Missing value estimation for DNA microarray gene expression data: local least squares imputation

  • Authors:

  • Hyunsoo Kim;Gene H. Golub;Haesun Park

  • Affiliations:

  • Department of Computer Science and Engineering, University of Minnesota Twin Cities, 200 Union Street S.E., Minneapolis, MN 55455, USA;Computer Science Department, Stanford University Gates Building 2B #280, Stanford, CA 94305-9025, USA;Department of Computer Science and Engineering, University of Minnesota Twin Cities, 200 Union Street S.E., Minneapolis, MN 55455, USA

  • Venue:
  • Bioinformatics
  • Year:
  • 2005

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Abstract

Motivation: Gene expression data often contain missing expression values. Effective missing value estimation methods are needed since many algorithms for gene expression data analysis require a complete matrix of gene array values. In this paper, imputation methods based on the least squares formulation are proposed to estimate missing values in the gene expression data, which exploit local similarity structures in the data as well as least squares optimization process. Results: The proposed local least squares imputation method (LLSimpute) represents a target gene that has missing values as a linear combination of similar genes. The similar genes are chosen by k-nearest neighbors or k coherent genes that have large absolute values of Pearson correlation coefficients. Non-parametric missing values estimation method of LLSimpute are designed by introducing an automatic k-value estimator. In our experiments, the proposed LLSimpute method shows competitive results when compared with other imputation methods for missing value estimation on various datasets and percentages of missing values in the data. Availability: The software is available at http://www.cs.umn.edu/~hskim/tools.html Contact: hpark@cs.umn.edu