Missing Expression Values Research Articles

Collaborative Structure-Preserved Missing Data Imputation for Single-Cell RNA-Seq Clustering.

Clustering of the single-cell RNA-seq (scRNA-seq) transcriptome profiles is able to identify cell types, which is beneficial to improve the understanding of disease progression. However, in practice, the single-cell expression data often contains a significant number of missing values as a result of technical variability. Missing data is a critical challenge in scRNA-seq clustering analysis since the unknown value does not reflect the underlying true expression level and makes it difficult to discovering cell types by applying clustering algorithms directly. Various approaches have been developed to overcome missing data issue in scRNA-seq clustering. Most of them recover missing expression values by borrowing observed data from similar cells or synthesizing data via generative adversarial networks. Such that the biologically meaningful cluster structure has not been sufficiently exploited. In this work, we introduce ColImpute, a collaborative structure-preserved missing data imputation approach for the scRNA-seq clustering. Specifically, a cluster structure-preserved imputation module and a subspace clustering module, which respectively perform missing data imputation and cell subtypes identification, are integrated into a unified optimization framework to train the two networks in a collaborative manner. Consequently, the clustering module effectively contributes cluster-structure information to guide the trainning process of the missing data imputation module. Simultaneously, the cluster structure-preserved imputation module reciprocally enhances the performance of the clustering module by generating more precise recovered samples. Promising experimental results show that the proposed method is effective for both the data imputation and the cell types identification.

ScIDPMs: Single-Cell RNA-Seq Imputation Using Diffusion Probabilistic Models.

Single-cell RNA sequencing (scRNA-seq) technology has revolutionized biological research by enabling the sequencing of mRNA in individual cells, thereby providing valuable insights into cellular gene expression and functions. However, scRNA-seq data often contain false zero values known as dropout events, which can obscure true gene expression levels and compromise downstream analysis accuracy. To address this issue, several computational approaches have been proposed for imputing missing gene expression values. Nevertheless, these methods struggle to capture dropout value distributions due to the sparsity of scRNA-seq data and complex gene expression patterns. In this study, we present a novel method called scIDPMs that utilizes conditional diffusion probabilistic models to impute scRNA-seq data. Firstly, scIDPMs identifies dropout sites based on gene expression characteristics and subsequently infers the missing values by considering available gene expression information. To effectively capture global gene expression features, scIDPMs employs a deep neural network with an attention mechanism to optimize the imputation process. We evaluated the performance of scIDPMs using simulated and real scRNA-seq datasets and compared it with ten other imputation methods. The results indicate that scIDPMs outperform other methods in restoring biologically meaningful gene expression values and improving downstream analysis.

Distribution-Agnostic Deep Learning Enables Accurate Single-Cell Data Recovery and Transcriptional Regulation Interpretation.

Single-cell RNA sequencing (scRNA-seq) is a robust method for studying gene expression at the single-cell level, but accurately quantifying genetic material is often hindered by limited mRNA capture, resulting in many missing expression values. Existing imputation methods rely on strict data assumptions, limiting their broader application, and lack reliable supervision, leading to biased signal recovery. To address these challenges, authors developed Bis, a distribution-agnostic deep learning model for accurately recovering missing sing-cell gene expression from multiple platforms. Bis is an optimal transport-based autoencoder model that can capture the intricate distribution of scRNA-seq data while addressing the characteristic sparsity by regularizing the cellular embedding space. Additionally, they propose a module using bulk RNA-seq data to guide reconstruction and ensure expression consistency. Experimental results show Bis outperforms other models across simulated and real datasets, showcasing superiority in various downstream analyses including batch effect removal, clustering, differential expression analysis, and trajectory inference. Moreover, Bis successfully restores gene expression levels in rare cell subsets in a tumor-matched peripheral blood dataset, revealing developmental characteristics of cytokine-induced natural killer cells within a head and neck squamous cell carcinoma microenvironment.

Enhancing Single-Cell RNA-seq Data Completeness with a Graph Learning Framework.

