Conclusions
KNNimpute is a fast, robust, and accurate method of estimating missing values for microarray data. Both KNNimpute and SVDimpute methods far surpass the currently accepted solutions (filling missing values with zeros or row average) by taking advantage of the structure of microarray data to estimate missing expression values.
We recommend KNNimpute over SVDimpute method for several reasons. First, the KNNimpute method is more robust than SVD to the type of data for which estimation is performed, performing better on non-time series or noisy data. Second, while both KNN and SVD methods are robust to increasing the fraction of missing data, KNN-based imputation shows less deterioration in performance with increasing percent of missing entries. And third, KNNimpute is less sensitive to the exact parameters used (number of nearest neighbors), whereas the SVD-based method shows sharp deterioration in performance when a non-optimal fraction of missing values is used. In addition, KNNimpute has the advantage of providing accurate estimation for missing values in genes that belong to small tight expression clusters. Such genes may not be similar to any of the eigengenes used for regression in SVDimpute, and their missing values could thus be estimated poorly by SVD-based estimation.
KNNimpute is a robust and sensitive approach to estimating missing data for gene expression microarrays. However, scientists should exercise caution when drawing critical biological conclusions from partially imputed data. The goal of this and other estimation methods is to provide an accurate way of estimating missing data points in order to minimize the bias introduced in the performance of microarray analysis methods. Estimated data should be flagged where possible, and their impact on the discovery of biological results should be assessed in order to avoid drawing unwarranted conclusions.
Parts of the work presented in this chapter were originally published in Bioinformatics (Troyanskaya et al., 2001).
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Troyanskaya, O.G., Botstein, D., Altman, R.B. (2003). Missing Value Estimation. In: Berrar, D.P., Dubitzky, W., Granzow, M. (eds) A Practical Approach to Microarray Data Analysis. Springer, Boston, MA. https://doi.org/10.1007/0-306-47815-3_3
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