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Evaluation of bias-variance trade-off for commonly used post-summarizing normalization procedures in large-scale gene expression studies.

ABSTRACT: Normalization procedures are widely used in high-throughput genomic data analyses to remove various technological noise and variations. They are known to have profound impact to the subsequent gene differential expression analysis. Although there has been some research in evaluating different normalization procedures, few attempts have been made to systematically evaluate the gene detection performances of normalization procedures from the bias-variance trade-off point of view, especially with strong gene differentiation effects and large sample size. In this paper, we conduct a thorough study to evaluate the effects of normalization procedures combined with several commonly used statistical tests and MTPs under different configurations of effect size and sample size. We conduct theoretical evaluation based on a random effect model, as well as simulation and biological data analyses to verify the results. Based on our findings, we provide some practical guidance for selecting a suitable normalization procedure under different scenarios.

SUBMITTER: Qiu X

PROVIDER: S-EPMC4062409 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Evaluation of bias-variance trade-off for commonly used post-summarizing normalization procedures in large-scale gene expression studies.

Qiu Xing X Hu Rui R Wu Zhixin Z

PloS one 20140618 6

Normalization procedures are widely used in high-throughput genomic data analyses to remove various technological noise and variations. They are known to have profound impact to the subsequent gene differential expression analysis. Although there has been some research in evaluating different normalization procedures, few attempts have been made to systematically evaluate the gene detection performances of normalization procedures from the bias-variance trade-off point of view, especially with s ...[more]

PMID: 24941114

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Project description:BackgroundTranslating research evidence into practice is challenging and, to date, there are relatively few public health interventions that have been effectively and cost-effectively implemented and delivered at scale. Theories, models and frameworks (herein termed 'frameworks') have been used in implementation science to describe, guide and explain implementation and scale-up. While economic constructs have been reported as both barriers and facilitators to effective implementation and scale-up of public health interventions, there is currently no published review of how economic constructs are considered within commonly used implementation and scale-up frameworks. This paper aimed to narratively review the economic constructs incorporated in commonly used implementation and scale-up frameworks.MethodsFrameworks for inclusion in the narrative review were identified from the literature and thematic content analysis was undertaken using a recursive deductive approach. Emergent key themes and sub-themes were identified and results were summarised narratively within each theme.ResultsTwenty-six framework publications were included in our analysis, with wide variation between frameworks in the scope and level of detail of the economic constructs included. Four key themes emerged from the data - 'resources', 'benefit', 'cost' and 'funding'. Only five frameworks incorporated all four identified key themes. Overarching lenses from which to consider key themes included 'stakeholder perspectives', 'stage in the research translation process' and 'context'. 'Resources' were most frequently considered in relation to the sub-themes of 'types of resources' (e.g. labour, time or infrastructure) and 'availability' of resources, and the opportunity for 'economies of scale'. The 'relative advantage of interventions' emerged as an interconnecting sub-theme between 'cost' and 'benefit'. 'Funding' was most often considered in relation to 'funding sources', 'availability', 'sustainability' or 'contextual impact'. The concept of 'opportunity cost' was considered in relatively few frameworks, despite being fundamental to economic theory.ConclusionsImplementation and scale-up frameworks provide a conceptual map to inform the effective and cost-effective implementation of public health interventions delivered at scale. Despite evidence of an emerging focus on the economic considerations of implementation and scale-up within some commonly used frameworks, our findings suggest that there is significant scope for further exploration of the economic constructs related to implementation and scale-up.

Project description:BACKGROUND:The potential utility of microRNA as biomarkers for early detection of cancer and other diseases is being investigated with genome-scale profiling of differentially expressed microRNA. Processes for measurement assurance are critical components of genome-scale measurements. Here, we evaluated the utility of a set of total RNA samples, designed with between-sample differences in the relative abundance of miRNAs, as process controls. RESULTS:Three pure total human RNA samples (brain, liver, and placenta) and two different mixtures of these components were evaluated as measurement assurance control samples on multiple measurement systems at multiple sites and over multiple rounds. In silico modeling of mixtures provided benchmark values for comparison with physical mixtures. Biomarker development laboratories using next-generation sequencing (NGS) or genome-scale hybridization assays participated in the study and returned data from the samples using their routine workflows. Multiplexed and single assay reverse-transcription PCR (RT-PCR) was used to confirm in silico predicted sample differences. Data visualizations and summary metrics for genome-scale miRNA profiling assessment were developed using this dataset, and a range of performance was observed. These metrics have been incorporated into an online data analysis pipeline and provide a convenient dashboard view of results from experiments following the described design. The website also serves as a repository for the accumulation of performance values providing new participants in the project an opportunity to learn what may be achievable with similar measurement processes. CONCLUSIONS:The set of reference samples used in this study provides benchmark values suitable for assessing genome-scale miRNA profiling processes. Incorporation of these metrics into an online resource allows laboratories to periodically evaluate their performance and assess any changes introduced into their measurement process.

Dataset Information

Evaluation of bias-variance trade-off for commonly used post-summarizing normalization procedures in large-scale gene expression studies.

Publications

Evaluation of bias-variance trade-off for commonly used post-summarizing normalization procedures in large-scale gene expression studies.

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