DNA文库筛选中的合并设计和偏倚校正
Pooling Design and Bias Correction in DNA Library Screening
摘要：
研究了基于概率方法的DNA文库筛选的群体检验方法。成组检验是从大量项目中检出少数阳性项目的一种方法，有着广泛的应用。在DNA文库筛选中，阳性项对应于具有特定DNA片段的克隆，并且需要鉴定和分离阳性克隆以用于文库的编译。在成组检测中，为了节省检测成本，将一组项目（称为池）一次性进行检测，并根据每个池的观察结果检测阳性项目。众所周知，分组设计，即合并设计，对于降低估计偏倚和实现准确检测是重要的。在概率方法中，基于后验概率挑选阳性克隆。然而，计算后验的简单方法涉及到指数级的求和，因此我们需要一个设备。循环置信传播（Loopy Belief Propagation，Loopy BP）算法是一种有效获得近似后验概率的算法。有一些工作调查的精度之间的关系的循环BP和池设计。在此基础上，我们设计了具有小后验概率估计偏差的池化设计，并证明了平衡不完全区组设计（BIBD）具有良好的性质。数值实验表明，在BIBD下进行偏差修正有助于提高估计精度。
英文摘要：
We study the group test for DNA library screening based on probabilistic approach. Group test is a method of detecting a few positive items from among a large number of items, and has wide range of applications. In DNA library screening, positive item corresponds to the clone having a specified DNA segment, and it is necessary to identify and isolate the positive clones for compiling the libraries. In the group test, a group of items, called pool, is assayed in a lump in order to save the cost of testing, and positive items are detected based on the observation from each pool. It is known that the design of grouping, that is, pooling design is important to %reduce the estimation bias and achieve accurate detection. In the probabilistic approach, positive clones are picked up based on the posterior probability. Naive methods of computing the posterior, however, involves exponentially many sums, and thus we need a device. Loopy belief propagation (loopy BP) algorithm is one of popular methods to obtain approximate posterior probability efficiently. There are some works investigating the relation between the accuracy of the loopy BP and the pooling design. Based on these works, we develop pooling design with small estimation bias of posterior probability, and we show that the balanced incomplete block design (BIBD) has nice property for our purpose. Some numerical experiments show that the bias correction under the BIBD is useful to improve the estimation accuracy.
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