BackgroundStability of multiple testing procedures, defined as the standard deviation of total number of discoveries, can be used as an indicator of variability of multiple testing procedures. Improving stability of multiple testing procedures can help to increase the consistency of findings from replicated experiments. Benjamini-Hochberg’s and Storey’s q-value procedures are two commonly used multiple testing procedures for controlling false discoveries in genomic studies. Storey’s q-value procedure has higher power and lower stability than Benjamini-Hochberg’s procedure. To improve upon the stability of Storey’s q-value procedure and maintain its high power in genomic data analysis, we propose a new multiple testing procedure, named Bon-EV, to control false discovery rate (FDR) based on Bonferroni’s approach.ResultsSimulation studies show that our proposed Bon-EV procedure can maintain the high power of the Storey’s q-value procedure and also result in better FDR control and higher stability than Storey’s q-value procedure for samples of large size(30 in each group) and medium size (15 in each group) for either independent, somewhat correlated, or highly correlated test statistics. When sample size is small (5 in each group), our proposed Bon-EV procedure has performance between the Benjamini-Hochberg procedure and the Storey’s q-value procedure. Examples using RNA-Seq data show that the Bon-EV procedure has higher stability than the Storey’s q-value procedure while maintaining equivalent power, and higher power than the Benjamini-Hochberg’s procedure.ConclusionsFor medium or large sample sizes, the Bon-EV procedure has improved FDR control and stability compared with the Storey’s q-value procedure and improved power compared with the Benjamini-Hochberg procedure. The Bon-EV multiple testing procedure is available as the BonEV package in R for download at https://CRAN.R-project.org/package=BonEV.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1414-x) contains supplementary material, which is available to authorized users.

BackgroundBreast and cervical cancers have emerged as major global health challenges and disproportionately lead to excess morbidity and mortality in low- and middle-income countries (LMICs) when compared to high-income countries. The objective of this paper was to highlight key findings, recommendations, and gaps in research and practice identified through a scoping study of recent reviews in breast and cervical cancer in LMICs.MethodsWe conducted a scoping study based on the six-stage framework of Arskey and O’Malley. We searched PubMed, Cochrane Reviews, and CINAHL with the following inclusion criteria: 1) published between 2005-February 2015, 2) focused on breast or cervical cancer 3) focused on LMIC, 4) review article, and 5) published in English.ResultsThrough our systematic search, 63 out of the 94 identified cervical cancer reviews met our selection criteria and 36 of the 54 in breast cancer. Cervical cancer reviews were more likely to focus upon prevention and screening, while breast cancer reviews were more likely to focus upon treatment and survivorship. Few of the breast cancer reviews referenced research and data from LMICs themselves; cervical cancer reviews were more likely to do so. Most reviews did not include elements of the PRISMA checklist.ConclusionOverall, a limited evidence base supports breast and cervical cancer control in LMICs. Further breast and cervical cancer prevention and control studies are necessary in LMICs.

Purpose: To describe the basic concepts of social network analysis (SNA), and introduce some applications of this technique in assessing aspects of institutional culture. Methods: We applied SNA to 3 settings; team function in the intensive-care unit, interdisciplinary composition of advisory committees for federal career development awardees, and relationships between Key Function directors at an institution-wide Clinical Translational Sciences Institute. Findings: In the ICU setting, SNA provides interpretable summaries of aspects of clinical team functioning. When applied to membership on mentorship committees, it allows for summary descriptions of the degree of interdisciplinarity of various clinical departments. Finally, when applied to relationships among leaders of an institution-wide research it highlights potential areas of problems in relationships among academic departments. In all cases, data collection is relatively rapid, thereby allowing for the possibility of frequent repeated analyses over time. Conclusions: SNA provides a useful and standardized set of tools for measuring important aspects of team function, interdisciplinarity, and organizational culture that may otherwise be difficult to measure in an objective way.