Dongqing Xie

A New Unsupervised Feature Ranking Method for Gene Expression Data Based on Consensus Affinity

Feature selection is widely established as one of the fundamental computational techniques in mining microarray data. Due to the lack of categorized information in practice, unsupervised feature selection is more practically important but correspondingly more difficult. Motivated by the cluster ensemble techniques, which combine multiple clustering solutions into a consensus solution of higher accuracy and stability, recent efforts in unsupervised feature selection proposed to use these consensus solutions as oracles. However, these methods are dependent on both the particular cluster ensemble algorithm used and the knowledge of the true cluster number. These methods will be unsuitable when the true cluster number is not available, which is common in practice. In view of the above problems, a new unsupervised feature ranking method is proposed to evaluate the importance of the features based on consensus affinity. Different from previous works, our method compares the corresponding affinity of each feature between a pair of instances based on the consensus matrix of clustering solutions. As a result, our method alleviates the need to know the true number of clusters and the dependence on particular cluster ensemble approaches as in previous works. Experiments on real gene expression data sets demonstrate significant improvement of the feature ranking results when compared to several state-of-the-art techniques.

Generalized Pair-Counting Similarity Measures for Clustering and Cluster Ensembles

In this paper, a number of pair-counting similarity measures associated with a general formulation of cluster ensemble are proposed. These measures are formulated based on our motivation to evaluate the consistency between an individual clustering solution and a cluster ensemble solution, or that between different cluster ensemble solutions, in a uniform manner. A number of criteria are proposed for the comparison of these generalized measures, from both the perspectives of theoretical analysis and experimental validation. We identify their different behaviors and their correlations in different scenarios of traditional clustering solutions and cluster ensembles, with the hope that the results of these studies could 1) serve as important criteria for the design and selection of evaluation measures for clustering solutions, and 2) provide explanations for ambiguous clustering results in related scenarios. Experiments with both synthetic and real data sets are conducted to verify our findings.

Joint User and Relay Selection for Cooperative NOMA Networks

This paper investigates the joint user and relay selection algorithm for cooperative non-orthogonal multiple access networks, where multiple users transmit messages to two destinations by utilizing multiple amplify-and-forward relays. To improve the outage performance of system, an optimal selection criterion is proposed at the user-relay pairs. We derive the closed-form analytical expressions on the outage probability, as well as the asymptotic expressions for large transmission power. Based on the derived analytical expressions, the effect of the number of relays and the target data rate on the outage performance is revealed. Results indicate that when the target data rate becomes small enough, the diversity order of the outage probability equals to the number of relays, while the target data rate grows without bound, the outage probability achieves one.

Semi-supervised clustering with pairwise and size constraints

In recent years, semi-supervised clustering receives considerable attention in the pattern recognition and data mining communities. This type of clustering algorithms takes advantage of partial prior knowledge, and significant improved performance beyond traditional unsupervised clustering algorithms is observed. In general, the partial prior knowledge is mainly in the form of pairwise constraints, which specify whether point pairs should be in the same cluster or in different clusters. Moreover, some other forms of constraints also attract research interests, for example, the balance constraint or the size constraint. However, it is also important to consider different types of constraints simultaneously, since different types of prior knowledge might have their own bias when considered separately. In this paper, we propose an improved algorithm to incorporate the pairwise and size constraints into a unified framework. Experiments on several benchmark data sets demonstrate that the proposed unified algorithm outperforms previous approaches under a variety of different conditions, which demonstrates that judicious integration of different types of constraints can result in improved performance than in those cases where only a single kind of constraint is used.

On the performance of three-dimensionalantenna arrays in millimetre wave propagation environments

In order to reap the full scale of benefits of millimetre wave (mmWave) massive multiple-input multiple-output (MIMO) systems, the design of antenna arrays at the transmitter or receiver becomes more critical due to the propagation characteristic at mm-frequencies. In this study, the authors investigate the performance of two types of antenna array, namely uniform rectangular planar array (URPA) and uniform cylindrical array (UCYA). The channel behaviour is presented in full-dimensional mmWave propagation conditions by considering both the azimuth and elevation dimensions. The squared inner product and singular value spread are studied for URPA and UCYA configurations, these properties reveal the effective interference and channels stability of antenna array. The authors also evaluate the achievable rate with the equal power allocation and water-pouring power allocation schemes. Simulation results show that under the same system configurations, the performance that includes the radiation pattern, the channel eigenvalue distribution, the effective interference, and the achievable rate, of UCYA configuration always outperforms that of URPA configuration. Therefore, it can be concluded that in three-dimensional propagation environments, the UCYA configuration is especially appealing for mmWave MIMO systems.

