DNA microarray data imputation and significance analysis of differential expression
Abstract
MOTIVATION: Significance analysis of differential expression in DNA microarray data is an important task. Much of the current research is focused on developing improved tests and software tools. The task is difficult not only owing to the high dimensionality of the data (number of genes), but also because of the often non-negligible presence of missing values. There is thus a great need to reliably impute these missing values prior to the statistical analyses. Many imputation methods have been developed for DNA microarray data, but their impact on statistical analyses has not been well studied. In this work we examine how missing values and their imputation affect significance analysis of differential expression. RESULTS: We develop a new imputation method (LinCmb) that is superior to the widely used methods in terms of normalized root mean squared error. Its estimates are the convex combinations of the estimates of existing methods. We find that LinCmb adapts to the structure of the data: If the data are heterogeneous or if there are few missing values, LinCmb puts more weight on local imputation methods; if the data are homogeneous or if there are many missing values, LinCmb puts more weight on global imputation methods. Thus, LinCmb is a useful tool to understand the merits of different imputation methods. We also demonstrate that missing values affect significance analysis. Two datasets, different amounts of missing values, different imputation methods, the standard t-test and the regularized t-test and ANOVA are employed in the simulations. We conclude that good imputation alleviates the impact of missing values and should be an integral part of microarray data analysis. The most competitive methods are LinCmb, GMC and BPCA. Popular imputation schemes such as SVD, row mean, and KNN all exhibit high variance and poor performance. The regularized t-test is less affected by missing values than the standard t-test. AVAILABILITY: Matlab code is available on request from the authors.
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Missing values in large-scale 'omics data can hinder downstream analyses.
“It has been shown that missing values in large-scale ‘omics data can hinder downstream analyses (Ouyang et al., 2004; Jörnsten et al., 2005).”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Jörnsten et al., 2005
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Missing data in multi-omics integration: Recent advances through artificial intelligence
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- Authors
- Jörnsten, R., Wang, H.-Y., Welsh, W. J., & Ouyang, M.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Groupe de Recherche sur les formes Injectables et les Technologies Associées (FR)
- University of Medicine and Dentistry of New Jersey (US)
- Corresponding author
- Jörnsten, Rebecka
- Ouyang, Ming
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2005-08-23
- Volume
- 21
- Issue
- 22
- Pages
- 4155-4161
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2136714708
- Cited by
- 121
- References
- 22
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 4 · 2022: 7 · 2021: 3 · 2020: 8 · 2019: 3 · 2018: 5 · 2017: 7 · 2016: 8 · 2015: 10 · 2014: 5 · 2013: 9 · 2012: 6
- FWCI
- 3.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. National Library of Medicine (G08LM006230, G08 LM06230-03A1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- microarray data, missing data imputation, differential gene expression, significance assessment, analysis of variance, high-dimensional data, imputation method comparison
- MeSH terms
- Algorithms, Analysis of Variance, Data Interpretation, Statistical, False Positive Reactions, Gene Expression Regulation, Humans, Liver, Liver Neoplasms, Models, Genetic, Software, Models, Statistical, Cluster Analysis, Sample Size, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Microarray Analysis
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