The influence of missing value imputation on detection of differentially expressed genes from microarray data
Open Access
- 10 October 2005
- journal article
- research article
- Published by Oxford University Press (OUP) in Bioinformatics
- Vol. 21 (23), 4272-4279
- https://doi.org/10.1093/bioinformatics/bti708
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 at Contact:idasch@math.uio.no Supplementary information:This publication has 15 references indexed in Scilit:
- Prediction of Missing Values in Microarray and Use of Mixed Models to Evaluate the PredictorsStatistical Applications in Genetics and Molecular Biology, 2005
- Missing value estimation for DNA microarray gene expression data: local least squares imputationBioinformatics, 2004
- Influence of microarrays experiments missing values on the stability of gene groups by hierarchical clusteringBMC Bioinformatics, 2004
- LSimpute: accurate estimation of missing values in microarray data with least squares methodsNucleic Acids Research, 2004
- Gaussian mixture clustering and imputation of microarray dataBioinformatics, 2004
- Statistical Challenges in Functional GenomicsStatistical Science, 2003
- Diagnosis of multiple cancer types by shrunken centroids of gene expressionProceedings of the National Academy of Sciences of the United States of America, 2002
- Genomic Expression Responses to DNA-damaging Agents and the Regulatory Role of the Yeast ATR Homolog Mec1pMolecular Biology of the Cell, 2001
- Significance analysis of microarrays applied to the ionizing radiation responseProceedings of the National Academy of Sciences of the United States of America, 2001
- Analysis of Variance for Gene Expression Microarray DataJournal of Computational Biology, 2000