Single cell RNA sequencing (scRNA-seq) is a powerful tool to capture gene expression snapshots in individual cells. However, a low amount of RNA in the individual cells results in dropout events, which introduce huge zero counts in the single cell expression matrix. We have developed VAImpute, a variational graph autoencoder based imputation technique that learns the inherent distribution of a large network/graph constructed from the scRNA-seq data leveraging copula correlation ( Ccor) among cells/genes. The trained model is utilized to predict the dropouts events by computing the probability of all non-edges (cell-gene) in the network. We devise an algorithm to impute the missing expression values of the detected dropouts. The performance of the proposed model is assessed on both simulated and real scRNA-seq datasets, comparing it to established single-cell imputation methods. VAImpute yields significant improvements to detect dropouts, thereby achieving superior performance in cell clustering, detecting rare cells, and differential expression. All codes and datasets are given in the github link: https://github.com/sumantaray/VAImputeAvailability.

Missing Value Imputation With Low-Rank Matrix Completion in Single-Cell RNA-Seq Data by Considering Cell Heterogeneity.

Single-cell RNA-sequencing (scRNA-seq) technologies enable the measurements of gene expressions in individual cells, which is helpful for exploring cancer heterogeneity and precision medicine. However, various technical noises lead to false zero values (missing gene expression values) in scRNA-seq data, termed as dropout events. These zero values complicate the analysis of cell patterns, which affects the high-precision analysis of intra-tumor heterogeneity. Recovering missing gene expression values is still a major obstacle in the scRNA-seq data analysis. In this study, taking the cell heterogeneity into consideration, we develop a novel method, called single cell Gauss–Newton Gene expression Imputation (scGNGI), to impute the scRNA-seq expression matrices by using a low-rank matrix completion. The obtained experimental results on the simulated datasets and real scRNA-seq datasets show that scGNGI can more effectively impute the missing values for scRNA-seq gene expression and improve the down-stream analysis compared to other state-of-the-art methods. Moreover, we show that the proposed method can better preserve gene expression variability among cells. Overall, this study helps explore the complex biological system and precision medicine in scRNA-seq data.

Network inference with Granger causality ensembles on single-cell transcriptomics.

Cellular gene expression changes throughout a dynamic biological process, such as differentiation. Pseudotimes estimate cells' progress along a dynamic process based on their individual gene expression states. Ordering the expression data by pseudotime provides information about the underlying regulator-gene interactions. Because the pseudotime distribution is not uniform, many standard mathematical methods are inapplicable for analyzing the ordered gene expression states. Here we present single-cell inference of networks using Granger ensembles (SINGE), an algorithm for gene regulatory network inference from ordered single-cell gene expression data. SINGE uses kernel-based Granger causality regression to smooth irregular pseudotimes and missing expression values. It aggregates predictions from an ensemble of regression analyses to compile a ranked list of candidate interactions between transcriptional regulators and target genes. In two mouse embryonic stem cell differentiation datasets, SINGE outperforms other contemporary algorithms. However, a more detailed examination reveals caveats about poor performance for individual regulators and uninformative pseudotimes.

Estimation of Missing Values Using Hybrid Fuzzy Clustering Mean and Majority Vote for Microarray Data

Rapid development in microarray experiments leads toward generating a large amount of genes expressions. Handling such a massive amount of gene expressions is a challenge using traditional methods especially multiple missing expression values exist. This article proposed a novel hybrid fuzzy clustering mean and majority vote to yield better performance. The proposed method will be evaluated with other three imputation methods and trained with four publicly available microarray datasets. The experimental results show that the proposed method has promising results that able to replace the missing data as well as achieves high accuracy rates and low root mean square error values.

