This brief presents a novel compact double-pole triple-throw switch based multi-phase integrated 6-bit passive phase shifter. An innovative topology adopts one high-pass circuit and two low-pass circuits connected in parallel via double-pole triple-throw switch is proposed to replace 22.5° and 45° inductor-based bridged T-type phase shifting cells for 50% chip size reduction. Furthermore, in 5.625° phase shifting cell, a novel topology whose phase error is smaller than 0.05° is proposed by adopting a phase shifting fluctuation optimization unit and two low-pass circuits connected in parallel. The proposed phase shifter is implemented in 55 nm CMOS technology. Electromagnetism simulation results show that the root-mean-square (RMS) phase error of this phase shifter is 2.5°~4.2° from 14 to 18 GHz. An average insertion loss (IL) of -13.1±2.1 dB is achieved. The core chip area is 1.2*0.62 mm2.

SUMMARY: Pathway analysis is widely used in genomics and omics research, but the data visualization has been highly limited in function, pathway coverage and data format. Here, we develop SBGNview a comprehensive R package to address these needs. By adopting the standard SBGN format, SBGNview greatly extend the coverage of pathway-based analysis and data visualization to essentially all major pathway databases beyond KEGG, including 5200 reference pathways and over 3000 species. In addition, SBGNview substantially extends or exceeds current tools (esp. Pathview) in both design and function, including standard input format (SBGN), high-quality output graphics (SVG format) convenient for both interpretation and further update, and flexible and open-end workflow for iterative editing and interactive visualization (Highlighter module). In addition to pathway analysis and data visualization, SBGNview provides essential infrastructure for SBGN data manipulation and processing. AVAILABILITY AND IMPLEMENTATION: The data underlying this article are available as part of the SBGNview package is available on both GitHub and Bioconductor: https://github.com/datapplab/SBGNview, https://bioconductor.org/packages/SBGNview. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Pathview is a novel tool set for pathway-based data integration and visualization. It maps and renders user data on relevant pathway graphs. Users only need to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps and integrates user data onto the pathway and renders pathway graphs with the mapped data. Although built as a stand-alone program, Pathview may seamlessly integrate with pathway and functional analysis tools for large-scale and fully automated analysis pipelines. AVAILABILITY: The package is freely available under the GPLv3 license through Bioconductor and R-Forge. It is available at http://bioconductor.org/packages/release/bioc/html/pathview.html and at http://Pathview.r-forge.r-project.org/. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes and has established major advantages over individual gene analyses, including greater robustness, sensitivity and biological relevance. However, previous GSA methods have limited usage as they cannot handle datasets of different sample sizes or experimental designs. RESULTS: To address these limitations, we present a new GSA method called Generally Applicable Gene-set Enrichment (GAGE). We successfully apply GAGE to multiple microarray datasets with different sample sizes, experimental designs and profiling techniques. GAGE shows significantly better results when compared to two other commonly used GSA methods of GSEA and PAGE. We demonstrate this improvement in the following three aspects: (1) consistency across repeated studies/experiments; (2) sensitivity and specificity; (3) biological relevance of the regulatory mechanisms inferred.GAGE reveals novel and relevant regulatory mechanisms from both published and previously unpublished microarray studies. From two published lung cancer data sets, GAGE derived a more cohesive and predictive mechanistic scheme underlying lung cancer progress and metastasis. For a previously unpublished BMP6 study, GAGE predicted novel regulatory mechanisms for BMP6 induced osteoblast differentiation, including the canonical BMP-TGF beta signaling, JAK-STAT signaling, Wnt signaling, and estrogen signaling pathways-all of which are supported by the experimental literature. CONCLUSION: GAGE is generally applicable to gene expression datasets with different sample sizes and experimental designs. GAGE consistently outperformed two most frequently used GSA methods and inferred statistically and biologically more relevant regulatory pathways. The GAGE method is implemented in R in the "gage" package, available under the GNU GPL from http://sysbio.engin.umich.edu/~luow/downloads.php.

BACKGROUND: Probability based statistical learning methods such as mutual information and Bayesian networks have emerged as a major category of tools for reverse engineering mechanistic relationships from quantitative biological data. In this work we introduce a new statistical learning strategy, MI3 that addresses three common issues in previous methods simultaneously: (1) handling of continuous variables, (2) detection of more complex three-way relationships and (3) better differentiation of causal versus confounding relationships. With these improvements, we provide a more realistic representation of the underlying biological system. RESULTS: We test the MI3 algorithm using both synthetic and experimental data. In the synthetic data experiment, MI3 achieved an absolute sensitivity/precision of 0.77/0.83 and a relative sensitivity/precision both of 0.99. In addition, MI3 significantly outperformed the control methods, including Bayesian networks, classical two-way mutual information and a discrete version of MI3. We then used MI3 and control methods to infer a regulatory network centered at the MYC transcription factor from a published microarray dataset. Models selected by MI3 were numerically and biologically distinct from those selected by control methods. Unlike control methods, MI3 effectively differentiated true causal models from confounding models. MI3 recovered major MYC cofactors, and revealed major mechanisms involved in MYC dependent transcriptional regulation, which are strongly supported by literature. The MI3 network showed that limited sets of regulatory mechanisms are employed repeatedly to control the expression of large number of genes. CONCLUSION: Overall, our work demonstrates that MI3 outperforms the frequently used control methods, and provides a powerful method for inferring mechanistic relationships underlying biological and other complex systems. The MI3 method is implemented in R in the "mi3" package, available under the GNU GPL from http://sysbio.engin.umich.edu/~luow/downloads.php and from the R package archive CRAN.