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Optimising experimental design for high-throughput phenotyping in mice: a case study.

ABSTRACT: To further the functional annotation of the mammalian genome, the Sanger Mouse Genetics Programme aims to generate and characterise knockout mice in a high-throughput manner. Annually, approximately 200 lines of knockout mice will be characterised using a standardised battery of phenotyping tests covering key disease indications ranging from obesity to sensory acuity. From these findings secondary centres will select putative mutants of interest for more in-depth, confirmatory experiments. Optimising experimental design and data analysis is essential to maximise output using the resources with greatest efficiency, thereby attaining our biological objective of understanding the role of genes in normal development and disease. This study uses the example of the noninvasive blood pressure test to demonstrate how statistical investigation is important for generating meaningful, reliable results and assessing the design for the defined research objectives. The analysis adjusts for the multiple-testing problem by applying the false discovery rate, which controls the number of false calls within those highlighted as significant. A variance analysis finds that the variation between mice dominates this assay. These variance measures were used to examine the interplay between days, readings, and number of mice on power, the ability to detect change. If an experiment is underpowered, we cannot conclude whether failure to detect a biological difference arises from low power or lack of a distinct phenotype, hence the mice are subjected to testing without gain. Consequently, in confirmatory studies, a power analysis along with the 3Rs can provide justification to increase the number of mice used.

SUBMITTER: Karp NA

PROVIDER: S-EPMC2974211 | biostudies-literature | 2010 Oct

REPOSITORIES: biostudies-literature

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Optimising experimental design for high-throughput phenotyping in mice: a case study.

Karp Natasha A NA Baker Lauren A LA Gerdin Anna-Karin B AK Adams Niels C NC Ramírez-Solis Ramiro R White Jacqueline K JK

Mammalian genome : official journal of the International Mammalian Genome Society 20100827 9-10

To further the functional annotation of the mammalian genome, the Sanger Mouse Genetics Programme aims to generate and characterise knockout mice in a high-throughput manner. Annually, approximately 200 lines of knockout mice will be characterised using a standardised battery of phenotyping tests covering key disease indications ranging from obesity to sensory acuity. From these findings secondary centres will select putative mutants of interest for more in-depth, confirmatory experiments. Optim ...[more]

PMID: 20799038

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Project description:ObjectiveHigh-throughput electronic phenotyping algorithms can accelerate translational research using data from electronic health record (EHR) systems. The temporal information buried in EHRs is often underutilized in developing computational phenotypic definitions. This study aims to develop a high-throughput phenotyping method, leveraging temporal sequential patterns from EHRs.Materials and methodsWe develop a representation mining algorithm to extract 5 classes of representations from EHR diagnosis and medication records: the aggregated vector of the records (aggregated vector representation), the standard sequential patterns (sequential pattern mining), the transitive sequential patterns (transitive sequential pattern mining), and 2 hybrid classes. Using EHR data on 10 phenotypes from the Mass General Brigham Biobank, we train and validate phenotyping algorithms.ResultsPhenotyping with temporal sequences resulted in a superior classification performance across all 10 phenotypes compared with the standard representations in electronic phenotyping. The high-throughput algorithm's classification performance was superior or similar to the performance of previously published electronic phenotyping algorithms. We characterize and evaluate the top transitive sequences of diagnosis records paired with the records of risk factors, symptoms, complications, medications, or vaccinations.DiscussionThe proposed high-throughput phenotyping approach enables seamless discovery of sequential record combinations that may be difficult to assume from raw EHR data. Transitive sequences offer more accurate characterization of the phenotype, compared with its individual components, and reflect the actual lived experiences of the patients with that particular disease.ConclusionSequential data representations provide a precise mechanism for incorporating raw EHR records into downstream machine learning. Our approach starts with user interpretability and works backward to the technology.

Project description:Detailed and standardized protocols for plant cultivation in environmentally controlled conditions are an essential prerequisite to conduct reproducible experiments with precisely defined treatments. Setting up appropriate and well defined experimental procedures is thus crucial for the generation of solid evidence and indispensable for successful plant research. Non-invasive and high throughput (HT) phenotyping technologies offer the opportunity to monitor and quantify performance dynamics of several hundreds of plants at a time. Compared to small scale plant cultivations, HT systems have much higher demands, from a conceptual and a logistic point of view, on experimental design, as well as the actual plant cultivation conditions, and the image analysis and statistical methods for data evaluation. Furthermore, cultivation conditions need to be designed that elicit plant performance characteristics corresponding to those under natural conditions. This manuscript describes critical steps in the optimization of procedures for HT plant phenotyping systems. Starting with the model plant Arabidopsis, HT-compatible methods were tested, and optimized with regard to growth substrate, soil coverage, watering regime, experimental design (considering environmental inhomogeneities) in automated plant cultivation and imaging systems. As revealed by metabolite profiling, plant movement did not affect the plants' physiological status. Based on these results, procedures for maize HT cultivation and monitoring were established. Variation of maize vegetative growth in the HT phenotyping system did match well with that observed in the field. The presented results outline important issues to be considered in the design of HT phenotyping experiments for model and crop plants. It thereby provides guidelines for the setup of HT experimental procedures, which are required for the generation of reliable and reproducible data of phenotypic variation for a broad range of applications.

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Optimising experimental design for high-throughput phenotyping in mice: a case study.

Publications

Optimising experimental design for high-throughput phenotyping in mice: a case study.

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