We show that this new measure provides useful information that the traditional root mean squared error cannot capture. We also show that the type of missingness matters: imputing 5% missing not at random has the same effect as imputing 10-30% missing at random. We propose a new method for imputation (LinImp), fitting a simple linear model for each channel separately, and compare it with the widely used KNNimpute method. For 10% missing at random, KNNimpute leads to twice as many lost differentially expressed genes as LinImp.

Availability: The R package for LinImp is available from http://folk.uio.no/idasch/imp.

Supplementary information: http://folk.uio.no/idasch/imp.


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