Non-clustering Method Research Articles

A novel graph-based k-partitioning approach improves the detection of gene-gene correlations by single-cell RNA sequencing

BackgroundGene expression is regulated by transcription factors, cofactors, and epigenetic mechanisms. Coexpressed genes indicate similar functional categories and gene networks. Detecting gene-gene coexpression is important for understanding the underlying mechanisms of cellular function and human diseases. A common practice of identifying coexpressed genes is to test the correlation of expression in a set of genes. In single-cell RNA-seq data, an important challenge is the abundance of zero values, so-called “dropout”, which results in biased estimation of gene-gene correlations for downstream analyses. In recent years, efforts have been made to recover coexpressed genes in scRNA-seq data. Here, our goal is to detect coexpressed gene pairs to reduce the “dropout” effect in scRNA-seq data using a novel graph-based k-partitioning method by merging transcriptomically similar cells.ResultsWe observed that the number of zero values was reduced among the merged transcriptomically similar cell clusters. Motivated by this observation, we leveraged a graph-based algorithm and develop an R package, scCorr, to recover the missing gene-gene correlation in scRNA-seq data that enables the reliable acquisition of cluster-based gene-gene correlations in three independent scRNA-seq datasets. The graphically partitioned cell clusters did not change the local cell community. For example, in scRNA-seq data from peripheral blood mononuclear cells (PBMCs), the gene-gene correlation estimated by scCorr outperformed the correlation estimated by the nonclustering method. Among 85 correlated gene pairs in a set of 100 clusters, scCorr detected 71 gene pairs, while the nonclustering method detected only 4 pairs of a dataset from PBMCs. The performance of scCorr was comparable to those of three previously published methods. As an example of downstream analysis using scCorr, we show that scCorr accurately identified a known cell type (i.e., CD4+ T cells) in PBMCs with a receiver operating characteristic area under the curve of 0.96.ConclusionsOur results demonstrate that scCorr is a robust and reliable graph-based method for identifying correlated gene pairs, which is fundamental to network construction, gene-gene interaction, and cellular omic analyses. scCorr can be quickly and easily implemented to minimize zero values in scRNA-seq analysis and is freely available at https://github.com/CBIIT-CGBB/scCorr.

Robust Cluster-Based method for monitoring generalized linear profiles in phase I

Profile monitoring is one of the new statistical quality control methods used to evaluate the functional relationship between the descriptive and response variables to measure the process quality. Most of the studies in this field concern processes whose response variables follow the normal distribution function, but in many industries and services, this assumption is not true. The presence of outliers in the historical data set could have a deleterious effect on phase I parameter estimation. Therefore, in this paper, we propose a robust cluster-based method for estimating the parameters of generalized linear profiles in phase I. In this method, the effect of data contamination on estimating the generalized linear model parameters is reduced and as a result, the performance of T^2 control charts is improved. The performance of this method has been evaluated for two specific modes of generalized linear profiles, including logistic and Poisson profiles, based on a step shift. The simulation results indicate the superiority of this cluster-based method in comparison to the non-clustering method and provide a more accurate estimation of the parameters.

MMC-DIA: multi-metric clustering with differential interference alignment for improving small cell performance

Interference in small cells occurs due to the interoperability of different wireless communication technologies. Uncontrolled interference defaces the increasing user density and subscriber services. Therefore, interference management is mandatory to balance user service and performance enhancements. In this paper, multi-metric clustering with differential interference alignment (MMC-DIA) for leveraging the performance of small cell users is presented. This proposed technique operates in two phases namely clustering and differential interference alignment. In the clustering process, sum-rate maximization objective based grouping of small cell users is performed to retain the efficiency of communication. In a differential IA phase, the transmitted signal is analyzed for its first and second order of assessment on the basis of transmitter–receiver communication interval. Pre-coding and cancellation matrix over the signal vectors are imposed in the periodic time intervals for improving the degree of freedom (DoF) and thereby retaining the efficiency of the system. This is applicable for both the first and second order signal derivatives to handle inter and intra cluster interference along with the objective satisfaction. The performance of the proposed technique is compared for sum-rate, spectral efficiency, and DoF with the existing methods and non-clustering method respectively. From the comparative analysis, the proposed MMC-DIA is found to improve spectral efficiency and sum rate by 6.84% and 11.18% respectively. Similarly, with respect to the varying transmit power, the proposed MMC-DIA achieves 5.85% and 6.292% better spectral efficiency and sum rate.