Evolutionary k-nearest neighbor imputation algorithm for gene expression data

Large data sets are produced by the gene expression process which is done by using the DNA microarray technology. These gene expression data are recognized as a common data source which contains missing expression values. In this paper, we present a genetic algorithm optimized k- Nearest neighbor algorithm (Evolutionary kNNImputation) for missing data imputation. Despite the common imputation methods this paper addresses the effectiveness of using supervised learning algorithms for missing data imputation. Missing data imputation approaches can be categorized into four main categories and among the four approaches, our focus is mainly on local approach where the proposed Evolutionary k- Nearest Neighbor Imputation Algorithm falls in. The Evolutionary k- Nearest Neighbor Imputation Algorithm is an extension of the common k- nearest Neighbor Imputation Algorithm which the genetic algorithm is used to optimize some parameters of k- Nearest Neighbor Algorithm. The selection of similarity matrix and the selection of the parameter value k can be identified as the optimization problem. We have compared the proposed Evolutionary k- Nearest Neighbor Imputation algorithm with k- Nearest Neighbor Imputation algorithm and mean imputation method. The three algorithms were tested using gene expression datasets. Certain percentages of values are randomly deleted in the datasets and recovered the missing values using the three algorithms. Results show that Evolutionary kNNImputation outperforms kNNImputation and mean imputation while showing the importance of using a supervised learning algorithm in missing data estimation. Even though mean imputation happened to show low mean error for a very few missing rates, supervised learning algorithms became effective when it comes to higher missing rates in datasets which is the most common situation among datasets.

Multiple imputation and analysis for high-dimensional incomplete proteomics data.

Multivariable analysis of proteomics data using standard statistical models is hindered by the presence of incomplete data. We faced this issue in a nested case–control study of 135 incident cases of myocardial infarction and 135 pair‐matched controls from the Framingham Heart Study Offspring cohort. Plasma protein markers (K = 861) were measured on the case–control pairs (N = 135), and the majority of proteins had missing expression values for a subset of samples. In the setting of many more variables than observations (K ≫ N), we explored and documented the feasibility of multiple imputation approaches along with subsequent analysis of the imputed data sets. Initially, we selected proteins with complete expression data (K = 261) and randomly masked some values as the basis of simulation to tune the imputation and analysis process. We randomly shuffled proteins into several bins, performed multiple imputation within each bin, and followed up with stepwise selection using conditional logistic regression within each bin. This process was repeated hundreds of times. We determined the optimal method of multiple imputation, number of proteins per bin, and number of random shuffles using several performance statistics. We then applied this method to 544 proteins with incomplete expression data (≤40% missing values), from which we identified a panel of seven proteins that were jointly associated with myocardial infarction. © 2015 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET.

High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell’s response to its intra- and extra-cellular microenvironment allows such data to be incorporated as signed and directed graphs or influence networks. These prior knowledge networks (PKNs) represent our current knowledge of the causality of cellular signal transduction. New signalling data is often examined and interpreted in conjunction with PKNs. However, different biological contexts, such as cell type or disease states, may have distinct variants of signalling pathways, resulting in the misinterpretation of new data. The identification of inconsistencies between measured data and signalling topologies, as well as the training of PKNs using context specific datasets (PKN contextualization), are necessary conditions to construct reliable, predictive models, which are current challenges in the systems biology of cell signalling. Here we present PRUNET, a user-friendly software tool designed to address the contextualization of a PKNs to specific experimental conditions. As the input, the algorithm takes a PKN and the expression profile of two given stable steady states or cellular phenotypes. The PKN is iteratively pruned using an evolutionary algorithm to perform an optimization process. This optimization rests in a match between predicted attractors in a discrete logic model (Boolean) and a Booleanized representation of the phenotypes, within a population of alternative subnetworks that evolves iteratively. We validated the algorithm applying PRUNET to four biological examples and using the resulting contextualized networks to predict missing expression values and to simulate well-characterized perturbations. PRUNET constitutes a tool for the automatic curation of a PKN to make it suitable for describing biological processes under particular experimental conditions. The general applicability of the implemented algorithm makes PRUNET suitable for a variety of biological processes, for instance cellular reprogramming or transitions between healthy and disease states.

Prediction of regulatory gene pairs using dynamic time warping and gene ontology.

Selecting informative genes is the most important task for data analysis on microarray gene expression data. In this work, we aim at identifying regulatory gene pairs from microarray gene expression data. However, microarray data often contain multiple missing expression values. Missing value imputation is thus needed before further processing for regulatory gene pairs becomes possible. We develop a novel approach to first impute missing values in microarray time series data by combining k-Nearest Neighbour (KNN), Dynamic Time Warping (DTW) and Gene Ontology (GO). After missing values are imputed, we then perform gene regulation prediction based on our proposed DTW-GO distance measurement of gene pairs. Experimental results show that our approach is more accurate when compared with existing missing value imputation methods on real microarray data sets. Furthermore, our approach can also discover more regulatory gene pairs that are known in the literature than other methods.

