Values In Microarray Data Research Articles

Regularized Sparse Modelling for Microarray Missing Value Estimation

The existence of missing values in microarray data inevitably hinders downstream biological analyses that expect complete data as input, therefore how to effectively explore the underlying structure of data to accurately estimate missing entries remains crucial and meaningful. In this study, we formalize the problem under a regularized sparse framework and accordingly propose local learning-based imputation models to capture the relationships that are hidden in gene expression profiles towards better imputation. Specifically, in view of the simultaneous variable selection and grouping effect of the elastic net penalty, we present an elastic net regularized local least squares-based imputation method to estimate the missing entries of a target gene with its neighbors. Besides, we investigate different similarity filtering metrics to select neighbor genes and develop another four imputation methods under the framework. Furthermore, the proposed methods process the target genes in ascending order of their associated missing rates. Finally, extensive comparative experiments against other eight commonly-used methods are conducted on multiple microarray datasets having varying missing rates. Results indicate the power of sparse regularization techniques and the superiority of elastic net over its competitors in terms of statistical analysis metrics.

Imputation of Gene Expression Data in Blood Cancer and Its Significance in Inferring Biological Pathways.

Purpose: Gene expression data generated from microarray technology is often analyzed for disease diagnostics and treatment. However, this data suffers with missing values that may lead to inaccurate findings. Since data capture is expensive, time consuming, and is required to be collected from subjects, it is worthwhile to recover missing values instead of re-collecting the data. In this paper, a novel but simple method, namely, DSNN (Doubly Sparse DCT domain with Nuclear Norm minimization) has been proposed for imputing missing values in microarray data. Extensive experiments including pathway enrichment have been carried out on four blood cancer dataset to validate the method as well as to establish the significance of imputation.Methods: A new method, namely, DSNN, was proposed for missing value imputation on gene expression data. Method was validated on four dataset, CLL, AML, MM (Spanish data), and MM (Indian data). All the dataset were downloaded from GEO repository. Missing values were introduced in the original data from 10 to 90% in steps of 10% because method validation requires ground truth. Quantitative results on normalized mean square error (NMSE) between the ground truth and imputed data were computed. To further validate and establish the significance of the proposed imputation method, two experiments were carried out on the data imputed with the proposed method, data imputed with the state-of-art methods, and data with missing values. In the first experiment, classification of normal vs. cancer subjects was carried out. In the second experiment, biological significance of imputation was ascertained by identifying top candidate tumor drivers using the existing state-of-the-art SPARROW algorithm, followed by gene list enrichment analysis on top candidate drivers.Results: Quantitative NMSE results of the DSNN method were compared with three state-of-the-art imputation methods. DSNN method was observed to perform better compared to these other methods both at high as well as low observable data. Experiment-1 demonstrated superior results on classification with imputation compared to that performed on missing data matrix as well as compared to classification on imputed data with existing methods. In experiment-2, cancer affected pathways were discovered with higher significance in the data imputed with the proposed method compared to those discovered with the missing data matrix.Conclusion: Missing value problem in microarray data is a serious problem and can adversely influence downstream analysis. A novel method, namely, DSNN is proposed for missing value imputation. The method is validated quantitatively on the application of classification and biologically by performing pathway enrichment analysis.

Mining Gene Expression Profile with Missing Values: An Integration of Kernel PCA and Robust Singular Values Decomposition

Background: Gene expression profiling and transcriptomics provide valuable information about the role of genes that are differentially expressed between two or more samples. It is always important and challenging to analyse High-throughput DNA microarray data with a number of missing values under various experimental conditions. </P><P> Objectives: Graphical data visualizations of the expression of all genes in a particular cell provide holistic views of gene expression patterns, which improve our understanding of cellular systems under normal and pathological conditions. However, current visualization methods are sensitive to missing values, which are frequently observed in microarray-based gene expression profiling, potentially affecting the subsequent statistical analyses. Methods: We addressed in this study the problem of missing values with respect to different imputation methods using gene expression biplot (GE biplot), one of the most popular gene visualization techniques. The effects of missing values for mining differentially expressed genes in gene expression data were evaluated using four well-known imputation methods: Robust Singular Value Decomposition (Robust SVD), Column Average (CA), Column Median (CM), and K-nearest Neighbors (KNN). Frobenius norm and absolute distances were used to measure the accuracy of the methods. Results: Three numerical experiments were performed using simulated data (i) and publicly available colon cancer (ii) and leukemia data (iii) to analyze the performance of each method. The results showed that CM and KNN performed better than Robust SVD and CA for identifying the index gene profile in the biplot visualization in both the simulation study and the colon cancer and leukemia microarray datasets. Conclusion: The impact of missing values on the GE biplot was smaller when the data matrix was imputed by KNN than by CM. This study concluded that KNN performed satisfactorily in generating a GE biplot in the presence of missing values in microarray data.

