15 research outputs found

    ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count datasets

    Get PDF
    <p>Abstract</p> <p>1 Background</p> <p>RNA sequencing is a flexible and powerful new approach for measuring gene, exon, or isoform expression. To maximize the utility of RNA sequencing data, new statistical methods are needed for clustering, differential expression, and other analyses. A major barrier to the development of new statistical methods is the lack of RNA sequencing datasets that can be easily obtained and analyzed in common statistical software packages such as R. To speed up the development process, we have created a resource of analysis-ready RNA-sequencing datasets.</p> <p>2 Description</p> <p>ReCount is an online resource of RNA-seq gene count tables and auxilliary data. Tables were built from raw RNA sequencing data from 18 different published studies comprising 475 samples and over 8 billion reads. Using the Myrna package, reads were aligned, overlapped with gene models and tabulated into gene-by-sample count tables that are ready for statistical analysis. Count tables and phenotype data were combined into Bioconductor ExpressionSet objects for ease of analysis. ReCount also contains the Myrna manifest files and R source code used to process the samples, allowing statistical and computational scientists to consider alternative parameter values.</p> <p>3 Conclusions</p> <p>By combining datasets from many studies and providing data that has already been processed from. fastq format into ready-to-use. RData and. txt files, ReCount facilitates analysis and methods development for RNA-seq count data. We anticipate that ReCount will also be useful for investigators who wish to consider cross-study comparisons and alternative normalization strategies for RNA-seq.</p

    Distance Functions and Attribute Weighting in a K-Nearest Neighbors Classifier

    Get PDF
    To assess environmental health of a stream, field, or other ecological object, characteristics of that object should be compared to a set of reference objects known to be healthy. Using streams as objects, we propose a k-nearest neighbors algorithm (Bates Prins and Smith, 2006) to find the appropriate set of reference streams to use as a comparison set for any given test stream. Previously, investigations of the k-nearest neighbors algorithm have utilized a variety of distance functions, the best of which has been the Interpolated Value Difference Metric (IVDM), proposed by Wilson and Martinez (1997). We propose two alternatives to the IVDM: Wilson and Martinez\u27s Windowed Value Difference Metric (WVDM) and the Density-Based Value Difference Metric (DBVDM) developed by Wojna (2005). We extend the WVDM and DBVDM to handle continuous response variables and compare these distance measures to the IVDM within the ecological k-nearest neighbors context. Additionally, we compare two existing attribute weighting schemes (Wojna 2005) when applied to the IVDM, WVDM, and DBVDM, and we propose a new attribute weighting method for use with these distance functions as well. In assessing environmental impairment, the WVDM and DBVDM were slight improvements over the IVDM. Attribute weighting also increased the effectiveness of the k-nearest neighbors algorithm in this ecological setting. This research was supported by NSF grant NSF-DMS 0552577 and was conducted during an 8-week summer research experience for undergraduates (REU)

    Distance Functions and Attribute Weighting in a K-Nearest Neighbors Classifier

    Get PDF
    To assess environmental health of a stream, field, or other ecological object, characteristics of that object should be compared to a set of reference objects known to be healthy. Using streams as objects, we propose a k-nearest neighbors algorithm (Bates Prins and Smith, 2006) to find the appropriate set of reference streams to use as a comparison set for any given test stream. Previously, investigations of the k-nearest neighbors algorithm have utilized a variety of distance functions, the best of which has been the Interpolated Value Difference Metric (IVDM), proposed by Wilson and Martinez (1997). We propose two alternatives to the IVDM: Wilson and Martinez\u27s Windowed Value Difference Metric (WVDM) and the Density-Based Value Difference Metric (DBVDM) developed by Wojna (2005). We extend the WVDM and DBVDM to handle continuous response variables and compare these distance measures to the IVDM within the ecological k-nearest neighbors context. Additionally, we compare two existing attribute weighting schemes (Wojna 2005) when applied to the IVDM, WVDM, and DBVDM, and we propose a new attribute weighting method for use with these distance functions as well. In assessing environmental impairment, the WVDM and DBVDM were slight improvements over the IVDM. Attribute weighting also increased the effectiveness of the k-nearest neighbors algorithm in this ecological setting. This research was supported by NSF grant NSF-DMS 0552577 and was conducted during an 8-week summer research experience for undergraduates (REU)

    Proceedings of the 3rd Biennial Conference of the Society for Implementation Research Collaboration (SIRC) 2015: advancing efficient methodologies through community partnerships and team science

    Get PDF
    It is well documented that the majority of adults, children and families in need of evidence-based behavioral health interventionsi do not receive them [1, 2] and that few robust empirically supported methods for implementing evidence-based practices (EBPs) exist. The Society for Implementation Research Collaboration (SIRC) represents a burgeoning effort to advance the innovation and rigor of implementation research and is uniquely focused on bringing together researchers and stakeholders committed to evaluating the implementation of complex evidence-based behavioral health interventions. Through its diverse activities and membership, SIRC aims to foster the promise of implementation research to better serve the behavioral health needs of the population by identifying rigorous, relevant, and efficient strategies that successfully transfer scientific evidence to clinical knowledge for use in real world settings [3]. SIRC began as a National Institute of Mental Health (NIMH)-funded conference series in 2010 (previously titled the “Seattle Implementation Research Conference”; $150,000 USD for 3 conferences in 2011, 2013, and 2015) with the recognition that there were multiple researchers and stakeholdersi working in parallel on innovative implementation science projects in behavioral health, but that formal channels for communicating and collaborating with one another were relatively unavailable. There was a significant need for a forum within which implementation researchers and stakeholders could learn from one another, refine approaches to science and practice, and develop an implementation research agenda using common measures, methods, and research principles to improve both the frequency and quality with which behavioral health treatment implementation is evaluated. SIRC’s membership growth is a testament to this identified need with more than 1000 members from 2011 to the present.ii SIRC’s primary objectives are to: (1) foster communication and collaboration across diverse groups, including implementation researchers, intermediariesi, as well as community stakeholders (SIRC uses the term “EBP champions” for these groups) – and to do so across multiple career levels (e.g., students, early career faculty, established investigators); and (2) enhance and disseminate rigorous measures and methodologies for implementing EBPs and evaluating EBP implementation efforts. These objectives are well aligned with Glasgow and colleagues’ [4] five core tenets deemed critical for advancing implementation science: collaboration, efficiency and speed, rigor and relevance, improved capacity, and cumulative knowledge. SIRC advances these objectives and tenets through in-person conferences, which bring together multidisciplinary implementation researchers and those implementing evidence-based behavioral health interventions in the community to share their work and create professional connections and collaborations
    corecore