Soft windowing application to improve analysis of high-throughput phenotyping data
Soft windowing application to improve analysis of high-throughput phenotyping data
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DOI：
10.1093/bioinformatics/btz744
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发表时间：
2020-03-01
期刊：
影响因子：
5.8
通讯作者：
Meehan, Terrence F.
中科院分区：
文献类型：
--
作者：
Haselimashhadi, Hamed;Mason, Jeremy C.;Meehan, Terrence F.
动机：高通量表型项目从小的治疗组和大的对照组中产生复杂的数据，这增加了分析的功效，但随着时间的推移引入了变化。需要一种方法来uttering一组时间上的本地控制，最大限度地提高分析能力，同时最大限度地减少噪声从未指定的环境factors.Results：在这里，我们介绍“软窗口”，选择一个窗口的时间，包括最合适的控制分析的方法。使用来自国际小鼠表型鉴定协会（IMPC）的表型数据，应用自适应窗口，使得在突变体附近收集的对照数据被分配最大权重，而较早或较晚收集的数据具有较小权重。我们将这种方法应用于IMPC数据，并将结果与从标准非窗口方法获得的结果进行比较。验证是使用一种重新验证的方法进行的，我们证明了从250万次分析中减少了10%的假阳性。我们将该方法应用于我们的生产分析管道，通过比较突变体与对照数据来建立基因型-表型关联。我们报告了一组2082个突变小鼠系的显著P值增加30%，以及通过表型重叠分别与软窗和非窗方法与106和99个疾病模型的联系。我们的方法是可推广的，可以受益于大规模的人类表型项目，如英国生物银行和我们所有的资源。
Motivation: High-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximizes analytic power while minimizing noise from unspecified environmental factors.Results: Here we introduce 'soft windowing', a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype-phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant P-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft-windowed and non-windowed approaches, respectively, from a set of 2082 mutant mouse lines. Our method is generalizable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources.