ALBERT

Description: Background: Recently, the Bayesian method becomes more popular for analyzing high dimensional gene expression data as it allows us to borrow information across different genes and provides powerful estimators for evaluating gene expression levels. It is crucial to develop a simple but efficient gene selection algorithm for detecting differentially expressed (DE) genes based on the Bayesian estimators. Results: In this paper, by extending the two-criterion idea of Chen et al. (Chen M-H, Ibrahim JG, Chi Y-Y. A new class of mixture models for differential gene expression in DNA microarray data. J Stat Plan Inference. 2008;138:387–404), we propose two new gene selection algorithms for general Bayesian models and name these new methods as the confident difference criterion methods. One is based on the standardized differences between two mean expression values among genes; the other adds the differences between two variances to it. The proposed confident difference criterion methods first evaluate the posterior probability of a gene having different gene expressions between competitive samples and then declare a gene to be DE if the posterior probability is large. The theoretical connection between the proposed first method based on the means and the Bayes factor approach proposed by Yu et al. (Yu F, Chen M-H, Kuo L. Detecting differentially expressed genes using alibrated Bayes factors. Statistica Sinica. 2008;18:783–802) is established under the normal-normal-model with equal variances between two samples. The empirical performance of the proposed methods is examined and compared to those of several existing methods via several simulations. The results from these simulation studies show that the proposed confident difference criterion methods outperform the existing methods when comparing gene expressions across different conditions for both microarray studies and sequence-based high-throughput studies. A real dataset is used to further demonstrate the proposed methodology. In the real data application, the confident difference criterion methods successfully identified more clinically important DE genes than the other methods. Conclusion: The confident difference criterion method proposed in this paper provides a new efficient approach for both microarray studies and sequence-based high-throughput studies to identify differentially expressed genes.

Description: Background: An academic, community medicine partnership was established to build a phenotype-to-outcome model targeting chronic pain. This model will be used to drive clinical decision support for pain medicine in the community setting. The first step in this effort is an examination of the electronic health records (EHR) from clinics that treat chronic pain. The biopsychosocial components provided by both patients and care providers must be of sufficient scope to populate the spectrum of patient types, treatment modalities, and possible outcomes. Methods: The patient health records from a large Midwest pain medicine practice (Michigan Pain Consultants, PC) contains physician notes, administrative codes, and patient-reported outcomes (PRO) on over 30,000 patients during the study period spanning 2010 to mid-2014. The PRO consists of a regularly administered Pain Health Assessment (PHA), a biopsychosocial, demographic, and symptomology questionnaire containing 163 items, which is completed approximately every six months with a compliance rate of over 95 %. The biopsychosocial items (74 items with Likert scales of 0–10) were examined by exploratory factor analysis and descriptive statistics to determine the number of independent constructs available for phenotypes and outcomes. Pain outcomes were examined both in the aggregate and the mean of longitudinal changes in each patient. Results: Exploratory factor analysis of the intake PHA revealed 15 orthogonal factors representing pain levels; physical, social, and emotional functions; the effects of pain on these functions; vitality and health; and measures of outcomes and satisfaction. Seven items were independent of the factors, offering unique information. As an exemplar of outcomes from the follow-up PHAs, patients reported approximately 60 % relief in their pain. When examined in the aggregate, patients showed both a decrease in pain levels and an increase in coping skills with an increased number of visits. When examined individually, 80-85 % of patients presenting with the highest pain levels reported improvement by approximately two points on an 11-point pain scale. Conclusions: We conclude that the data available in a community practice can be a rich source of biopsychosocial information relevant to the phenotypes of chronic pain. It is anticipated that phenotype linkages to best treatments and outcomes can be constructed from this set of records.

Description: Background: The Sequence Read Archive (SRA) is the largest public repository of sequencing data from the nextgeneration of sequencing platforms including Illumina (Genome Analyzer, HiSeq, MiSeq, .etc),Roche 454 GS System, Applied Biosystems SOLiD System, Helicos Heliscope, PacBio RS, andothers. Results: SRAdb is an attempt to make query of the metadata associated with SRA submission, study, sample,experiment and run more robust and precise, and make access to sequencing data in the SRA easier.We have parsed all the SRA metadata into a SQLite database that is routinely updated and can beeasily distributed. The SRAdb R/Bioconductor package then utilizes this SQLite database forquerying and accessing metadata. Full text search functionality makes querying metadata veryflexible and powerful. Fastq files associated with query results can be downloaded easily for localanalysis. The package also includes an interface from R to a popular genome browser, the IntegratedGenomics Viewer. Conclusions: SRAdb Bioconductor package provides a convenient and integrated framework to query and accessSRA metadata quickly and powerfully from within R.

