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Corrigendum for Kim et al., Bioinformatics 21 (2) 187-198.
Bioinformatics 2006 22(11):1410-1411; doi:10.1093/bioinformatics/btk053
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

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

Hyunsoo Kim , Gene H. Golub and Haesun Park

In our article, only a set of random positions of missing values was used for each dataset. However, imputation methods may perform differently on the same dataset with different sets of random positions of missing values. In addition, the performance of BPCA of each figure was not correct due to a mistake in implementation. After correcting an error in the implementation and using ten different sets of random positions of missing values, we regenerated all figures. Thus, the results presented here are the fair comparisons between LLSimpute and BPCA.

193p. The NRMSEs of LLSk/L2 and BPCA were 0.6068 and 0.6064, respectively. In the SP.CYCLE dataset that has significant cluster structures, LLSimpute showed competitive performance with BPCA. The NRMSEs of LLSk/L2 and BPCA on the GA.ENV dataset were 0.5145 and 0.5232, respectively.

194p. LLSk/L2 showed the best performance when the number of samples was small since it uses local similarity structures and an optimization process via least squares. For various percentages of missing entries, LLSk/L2 showed competitive performance with BPCA.

195p. Figure 5 shows the NRMSEs with respect to noise levels. The performance sensitivity to noise of LLSk/L2 and LLSk/PC were similar to that of BPCA.

196p. All methods showed robustness against the noise when the SD was less than 0.1.


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Bioinformatics (2005) 21(2), 187–198


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This Article
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