A global learning with local preservation method for microarray data imputation

Microarray data suffer from missing values for various reasons, including insufficient resolution, image noise, and experimental errors. Because missing values can hinder downstream analysis steps that require complete data as input, it is crucial to be able to estimate the missing values. In this study, we propose a Global Learning with Local Preservation method (GL2P) for imputation of missing values in microarray data. GL2P consists of two components: a local similarity measurement module and a global weighted imputation module. The former uses a local structure preservation scheme to exploit as much information as possible from the observable data, and the latter is responsible for estimating the missing values of a target gene by considering all of its neighbors rather than a subset of them. Furthermore, GL2P imputes the missing values in ascending order according to the rate of missing data for each target gene to fully utilize previously estimated values. To validate the proposed method, we conducted extensive experiments on six benchmarked microarray datasets. We compared GL2P with eight state-of-the-art imputation methods in terms of four performance metrics. The experimental results indicate that GL2P outperforms its competitors in terms of imputation accuracy and better preserves the structure of differentially expressed genes. In addition, GL2P is less sensitive to the number of neighbors than other local learning-based imputation methods.

Embracing noise to improve cross-batch prediction accuracy

One important application of microarray in clinical settings is for constructing a diagnosis or prognosis model. Batch effects are a well-known obstacle in this type of applications. Recently, a prominent study was published on how batch effects removal techniques could potentially improve microarray prediction performance. However, the results were not very encouraging, as prediction performance did not always improve. In fact, in up to 20% of the cases, prediction accuracy was reduced. Furthermore, it was stated in the paper that the techniques studied require sufficiently large sample sizes in both batches (train and test) to be effective, which is not a realistic situation especially in clinical settings. In this paper, we propose a different approach, which is able to overcome limitations faced by conventional methods. Our approach uses ranking value of microarray data and a bagging ensemble classifier with sequential hypothesis testing to dynamically determine the number of classifiers required in the ensemble. Using similar datasets to those in the original study, we showed that in only one case (<2%) is our performance reduced (by more than -0.05 AUC) and, in >60% of cases, it is improved (by more than 0.05 AUC). In addition, our approach works even on much smaller training data sets and is independent of the sample size of the test data, making it feasible to be applied on clinical studies.

Missing value imputation for epistatic MAPs

BackgroundEpistatic miniarray profiling (E-MAPs) is a high-throughput approach capable of quantifying aggravating or alleviating genetic interactions between gene pairs. The datasets resulting from E-MAP experiments typically take the form of a symmetric pairwise matrix of interaction scores. These datasets have a significant number of missing values - up to 35% - that can reduce the effectiveness of some data analysis techniques and prevent the use of others. An effective method for imputing interactions would therefore increase the types of possible analysis, as well as increase the potential to identify novel functional interactions between gene pairs. Several methods have been developed to handle missing values in microarray data, but it is unclear how applicable these methods are to E-MAP data because of their pairwise nature and the significantly larger number of missing values. Here we evaluate four alternative imputation strategies, three local (Nearest neighbor-based) and one global (PCA-based), that have been modified to work with symmetric pairwise data.ResultsWe identify different categories for the missing data based on their underlying cause, and show that values from the largest category can be imputed effectively. We compare local and global imputation approaches across a variety of distinct E-MAP datasets, showing that both are competitive and preferable to filling in with zeros. In addition we show that these methods are effective in an E-MAP from a different species, suggesting that pairwise imputation techniques will be increasingly useful as analogous epistasis mapping techniques are developed in different species. We show that strongly alleviating interactions are significantly more difficult to predict than strongly aggravating interactions. Finally we show that imputed interactions, generated using nearest neighbor methods, are enriched for annotations in the same manner as measured interactions. Therefore our method potentially expands the number of mapped epistatic interactions. In addition we make implementations of our algorithms available for use by other researchers.ConclusionsWe address the problem of missing value imputation for E-MAPs, and suggest the use of symmetric nearest neighbor based approaches as they offer consistently accurate imputations across multiple datasets in a tractable manner.

Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency

BackgroundProstate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear. We present a computational method for inferring genetic regulatory networks from micorarray data automatically with transcription factor analysis and conditional independence testing to explore the potential significant gene regulatory networks that are correlated with cancer, tumor grade and stage in the prostate cancer.ResultsTo deal with missing values in microarray data, we used a K-nearest-neighbors (KNN) algorithm to determine the precise expression values. We applied web services technology to wrap the bioinformatics toolkits and databases to automatically extract the promoter regions of DNA sequences and predicted the transcription factors that regulate the gene expressions. We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD) as a target dataset to evaluate our method. The predicted results showed that the possible biomarker genes related to cancer and denoted the androgen functions and processes may be in the development of the prostate cancer and promote the cell death in cell cycle. Our predicted results showed that sub-networks of genes SREBF1, STAT6 and PBX1 are strongly related to a high extent while ETS transcription factors ELK1, JUN and EGR2 are related to a low extent. Gene SLC22A3 may explain clinically the differentiation associated with the high grade cancer compared with low grade cancer. Enhancer of Zeste Homolg 2 (EZH2) regulated by RUNX1 and STAT3 is correlated to the pathological stage.ConclusionsWe provide a computational framework to reconstruct the genetic regulatory network from the microarray data using biological knowledge and constraint-based inferences. Our method is helpful in verifying possible interaction relations in gene regulatory networks and filtering out incorrect relations inferred by imperfect methods. We predicted not only individual gene related to cancer but also discovered significant gene regulation networks. Our method is also validated in several enriched published papers and databases and the significant gene regulatory networks perform critical biological functions and processes including cell adhesion molecules, androgen and estrogen metabolism, smooth muscle contraction, and GO-annotated processes. Those significant gene regulations and the critical concept of tumor progression are useful to understand cancer biology and disease treatment.

Applications of Signal Processing Techniques to Bioinformatics, Genomics, and Proteomics

The recent development of high-throughput molecular genetics technologies has brought a major impact to bioinformatics and systems biology. These technologies have made possible the measurement of the expression profiles of genes and proteins in a highly parallel and integrated fashion. The examination of the huge amounts of genomic and proteomic data holds the promise for understanding the complex interactions between genes and proteins, the functional processes of a cell, and the impact of various factors on a cell, and ultimately, for enabling the design of new technologies for intelligent management of diseases. This special issue focuses on modeling and processing of data arising in bioinformatics, genomics, and proteomics using signal processing methods. The importance of signal processing techniques is due to their important role in extracting, processing, and interpreting the information contained in genomic and proteomic data. It is our hope that signal processing methods will lead to new advances and insights in uncovering the structure, functioning and evolution of biological systems. The special issue consists of nine papers that span a wide range of problems and applications in bioinformatics, genomics, and proteomics such as design of compressive sensing microarrays, analysis of missing values in microarray data, and effect of imputation techniques on post genomic inference methods, RNA sequence alignment, detection of periodicity in genomic sequences and gene expression profiles, clustering and classification of gene and protein expression data, and intervention in probabilistic Boolean networks. Next, we will briefly introduce the papers reported in this special issue. W. Dai et al. analyze how to design a microarray that it is fit for compressive sensing and that captures also the biochemistry of probe-target DNA hybridization. Algorithms and design results are reported for determining probe sequences that satisfy the binding requirements and for evaluating the target concentrations. M. S. B. Sehgal et al. address the general problem of improving post genomic knowledge discovery procedures such as the selection of the most significant genes and inference of gene regulatory networks using missing microarray data imputation techniques. It is shown that instead of neglecting missing data, recycling microarray data via robust imputation techniques can yield substantial performance improvements in the subsequent post genomic discovery procedures. B.-J. Yoon developed a novel efficient and robust approach for fast and accurate structural alignment of RNAs, including pseudoknots. The proposed method turns out to accelerate the dynamic programming algorithm for family-specific models such as profile-csHMMs and CMs, and to be robust to small parameter changes that are present in the model used to predict the constraint. The paper by J. Epps explains in detail the origins of ambiguity in period estimation for symbolic sequences, and proposes a novel hybrid autocorrelation-IPDFT technique for periodicity characterization of sequences. W. Zhao et al. developed a novel algorithm for identification of genes involved in cyclic processes by combining gene expression analysis and prior knowledge. The proposed cyclic-genes detection algorithm is validated on data sets corresponding to Saccharomyces cerevisiae and Drosophila melanogaster, and shown to represent a valuable technique for unveiling pathways related to cyclic processes. T. J. Hestilow and Y. Huang propose a novel method for gene clustering using the shape information of gene expression profiles. The shape information which is represented in terms of normalized and time-scaled forward first-order differences is then exploited by a variational Bayes clustering approach and a non-Bayesian (Silhouette) cluster statistic, and shown to yield promising results in clustering time-series microarray data. The paper by W. Zhao et al. proposes a new clustering approach to combine the traditional clustering methods with power spectral analysis of time series gene expression measurements. Simulation results confirm that the proposed clustering approach provides superior performance relative to hierarchical, K-means, and self-organizing maps, and yields additional information about temporal regulated genetic processes, for example, cell-cycle. T. T. Vu and U. M. Braga-Neto address the important problem of assessing the effectiveness of bagging in the classification of small-sample genomic and proteomic data sets. Representative experimental results are presented and discussed. Finally, the paper by B. Faryabi et al. studies the effects on intervention performance in the context of probabilistic Boolean networks due to a reduction in the values of the model parameters.

