Missing value imputation for gene expression data: computational techniques to recover missing data from available information

Abstract

Microarray gene expression data generally suffers from missing value problem due to a variety of experimental reasons. Since the missing data points can adversely affect downstream analysis, many algorithms have been proposed to impute missing values. In this survey, we provide a comprehensive review of existing missing value imputation algorithms, focusing on their underlying algorithmic techniques and how they utilize local or global information from within the data, or their use of domain knowledge during imputation. In addition, we describe how the imputation results can be validated and the different ways to assess the performance of different imputation algorithms, as well as a discussion on some possible future research directions. It is hoped that this review will give the readers a good understanding of the current development in this field and inspire them to come up with the next generation of imputation algorithms.

Improved methods for the imputation of missing data by nearest neighbor methods

“…Liew et al (2011) and Moorthy et al (2014) reviewed the available methods and algorithms for the imputation of missing values with a focus on gene expression data. Johansson and Hakkinen (2006) proposed WeNNI, which utilizes continuous weights in the nearest neighbors imputation procedure.…”

Iterative bicluster-based least square framework for estimation of missing values in microarray gene expression data

“…There are mainly two ways to explore the coherence information, namely the global and the local approaches [8]. The global approaches assume a global covariance structure in all genes [9,10] while the local approaches exploit correlations among certain genes only [11][12][13][14].…”

The Possibility of Applying the EM-PCA Procedure to Lake Water

“…EM-PCA begins with the initialization of the missing data by quoting the average values in the corresponding rows and columns, and then their iterations in such a way to substitute missing data with values predicted (estimated) by the PCA [40]. The algorithm for missing data [41][42][43][44][45][46][47] is repeated until the convergence criteria. The mean values obtained in the convergence criterion replace missing data in the corresponding rows and columns.…”