Multi-omics experiments are increasingly commonplace in biomedical research, and add layers of complexity to experimental design, data integration, and analysis. R and Bioconductor provide a generic framework for statistical analysis and visualization, as well as specialized data classes for a variety of high-throughput data types, but methods are lacking for integrative analysis of multi-omics experiments. The MultiAssayExperiment software package, implemented in R and leveraging Bioconductor software and design principles, provides for the coordinated representation of, storage of, and operation on multiple diverse genomics data. We provide the unrestricted multiple ‘omics data for each cancer tissue in The Cancer Genome Atlas (TCGA) as ready-to-analyze MultiAssayExperiment objects, and demonstrate in these and other datasets how the software simplifies data representation, statistical analysis, and visualization. The MultiAssayExperiment Bioconductor package reduces major obstacles to efficient, scalable and reproducible statistical analysis of multi-omics data and enhances data science applications of multiple omics datasets.

PURPOSE Investigations of the molecular basis for the development, progression, and treatment of cancer increasingly use complementary genomic assays to gather multiomic data, but management and analysis of such data remain complex. The cBioPortal for cancer genomics currently provides multiomic data from > 260 public studies, including The Cancer Genome Atlas (TCGA) data sets, but integration of different data types remains challenging and error prone for computational methods and tools using these resources. Recent advances in data infrastructure within the Bioconductor project enable a novel and powerful approach to creating fully integrated representations of these multiomic, pan-cancer databases. METHODS We provide a set of R/Bioconductor packages for working with TCGA legacy data and cBioPortal data, with special considerations for loading time; efficient representations in and out of memory; analysis platform; and an integrative framework, such as MultiAssayExperiment. Large methylation data sets are provided through out-of-memory data representation to provide responsive loading times and analysis capabilities on machines with limited memory. RESULTS We developed the curatedTCGAData and cBioPortalData R/Bioconductor packages to provide integrated multiomic data sets from the TCGA legacy database and the cBioPortal web application programming interface using the MultiAssayExperiment data structure. This suite of tools provides coordination of diverse experimental assays with clinicopathological data with minimal data management burden, as demonstrated through several greatly simplified multiomic and pan-cancer analyses. CONCLUSION These integrated representations enable analysts and tool developers to apply general statistical and plotting methods to extensive multiomic data through user-friendly commands and documented examples.

Genetic validation of a long-standing hypothesis suggests that further investigation of the effects, particularly of copper, on IHD may provide a practical means of reducing the leading cause of mortality and morbidity.

Purpose To evaluate the value of multiparametric quantitative ultrasound imaging (QUI) in assessing chronic kidney disease (CKD) using kidney biopsy pathology as the reference standard. Methods We prospectively measured multiparametric QUI markers with grayscale, spectral Doppler, and acoustic radiation force impulse imaging in 25 patients with CKD prior to their kidney biopsies and in 10 healthy volunteers. Based on all pathology (glomerulosclerosis, IF/TA, arteriosclerosis, and edema) scores, the 25 CKD patients were classified into mild (no grade 3 and < 2 of grade 2) and moderate to severe (at least 2 of grade 2 or 1 of grade 3) CKD groups. Multiparametric QUI in the study included kidney length, cortical thickness, pixel-intensity, parenchymal shear wave velocity (SWV), intrarenal artery peak systolic velocity (PSV), end diastolic velocity (EDV), and resistive index (RI). We tested the difference in QUI parameters among mild CKD, moderate to severe CKD, and healthy controls using ANOVA, analyzed correlations of QUI parameters to kidney pathology scores and to eGFR using the Pearson correlation coefficient, and examined the diagnostic performance of QUI parameters in determining moderate CKD and eGFR <60 using ROC curve analysis. Results There were significant differences in cortical thickness, pixel-intensity, PSV, and EDV among the 3 groups (all p< 0.01). Among QUI parameters, the top AUROCs of PSV and EDV for determining pathologic moderate to severe CKD were 0.88 and 0.97, and for eGFR <60 were 0.76 and 0.86, respectively. Moderate to good correlations were found for PSV, EDV, and pixel-intensity to pathology score and eGFR. Conclusion PSV, EDV, and pixel-intensity are valuable in determining moderate to severe CKD. The value of SWV in assessing CKD needs further investigation.

Introduction: Diabetic dyslipidaemia is characterised by hypertriglyceridaemia, low High Density Lipoprotein (HDL), postprandial lipimea, small and dense LDL particles is considered to be a major predisposing factor for various macrovascular complications. Omega-3 fatty acids are fish oil derivative introduced in the market for dyslipidaemia associated with increased triglyceride level. Aim:To study the effect of omega-3 fatty acids on lipid profile in Type II diabetes patients.