An Integrative DTW-based imputation method for gene expression time series data

Abstract

Gene expression microarrays are the most commonly available source of high-throughput biological data. They are widely employed for studying many different aspects of gene regulation and function, ranging from understanding the global cell-cycle control of microorganisms to cancer in humans. Gene expression microarray experiments often generate data sets with multiple missing values. Many algorithms for gene expression data analysis require a complete data matrix and therefore, the accurate estimation of missing entries is crucial for their optimal usage. The latter has driven the development of various microarray imputation methods. However, most of these approaches are not particularly suitable for time series expression profiles. Moreover, their performance is not satisfactory for datasets with high rates of missing data or small numbers of samples. Another drawback of all these methods is that their estimation is based solely on a single expression matrix and no other additional data sources to impute the missing entries are used. Motivated by these, we propose herein an imputation algorithm that is particularly suited for the estimation of missing values in gene expression time series data using information that is contained in multiple related data sets. The proposed algorithm initially identifies an appropriate set of estimation matrices by using the Dynamic Time Warping (DTW) distance in order to measure similarities between gene expression matrices. Next it employs the same distance measure to evaluate the similarity between gene expression profiles and further applies a hybrid aggregation algorithm to combine the inter-gene similarities across the selected matrices in order to identify estimation genes. Then the expression profiles of those estimation genes are used to obtain the final imputation. The estimation accuracy of the proposed algorithm, called Integrative DTWbased Imputation (IDTWimpute), is benchmarked against that of two other imputation methods (KNNimpute and DTWimpute) in terms of root mean squared difference. In addition, the impact of the three methods on the quality of gene clustering is evaluated by using k-means and k-medoids clustering algorithms and two different cluster validation measures.

A New Fuzzy Logic-Based Similarity Measure Applied to Large Gap Imputation for Uncorrelated Multivariate Time Series

“…The results showed that the combination of MICE and RF was more efficient than original methods for multivariate imputation. K-Nearest Neighbors ( -NN)-based imputation is also a popular method for completing missing values such as [11,26,27,[30][31][32]. This approach identifies most similar patterns in the space of available features to impute missing data.…”

“…Step a and Step b for data (32) return position of -the most similar window to (33) end for (34) Replace the missing values at the position by average vector of the window after and the one previous (35) end for (36) end for (37) return -imputed time series This makes it possible to find out windows that have the most similar dynamics and shape to the queries. …”

Novel Features for Binary Time Series Based on Branch Length Similarity Entropy

“…The abovementioned approaches are widely used in various fields, such as multimedia, healthcare, and finance [ 29 ]. These approaches have been applied in major research topics including earthquake prediction [ 30 ], terrestrial ecosystem dynamics [ 31 ], stock-price data, exchange-rate analysis [ 32 ], and bioinformatics [ 33 ]. However, they have not been specialized for binary time-series.…”

Which DTW method applied to marine univariate time series imputation

“…DTWcost is used as distance metric instead of pointwise distance measurements. Kostadinova et al [27] proposed an Integrative DTW-Based Imputation algorithm that is particularly suited for the estimation of missing values in gene expression time series data using multiple related information in datasets. This algorithm identifies an appropriate set of estimation matrices by using DTW-cost distance in order to measure similarities between gene expression matrices.…”