Secure Mobile Agents Based Clustering Protocol using Modified PSO Algorithm

As per the studies on Wireless Sensor Network (WSN), the major concern of WSN is network lifespan and Quality of Service (QoS) improvement as the sensor nodes are having limited processing and battery capabilities. The clustering based methods delivered improved performance compared to non-clustering methods, but cluster heads (CHs) suffered from the excessive load which leads more energy consumption of sensor nodes that becomes the CH in each interval. The mobile agent’s technology solves such problems. But using mobile agent’s leads problems of data security and unreliable communications in WSN. In this paper, we proposed novel mobile specialists based clustering protocol utilizing the adjusted particle swarm optimization (PSO) algorithm called Secure Mobile Agent based Clustering using modified PSO (SMACP). To optimize the clustering performance we design modified PSO based in CH selection and cluster formation performed efficiently. To secure data communications using the mobile agents, we applied the trust evaluation scheme to evaluate each mobile agents and sensors using parameters such as residual energy, distance, and packet forwarding probability. The simulation results computed for different WSNs in terms of energy efficiency, average throughput, packet delivery ratio (PDR), also, start to finish delay. The exhibition demonstrates that SMACP outperformed the previous methods effectively.

A Comparison of Clustering and Prediction Methods for Identifying Key Chemical–Biological Features Affecting Bioreactor Performance

Chemical–biological systems, such as bioreactors, contain stochastic and non-linear interactions which are difficult to characterize. The highly complex interactions between microbial species and communities may not be sufficiently captured using first-principles, stationary, or low-dimensional models. This paper compares and contrasts multiple data analysis strategies, which include three predictive models (random forests, support vector machines, and neural networks), three clustering models (hierarchical, Gaussian mixtures, and Dirichlet mixtures), and two feature selection approaches (mean decrease in accuracy and its conditional variant). These methods not only predict the bioreactor outcome with sufficient accuracy, but the important features correlated with said outcome are also identified. The novelty of this work lies in the extensive exploration and critique of a wide arsenal of methods instead of single methods, as observed in many papers of similar nature. The results show that random forest models predict the test set outcomes with the highest accuracy. The identified contributory features include process features which agree with domain knowledge, as well as several different biomarker operational taxonomic units (OTUs). The results reinforce the notion that both chemical and biological features significantly affect bioreactor performance. However, they also indicate that the quality of the biological features can be improved by considering non-clustering methods, which may better represent the true behaviour within the OTU communities.

Performance Evaluation of Cluster Based Cooperative Cognitive Radio Network

Cognitive radio is regarded as one of the transformational technologies that play a fundamental role in establishment and development of next generation wireless networks. Clustering based cognitive radio networks can reduce the energy consumption and increase the spectrum sensing accuracy. In this paper, we investigate the performance of a cognitive radio network with a clustering architecture for cooperative spectrum sensing. The network efficiency is evaluated in terms of throughput and energy consumption, under fading channel conditions. Simulation results show significant improvement of energy efficiency compared to the conventional non-clustering method by ensuring allowable interference with primary users.

Testing clustering strategies for metabarcoding-based investigation of community-environment interactions.

The degradation of freshwater ecosystems has become a common ecological and environmental problem globally. Owing to the complexity of biological communities, there remain tremendous technical challenges for investigating influence of environmental stressors (e.g., chemical pollution) on biological communities. High-throughput sequencing-based metabarcoding provides a powerful tool to reveal complex interactions between environments and biological communities. Among many technical issues, the clustering strategies for operational taxonomic units (OTUs) which are crucial for assessing biodiversity of communities, may affect final conclusions. Here, we used zooplankton communities along an environmental pollution gradient in the Chaobai River in Northern China to test different clustering strategies, including nonclustering and clustering with varied thresholds. Our results showed that though the number of OTUs estimated by nonclustering strategies and clustering strategies with divergence thresholds of 99%-97% largely varied, they were able to identify the same set of significant environmental and spatial variables responsible for geographical distributions of zooplankton communities. In addition, the ecological conclusions obtained by clustering thresholds of 99%-97% were consistent with nonclustering strategies, where for all eight clustering scenarios we detected that species sorting predicted by environmental variables overrode dispersal as the dominant factor in structuring zooplankton communities. However, clustering with the divergence thresholds of <95% affected the environmental and spatial variables identified. We conclude that both newly developed nonclustering methods and traditional clustering methods with divergence thresholds ≥97% were reliable to reveal mechanisms of complex community-environment interactions, although different clustering strategies could lead to largely varied biodiversity estimates such as those for α-diversity.

