  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.