Sequential local least squares imputation estimating missing value of microarray data

Missing values in microarray data can significantly affect subsequent analysis, thus it is important to estimate these missing values accurately. In this paper, a sequential local least squares imputation (SLLSimpute) method is proposed to solve this problem. It estimates missing values sequentially from the gene containing the fewest missing values and partially utilizes these estimated values. In addition, an automatic parameter selection algorithm, which can generate an appropriate number of neighboring genes for each target gene, is presented for parameter estimation. Experimental results confirmed that SLLSimpute method exhibited better estimation ability compared with other currently used imputation methods.

Missing value imputation for microarray gene expression data using histone acetylation information.

BackgroundIt is an important pre-processing step to accurately estimate missing values in microarray data, because complete datasets are required in numerous expression profile analysis in bioinformatics. Although several methods have been suggested, their performances are not satisfactory for datasets with high missing percentages.ResultsThe paper explores the feasibility of doing missing value imputation with the help of gene regulatory mechanism. An imputation framework called histone acetylation information aided imputation method (HAIimpute method) is presented. It incorporates the histone acetylation information into the conventional KNN(k-nearest neighbor) and LLS(local least square) imputation algorithms for final prediction of the missing values. The experimental results indicated that the use of acetylation information can provide significant improvements in microarray imputation accuracy. The HAIimpute methods consistently improve the widely used methods such as KNN and LLS in terms of normalized root mean squared error (NRMSE). Meanwhile, the genes imputed by HAIimpute methods are more correlated with the original complete genes in terms of Pearson correlation coefficients. Furthermore, the proposed methods also outperform GOimpute, which is one of the existing related methods that use the functional similarity as the external information.ConclusionWe demonstrated that the using of histone acetylation information could greatly improve the performance of the imputation especially at high missing percentages. This idea can be generalized to various imputation methods to facilitate the performance. Moreover, with more knowledge accumulated on gene regulatory mechanism in addition to histone acetylation, the performance of our approach can be further improved and verified.

Dealing with gene expression missing data

Compared evaluation of different methods is presented for estimating missing values in microarray data: weighted K-nearest neighbours imputation (KNNimpute), regression-based methods such as local least squares imputation (LLSimpute) and partial least squares imputation (PLSimpute) and Bayesian principal component analysis (BPCA). The influence in prediction accuracy of some factors, such as methods' parameters, type of data relationships used in the estimation process (i.e. row-wise, column-wise or both), missing rate and pattern and type of experiment [time series (TS), non-time series (NTS) or mixed (MIX) experiments] is elucidated. Improvements based on the iterative use of data (iterative LLS and PLS imputation--ILLSimpute and IPLSimpute), the need to perform initial imputations (modified PLS and Helland PLS imputation--MPLSimpute and HPLSimpute) and the type of relationships employed (KNNarray, LLSarray, HPLSarray and alternating PLS--APLSimpute) are proposed. Overall, it is shown that data set properties (type of experiment, missing rate and pattern) affect the data similarity structure, therefore influencing the methods' performance. LLSimpute and ILLSimpute are preferable in the presence of data with a stronger similarity structure (TS and MIX experiments), whereas PLS-based methods (MPLSimpute, IPLSimpute and APLSimpute) are preferable when estimating NTS missing data.