Motivation: Genome-wide measurements of genetic and epigenetic alterations are generating more and more high-dimensional binary data. The special mathematical characteristics of binary data make the direct use of the classical principal component analysis (PCA) model to explore low-dimensional structures less obvious. Although there are several PCA alternatives for binary data in the psychometric, data analysis and machine learning literature, they are not well known to the bioinformatics community. Results: In this article, we introduce the motivation and rationale of some parametric and nonparametric versions of PCA specifically geared for binary data. Using both realistic simulations of binary data as well as mutation, CNA and methylation data of the Genomic Determinants of Sensitivity in Cancer 1000 (GDSC1000), the methods were explored for their performance with respect to finding the correct number of components, overfit, finding back the correct low-dimensional structure, variable importance, etc. The results show that if a low-dimensional structure exists in the data, that most of the methods can find it. When assuming a probabilistic generating process is underlying the data, we recommend to use the parametric logistic PCA model, while when such an assumption is not valid and the data are considered as given, the nonparametric Gifi model is recommended. Availability: The codes to reproduce the results in this article are available at the homepage of the Biosystems Data Analysis group (www.bdagroup.nl).

Motivation: In biology, we are often faced with multiple datasets recorded on the same set of objects, such as multi-omics and phenotypic data of the same tumors. These datasets are typically not independent from each other. For example, methylation may influence gene expression, which may, in turn, influence drug response. Such relationships can strongly affect analyses performed on the data, as we have previously shown for the identification of biomarkers of drug response. Therefore, it is important to be able to chart the relationships between datasets. Results: We present iTOP, a methodology to infer a topology of relationships between datasets. We base this methodology on the RV coefficient, a measure of matrix correlation, which can be used to determine how much information is shared between two datasets. We extended the RV coefficient for partial matrix correlations, which allows the use of graph reconstruction algorithms, such as the PC algorithm, to infer the topologies. In addition, since multi-omics data often contain binary data (e.g. mutations), we also extended the RV coefficient for binary data. Applying iTOP to pharmacogenomics data, we found that gene expression acts as a mediator between most other datasets and drug response: only proteomics clearly shares information with drug response that is not present in gene expression. Based on this result, we used TANDEM, a method for drug response prediction, to identify which variables predictive of drug response were distinct to either gene expression or proteomics. Availability and implementation: An implementation of our methodology is available in the R package iTOP on CRAN. Additionally, an R Markdown document with code to reproduce all figures is provided as Supplementary Material. Supplementary information: Supplementary data are available at Bioinformatics online.

The 2D transfer function based on scalar value and gradient magnitude (SG-TF) is popularly used in volume rendering. However, it is plagued by the boundary-overlapping problem: different structures with similar attributes have the same region in SG-TF space, and their boundaries are usually connected. The SG-TF thus often fails in separating these structures (or their boundaries) and has limited ability to classify different objects in real-world 3D images. To overcome such a difficulty, we propose a novel method for boundary separation by integrating spatial connectivity computation of the boundaries and set operations on boundary voxels into the SG-TF. Specifically, spatial positions of boundaries and their regions in the SG-TF space are computed, from which boundaries can be well separated and volume rendered in different colors. In the method, the boundaries are divided into three classes and different boundary-separation techniques are applied to them, respectively. The complex task of separating various boundaries in 3D images is then simplified by breaking it into several small separation problems. The method shows good object classification ability in real-world 3D images while avoiding the complexity of high-dimensional transfer functions. Its effectiveness and validation is demonstrated by many experimental results to visualize boundaries of different structures in complex real-world 3D images.

The control strategies of Doubly Fed Induction Generator (DFIG) system output current unbalance and distortion suppression have been well investigated in detail, with the implementation of two kinds of resonant regulators, i.e., conventional Resonance (R) regulator or Vector Proportional Integral (VPI) regulator. Nevertheless, these two resonance regulators have never been compared from the perspective of suppression capability of output current unbalance and distortion. In this paper, the impedance based analysis method is adopted to theoretically explain and compare the DFIG system impedance reshaping though the introduction of R and VPI regulator. It is pointed out that, when implemented in the DFIG system output current unbalance and distortion suppression, the VPI regulator (equivalent to the combination of virtual positive inductor and virtual positive resistor) has two advantages over R regulator (equivalent to the combination of virtual positive resistor and virtual negative inductor), i.e., better high order harmonic distortion suppression. The theoretical analysis and MATLAB simulation results have validated the correctness of this conclusion.

The Doubly-Fed Induction Generator (DFIG) based wind turbine system is presently dominant in the wind turbine market. Due to heavy load switch-off and faults in the power grid, voltage swells may occur and this phenomenon is currently given sufficient insights. This paper starts to describe the DFIG modeling and challenges when facing the symmetrical voltage swell. Then, the High Voltage Ride-Through (HVRT) capability of the DFIG can be calculated by using the demagnetizing current control, and the stator current, rotor current as well as the electromagnetic toque can be deduced during the transient voltage swell and its recovery. It is concluded that although both higher swell level and higher rotor speed cause higher rotor electromotive force, the doubly-fed induction generator can successfully ride through the grid fault due to the relatively small swell level required by the modern grid codes. Additionally, the calculated maximum stresses of the DFIG can be verified by simulation results in terms of the rotor current, stator current, and the toque at various swell levels.

In this work, a novel salient object detection method is proposed based on the saliency driven clustering. To capture visual patterns of an image, the color contrast prior and boundary prior are utilized to generate the image clusters automatically. Then, a simple operation like regional saliency computation is applied to refine the saliency maps generated by two priors. The final saliency map are obtained by combining the refined contrast prior saliency and boundary prior saliency. Extensive experiments show that our proposed model achieves better performance on salient region detection against the state-of-the-art methods.