Assessment of the assessment: evaluation of the model quality estimates in CASP10.

The article presents an assessment of the ability of the thirty-seven model quality assessment (MQA) methods participating in CASP10 to provide an a priori estimation of the quality of structural models, and of the 67 tertiary structure prediction groups to provide confidence estimates for their predicted coordinates. The assessment of MQA predictors is based on the methods used in previous CASPs, such as correlation between the predicted and observed quality of the models (both at the global and local levels), accuracy of methods in distinguishing between good and bad models as well as good and bad regions within them, and ability to identify the best models in the decoy sets. Several numerical evaluations were used in our analysis for the first time, such as comparison of global and local quality predictors with reference (baseline) predictors and a ROC analysis of the predictors' ability to differentiate between the well and poorly modeled regions. For the evaluation of the reliability of self-assessment of the coordinate errors, we used the correlation between the predicted and observed deviations of the coordinates and a ROC analysis of correctly identified errors in the models. A modified two-stage procedure for testing MQA methods in CASP10 whereby a small number of models spanning the whole range of model accuracy was released first followed by the release of a larger number of models of more uniform quality, allowed a more thorough analysis of abilities and inabilities of different types of methods. Clustering methods were shown to have an advantage over the single- and quasi-single- model methods on the larger datasets. At the same time, the evaluation revealed that the size of the dataset has smaller influence on the global quality assessment scores (for both clustering and nonclustering methods), than its diversity. Narrowing the quality range of the assessed models caused significant decrease in accuracy of ranking for global quality predictors but essentially did not change the results for local predictors. Self-assessment error estimates submitted by the majority of groups were poor overall, with two research groups showing significantly better results than the remaining ones.

Improved inference of gene regulatory networks through integrated Bayesian clustering and dynamic modeling of time-course expression data.

Inferring gene regulatory networks from expression data is difficult, but it is common and often useful. Most network problems are under-determined–there are more parameters than data points–and therefore data or parameter set reduction is often necessary. Correlation between variables in the model also contributes to confound network coefficient inference. In this paper, we present an algorithm that uses integrated, probabilistic clustering to ease the problems of under-determination and correlated variables within a fully Bayesian framework. Specifically, ours is a dynamic Bayesian network with integrated Gaussian mixture clustering, which we fit using variational Bayesian methods. We show, using public, simulated time-course data sets from the DREAM4 Challenge, that our algorithm outperforms non-clustering methods in many cases (7 out of 25) with fewer samples, rarely underperforming (1 out of 25), and often selects a non-clustering model if it better describes the data. Source code (GNU Octave) for BAyesian Clustering Over Networks (BACON) and sample data are available at: http://code.google.com/p/bacon-for-genetic-networks.

An efficient method for record management in flash memory environment

Flash memory has its unique characteristics: the write operation is much more costly than the read operation, and in-place updating is not allowed. In this paper, we analyze how these characteristics affect the performance of clustering and non-clustering methods for record management, and show that non-clustering is more suitable in flash memory environment. Also, we identify the problems of the existing non-clustering method when applied to flash memory environment without any modification, and propose an effective method for record management in flash memory databases. This method, which is basically based on the non-clustering method, tries to store consecutively inserted records in the same page in order to make it possible to process them with only one write operation. In this paper, we call this method group write. Moreover, we propose two novel techniques for achieving efficient group writes: (1) dedicated buffers for group writes and (2) free space lists managed in main memory for maintaining only those pages having large free space. Our method greatly improves the write performance of database applications running in flash memory. For performance evaluation, we conduct a variety of experiments. The results show that our method achieves speed up by up to 1.67 times compared with the original non-clustering method.