Imputing gene expression from selectively reduced probe sets

Measuring complete gene expression profiles for a large number of experiments is costly. We propose an approach in which a small subset of probes is selected based on a preliminary set of full expression profiles. In subsequent experiments, only the subset is measured, and the missing values are imputed. We develop several algorithms to simultaneously select probes and impute missing values, and demonstrate that these probe selection for imputation (PSI) algorithms can successfully reconstruct missing gene expression values in a wide variety of applications, as evaluated using multiple metrics of biological importance. We analyze the performance of PSI methods under varying conditions, provide guidelines for choosing the optimal method based on the experimental setting, and indicate how to estimate imputation accuracy. Finally, we apply our approach to a large-scale study of immune system variation.

Predicting missing expression values in gene regulatory networks using a discrete logic modeling optimization guided by network stable states

The development of new high-throughput technologies enables us to measure genome-wide transcription levels, protein abundance, metabolite concentration, etc. Nevertheless, these experimental data are often noisy and incomplete, which hinders data analysis, modeling and prediction. Here, we propose a method to predict expression values of genes involved in stable cellular phenotypes from the expression values of the remaining genes in a literature-based gene regulatory network. The consistency between predicted and known stable states from experimental data is used to guide an iterative network pruning that contextualizes the network to the biological conditions under which the expression data were obtained. Using the contextualized network and the property of network stability we predict gene expression values missing from experimental data. The prediction method assumes a Boolean model to compute steady states of networks and an evolutionary algorithm to iteratively prune the networks. The evolutionary algorithm samples the probability distribution of positive feedback loops or positive circuits and individual interactions within the subpopulation of the best-pruned networks at each iteration. The resulting expression inference is based not only on previous knowledge about local connectivity but also on a global network property (stability), providing robustness in the predictions.

Identifying Functional Modules of Diffuse Large B-cell Lymphoma Gene Co-expression networks by Hierarchical Clustering Method Based on Random Matrix Theory

In this work, we propose an original method, the Random matrix theory (RMT)-based hierarchical clustering method, to identify functional gene networks of diffuse large B-cell Lymphoma (DLBCL) gene co-expression networks. Comparing topological approach, the RMT-based hierarchical clustering method is effective in representing not only the strong correlations between genes inside the modules (the modularity and independency of networks), but also the weak correlations between different modules (the hierarchy of networks). We show that missing expression values among microarray dataset should not be neglected, and different imputation methods result in different performances. We suggest LLS to estimate missing values for better performance in accuracy and stability. Based on the RMT, the random noises are separated from DLBCL gene expression data. We use normalized root mean squared error (NRMSE) ratio method to identify a transition zone of NNSDs, and for DLBCL networks it is [0.71, 0.84].

KNN-DTW Based Missing Value Imputation for Microarray Time Series Data

Microarray technology provides an opportunity for scientists to analyze thousands of gene expression profiles simultaneously. However, microarray gene expression data often contain multiple missing expression values due to many reasons. Effective methods for missing value imputation in gene expression data are needed since many algorithms for gene analysis require a complete matrix of gene array values. Several algorithms are proposed to handle this problem, but they have various limitations. In this paper, we develop a novel method to impute missing values in microarray time-series data combining k-nearest neighbor (KNN) and dynamic time warping (DTW). We also analyze and implement several variants of DTW to further improve the efficiency and accuracy of our method. Experimental results show that our method is more accurate compared with existing missing value imputation methods on real microarray time series datasets.

Approximation of Missing Values in DNA Microarray Gene Expression Data

In the past few years, there has been a detonation of data in the field of biotechnology. Gene expression microarray experiments produce datasets with numerous missing expression values due to various reasons, e.g. insufficient resolution, image corruption, dust or scratches on the slides, or experimental error during the laboratory process.. To improve these missing values, many algorithms for gene expression analysis oblige a complete matrix of gene array values as input, such as K nearest neighbor impute method, Bayesian principal components analysis impute method, etc. Accurate estimation of missing values is an important requirement for efficient data analysis. Main problem of existing methods for microarray data is that there is no external information but the estimation is based exclusively on the expression data. We conjectured that utilizing a priori information on functional similarities available from public databases facilitates the missing value estimation. Robust missing value estimation methods are required since many algorithms for gene expression analysis entail a complete matrix of gene array values. Either genes with missing values can be removed, or the missing values can be replaced using prediction. Current methods for estimating the missing values include sample mean and K-nearest neighbors (KNN). Whether the accuracy of estimation methods depends on the actual gene expression has not been thoroughly investigated. Under this setting, we examine how the accuracy depends on the actual expression level and propose new method that provides improvements in accuracy relative to the current methods in certain ranges of gene expression.