Consensus Comparative Analysis of Human Embryonic Stem Cell-Derived Cardiomyocytes

Global transcriptional analyses have been performed with human embryonic stem cells (hESC) derived cardiomyocytes (CMs) to identify molecules and pathways important for human CM differentiation, but variations in culture and profiling conditions have led to greatly divergent results among different studies. Consensus investigation to identify genes and gene sets enriched in multiple studies is important for revealing differential gene expression intrinsic to human CM differentiation independent of the above variables, but reliable methods of conducting such comparison are lacking. We examined differential gene expression between hESC and hESC-CMs from multiple microarray studies. For single gene analysis, we identified genes that were expressed at increased levels in hESC-CMs in seven datasets and which have not been previously highlighted. For gene set analysis, we developed a new algorithm, consensus comparative analysis (CSSCMP), capable of evaluating enrichment of gene sets from heterogeneous data sources. Based on both theoretical analysis and experimental validation, CSSCMP is more efficient and less susceptible to experimental variations than traditional methods. We applied CSSCMP to hESC-CM microarray data and revealed novel gene set enrichment (e.g., glucocorticoid stimulus), and also identified genes that might mediate this response. Our results provide important molecular information intrinsic to hESC-CM differentiation. Data and Matlab codes can be downloaded from S1 Data.

Gene set enrichment ensemble using fold change data only

In a number of biological studies, the raw gene expression data are not usually published due to different causes, such as data privacy and patent rights. Instead, significant gene lists with fold change values are usually provided in most studies. However, due to variations in data sources and profiling conditions, only a small number of common significant genes could be found among similar studies. Moreover, traditional gene set based analyses that consider these genes have not taken into account the fold change values, which may be important to distinguish between the different levels of significance of the genes. Human embryonic stem cell derived cardiomyocytes (hESC-CM) is a good representative of this category. hESC-CMs, with its role as a potentially unlimited source of human heart cells for regenerative medicine, have attracted the attentions of biological and medical researchers. Because of the difficulty of acquiring data and the resulting expenses, there are only a few related hESC-CM studies and few hESC-CM gene expression data are provided. In view of these challenges, we propose a new Gene Set Enrichment Ensemble (GSEE) approach to perform gene set based analysis on individual studies based on significant up-regulated gene lists with fold change data only. Our approach provides both explicit and implicit ways to utilize the fold change data, in order to make full use of scarce data. We validate our approach with hESC-CM data and fetal heart data, respectively. Experimental results on significant gene lists from different studies illustrate the effectiveness of our proposed approach.

AORS: Affinity-based outlier ranking score

Outlier ranking methods can provide a quantitative measure to evaluate the outlierness of data instances in data clustering and attract great interest in pattern recognition and data mining communities. However, it has been pointed out that the diverse scaling ranges of these scores bring difficulty to result interpretation. Moreover, popular outlier ranking scores based on simple distance measures might not accurately reflect the complex affinity among data points. In this paper, we propose a new outlier ranking method based on consensus affinity of a cluster ensemble. Two new outlier ranking scores generalized from well-known clustering evaluation measures, Rvv from the RAND measure and ARIvv from Adjusted Rand Index (ARI), are adopted for outlierness evaluation. Compared to other outlierness ranking measures, the two new measures have the desired bounds without additional transformations. Consistent with the improvement of Adjusted Rand Index (ARI) over RAND, we find that ARIvv also significantly outperforms Rvv. Benefiting from the consensus affinity of a cluster ensemble, our proposed method with the ARIvv score provides significant improvement beyond a number of competing algorithms on public UCI benchmark data sets. Studies with both theoretical analysis and experimental validation show the effectiveness of our proposed methods.

DF relaying networks in randomly distributed interference environments

In this paper, we consider a two-hop cooperative relaying network in a randomly distributed interference environment, where the source communicates with the destination, assisted by N decode-and-forward (DF) relays. We consider the interference-limited environments, where the received signals at the relays and the destination are degraded by random interference nodes distributed according to homogeneous Poisson point processes (PPPs). Based on received signal-to-interference ratio (SIR), relay selection has been deployed to determine one optimal relay out of N ones. To analyze the system performance, we derive both analytical and asymptotic expressions for outage probability under Rayleigh fading channel. From the given results, we see that the system diversity order on SIR varies reversely with the path loss factor α. Additionally, the given results suggest that the system performance is mainly limited by the second hop. Numerical verifications and simulation results are presented to validate our analysis.

Putative protein interaction analysis for human embryonic stem cell derived cardiomyocytes

Human embryonic stem cell derived cardiomyocytes (hESC-CMs) are well recognized as one of the most important topics in the biological and medical communities. However, related bioinformatics work on hESC-CMs has seldom reported because there are very scarce public hESC-CM expression data and very few samples. Moreover, traditional analysis based on protein protein interactions (PPIs), which are widely adopted in other kinds of species or organs, are used in very few studies as auxiliary tools to reveal partial topological characteristics. However, to our best knowledge, comprehensive investigation of interaction analysis for different hESC-CMs studies have never been reported. In view of these problems, we developed three different measures to conduct the putative protein interaction analysis of a number of hESC-CM studies with two different sets of PPI data. Results of our work gain deeper insight into related gene regulatory mechanisms and biological processes. Significant hESC-CMs with larger Fold Change (FC) values are found to have a larger probability to appear in PPI data. More interactions are observed in gene pairs with larger FC values than those with smaller ones. Significant hESC-CMs with larger FC values do not necessarily have a larger probability to be the hub ones in interaction networks.