An algorithm for missing value estimation for DNA microarray data

Shmuel Friedland, Amir Niknejad, Mostafa Kaveh, Hossein Zare

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Gene expression data matrices often contain missing expression values. In this paper, we describe a new algorithm, named improved fixed rank approximation algorithm (IFRAA), for missing values estimations of the large gene expression data matrices. We compare the present algorithm with the two existing and widely used methods for reconstructing missing entries for DNA microarray gene expression data: the Bayesian principal component analysis (BPCA) and the local least squares imputation method (LLS). The three algorithms were applied to four microarray data sets and two synthetic low-rank data matrices. Certain percentages of the elements of these data sets were randomly deleted, and the three algorithms were used to recover them. In conclusion IFRAA appears to be the most reliable and accurate approach for recovering missing DNA microarray gene expression data, or any other noisy data matrices that are effectively low rank.

Original languageEnglish (US)
Title of host publication2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
PagesII1092-II1095
StatePublished - Dec 1 2006
Event2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 - Toulouse, France
Duration: May 14 2006May 19 2006

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2
ISSN (Print)1520-6149

Other

Other2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006
CountryFrance
CityToulouse
Period5/14/065/19/06

Keywords

  • Bayesian analysis
  • Gene expression matrix
  • K-nearest neighbor
  • Least squares
  • Missing values imputation
  • Principal component analysis
  • Singular value decomposition

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