Microarray missing values imputation methods: Critical analysis review

Gene expression data often contain missing expression values. For the purpose of conducting an effective clustering analysis and since many algorithms for gene expression data analysis require a complete matrix of gene array values, choosing the most effective missing value estimation method is necessary. In this paper, the most commonly used imputation methods from literature are critically reviewed and analyzed to explain the proper use, weakness and point the observations on each published method. From the conducted analysis, we conclude that the Local Least Square (LLS) and Support Vector Regression (SVR) algorithms have achieved the best performances. SVR can be considered as a complement algorithm for LLS especially when applied to noisy data. However, both algorithms suffer from some deficiencies presented in choosing the value of Number of Selected Genes (K) and the appropriate kernel function. To overcome these drawbacks, the need for new method that automatically chooses the parameters of the function and it also has an appropriate computational complexity is imperative.

Accounting for one-channel depletion improves missing value imputation in 2-dye microarray data

BackgroundFor 2-dye microarray platforms, some missing values may arise from an un-measurably low RNA expression in one channel only. Information of such "one-channel depletion" is so far not included in algorithms for imputation of missing values.ResultsCalculating the mean deviation between imputed values and duplicate controls in five datasets, we show that KNN-based imputation gives a systematic bias of the imputed expression values of one-channel depleted spots. Evaluating the correction of this bias by cross-validation showed that the mean square deviation between imputed values and duplicates were reduced up to 51%, depending on dataset.ConclusionBy including more information in the imputation step, we more accurately estimate missing expression values.

Restoration of Liver Mass after Injury Requires Proliferative and Not Embryonic Transcriptional Patterns

Normal adult liver is uniquely capable of renewal and repair after injury. Whether this response represents simple hyperplasia of various liver elements or requires recapitulation of the genetic program of the developing liver is not known. To study these possibilities, we examined transcriptional programs of adult liver after partial hepatectomy and contrasted these with developing embryonic liver. Principal component analysis demonstrated that the time series of gene expression during liver regeneration does not segregate according to developmental transcription patterns. Gene ontology analysis revealed that liver restoration after hepatectomy and liver development differ dramatically with regard to transcription factors and chromatin structure modification. In contrast, the tissues are similar with regard to proliferation-associated genes. Consistent with these findings, real-time polymerase chain reaction showed transcription factors known to be important in liver development are not induced during liver regeneration. These three lines of evidence suggest that at a transcriptional level restoration of liver mass after injury is best described as hepatocyte hyperplasia and not true regeneration. We speculate this novel pattern of gene expression may underlie the unique capacity of the liver to repair itself after injury.

Improving missing value estimation in microarray data with gene ontology

Gene expression microarray experiments produce datasets with frequent missing expression values. Accurate estimation of missing values is an important prerequisite for efficient data analysis as many statistical and machine learning techniques either require a complete dataset or their results are significantly dependent on the quality of such estimates. A limitation of the existing estimation methods for microarray data is that they use no external information but the estimation is based solely on the expression data. We hypothesized that utilizing a priori information on functional similarities available from public databases facilitates the missing value estimation. We investigated whether semantic similarity originating from gene ontology (GO) annotations could improve the selection of relevant genes for missing value estimation. The relative contribution of each information source was automatically estimated from the data using an adaptive weight selection procedure. Our experimental results in yeast cDNA microarray datasets indicated that by considering GO information in the k-nearest neighbor algorithm we can enhance its performance considerably, especially when the number of experimental conditions is small and the percentage of missing values is high. The increase of performance was less evident with a more sophisticated estimation method. We conclude that even a small proportion of annotated genes can provide improvements in data quality significant for the eventual interpretation of the microarray experiments. Java and Matlab codes are available on request from the authors. Available online at http://users.utu.fi/jotatu/GOImpute.html.