Description: Background: Each omics platform is now able to generate a large amount of data. Genomics, proteomics,metabolomics, interactomics are compiled at an ever increasing pace and now form a core part of thefundamental systems biology framework. Recently, several integrative approaches have beenproposed to extract meaningful information. However, these approaches lack of visualisation outputsto fully unravel the complex associations between different biological entities. Results: The multivariate statistical approaches 'regularized Canonical Correlation Analysis' and 'sparsePartial Least Squares regression' were recently developed to integrate two types of highlydimensional 'omics' data and to select relevant information. Using the results of these methods, wepropose to revisit few graphical outputs to better understand the relationships between two 'omics'data and to better visualise the correlation structure between the different biological entities. Thesegraphical outputs include Correlation Circle plots, Relevance Networks and Clustered Image Maps.We demonstrate the usefulness of such graphical outputs on several biological data sets and furtherassess their biological relevance using gene ontology analysis. Conclusions: Such graphical outputs are undoubtedly useful to aid the interpretation of these promising integrativeanalysis tools and will certainly help in addressing fundamental biological questions andunderstanding systems as a whole. AvailabilityThe graphical tools described in this paper are implemented in the freely available R packagemixOmics and in its associated web application.

Description: Background: Greater use of computerized decision support (DS) systems could address continuing safetyand quality problems in healthcare, but the healthcare field has struggled to implement DStechnology. This study surveys DS experience across multiple non-healthcare disciplines fornew insights that are generalizable to healthcare provider decisions. In particular, it soughtdesign principles and lessons learned from the other disciplines that could inform efforts toaccelerate the adoption of clinical decision support (CDS). Methods: Our systematic review drew broadly from non-healthcare databases in the basic sciences,social sciences, humanities, engineering, business, and defense: PsychINFO, BusinessSourcePremier, Social Sciences Abstracts, Web of Science, and Defense Technical InformationCenter. Because our interest was in DS that could apply to clinical decisions, we selectedarticles that (1) provided a review, overview, discussion of lessons learned, or an evaluationof design or implementation aspects of DS within a non-healthcare discipline and (2)involved an element of human judgment at the individual level, as opposed to decisions thatcan be fully automated or that are made at the organizational level. Results: Clinical decisions share some similarities with decisions made by military commanders,business managers, and other leaders: they involve assessing new situations and choosingcourses of action with major consequences, under time pressure, and with incompleteinformation. We identified seven high-level DS system design features from the nonhealthcareliterature that could be applied to CDS: providing broad, system-levelperspectives; customizing interfaces to specific users and roles; making the DS reasoningtransparent; presenting data effectively; generating multiple scenarios covering disparateoutcomes (e.g., effective; effective with side effects; ineffective); allowing for contingentadaptations; and facilitating collaboration. The article provides examples of each feature. TheDS literature also emphasizes the importance of organizational culture and training inimplementation success. The literature contrasts "rational-analytic" vs. "naturalistic-intuitive"decision-making styles, but the best approach is often a balanced approach that combinesboth styles. It is also important for DS systems to enable exploration of multiple assumptions,and incorporation of new information in response to changing circumstances. Conclusions: Complex, high-level decision-making has common features across disciplines as seeminglydisparate as defense, business, and healthcare. National efforts to advance the healthinformation technology agenda through broader CDS adoption could benefit by applying theDS principles identified in this review.

Description: Background: Retinal ganglion cell (RGC) loss is one of the earliest and most important cellular changes in glaucoma. The DARC (Detection of Apoptosing Retinal Cells) technology enables in vivo real-time non-invasive imaging of single apoptosing retinal cells in animal models of glaucoma and Alzheimer's disease. To date, apoptosing RGCs imaged using DARC have been counted manually. This is time-consuming, labour-intensive, vulnerable to bias, and has considerable inter- and intra-operator variability. Results: A semi-automated algorithm was developed which enabled automated identification of apoptosing RGCs labeled with fluorescent Annexin-5 on DARC images. Automated analysis included a pre-processing stage involving local-luminance and local-contrast "gain control", a "blob analysis" step to differentiate between cells, vessels and noise, and a method to exclude non-cell structures using specific combined 'size' and 'aspect' ratio criteria. Apoptosing retinal cells were counted by 3 masked operators, generating 'Gold-standard' mean manual cell counts, and were also counted using the newly developed automated algorithm. Comparison between automated cell counts and the mean manual cell counts on 66 DARC images showed significant correlation between the two methods (Pearson's correlation coefficient 0.978 (p 〈 0.001), R Squared = 0.956. The Intraclass correlation coefficient was 0.986 (95% CI 0.977-0.991, p 〈 0.001), and Cronbach's alpha measure of consistency = 0.986, confirming excellent correlation and consistency. No significant difference (p = 0.922, 95% CI: -5.53 to 6.10) was detected between the cell counts of the two methods. Conclusions: The novel automated algorithm enabled accurate quantification of apoptosing RGCs that is highly comparable to manual counting, and appears to minimise operator-bias, whilst being both fast and reproducible. This may prove to be a valuable method of quantifying apoptosing retinal cells, with particular relevance to translation in the clinic, where a Phase I clinical trial of DARC in glaucoma patients is due to start shortly.

Description: Translational genomics research in cancers, e.g., International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), has generated large multidimensional datasets from high-throughput technologi...

Description: Using phylogenomic analysis tools for tracking pathogens has become standard practice in academia, public health agencies, and large industries. Using the same raw read genomic data as input, there are several...

Description: The B and T cells of the human adaptive immune system leverage a highly diverse repertoire of antigen-specific receptors to protect the human body from pathogens. The sequencing and analysis of immune repertoi...