Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data.
Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data.
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DOI：
10.1186/s12859-018-2344-6
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发表时间：
2018-09-12
影响因子：
3
通讯作者：
Boulesteix AL
中科院分区：
文献类型：
--
作者：
Klau S;Jurinovic V;Hornung R;Herold T;Boulesteix AL
在过去的几十年里，将高维组学数据纳入预测模型已经成为一个研究得很好的话题。尽管这些方法中的大多数不考虑同一数据集中可用的协变量集合中可能不同类型的变量，但存在许多这样的场景，其中变量可以被构造成不同类型的块，例如，临床、转录组和甲基化数据。到目前为止，存在一些计算密集的方法，利用这种块结构。在本文中，我们提出了优先级Lasso，一个直观和实用的分析策略，用于建立基于Lasso的预测模型，考虑到这种块结构。它需要定义数据块的优先级顺序。Lasso模型针对每个块连续计算，并且每个步骤的拟合值作为偏移包括在下一步骤的拟合中。我们在不同的环境中对急性髓系白血病（AML）数据集（包括临床变量，细胞遗传学，基因突变和表达变量）应用优先级Lasso，并将其在独立验证数据集上的性能与标准Lasso模型的性能进行比较。结果表明，priority-Lasso在预测精度方面能够与Lasso保持同步。具有较高优先级的块的变量优于具有较低优先级的块的变量，这导致易于使用和可移植的模型用于临床实践。本文的在线版本（10.1186/s12859-018-2344-6）包含补充材料，可供授权用户使用。
The inclusion of high-dimensional omics data in prediction models has become a well-studied topic in the last decades. Although most of these methods do not account for possibly different types of variables in the set of covariates available in the same dataset, there are many such scenarios where the variables can be structured in blocks of different types, e.g., clinical, transcriptomic, and methylation data. To date, there exist a few computationally intensive approaches that make use of block structures of this kind. In this paper we present priority-Lasso, an intuitive and practical analysis strategy for building prediction models based on Lasso that takes such block structures into account. It requires the definition of a priority order of blocks of data. Lasso models are calculated successively for every block and the fitted values of every step are included as an offset in the fit of the next step. We apply priority-Lasso in different settings on an acute myeloid leukemia (AML) dataset consisting of clinical variables, cytogenetics, gene mutations and expression variables, and compare its performance on an independent validation dataset to the performance of standard Lasso models. The results show that priority-Lasso is able to keep pace with Lasso in terms of prediction accuracy. Variables of blocks with higher priorities are favored over variables of blocks with lower priority, which results in easily usable and transportable models for clinical practice. The online version of this article (10.1186/s12859-018-2344-6) contains supplementary material, which is available to authorized users.
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影响因子：
11.4
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通讯作者：
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影响因子：
48
作者：
Huber W;Carey VJ;Gentleman R;Anders S;Carlson M;Carvalho BS;Bravo HC;Davis S;Gatto L;Girke T;Gottardo R;Hahne F;Hansen KD;Irizarry RA;Lawrence M;Love MI;MacDonald J;Obenchain V;Oleś AK;Pagès H;Reyes A;Shannon P;Smyth GK;Tenenbaum D;Waldron L;Morgan M
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Morgan M
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Waldron, Levi
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45.3
作者：
Büchner, T;Berdel, WE;Hiddemann, W
通讯作者：
Hiddemann, W
影响因子：
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作者：
Zhu, J;Hastie, T
通讯作者：
Hastie, T