MREC：一个快速和通用的框架，用于对齐和匹配点云与单细胞分子数据的应用
MREC: a fast and versatile framework for aligning and matching point clouds with applications to single cell molecular data
摘要：
比较和对齐大型数据集是一个普遍存在的问题，发生在许多不同的知识领域。我们介绍和研究MREC，递归分解算法计算数据集之间的匹配。基本思想是对数据进行分区，匹配分区，然后递归地匹配每对已识别分区内的点。匹配本身是使用黑盒匹配过程完成的，这些过程太昂贵，无法在整个数据集上运行。使用匹配质量的绝对度量，该框架支持对包括划分过程和匹配算法在内的参数进行优化。通过设计，MREC可以应用于非常大的数据集。我们分析的程序来描述，当我们可以期望它工作良好，并证明其灵活性和权力，通过将其应用到单细胞分子数据分析中出现的一些对齐问题。
英文摘要：
Comparing and aligning large datasets is a pervasive problem occurring across many different knowledge domains. We introduce and study MREC, a recursive decomposition algorithm for computing matchings between data sets. The basic idea is to partition the data, match the partitions, and then recursively match the points within each pair of identified partitions. The matching itself is done using black box matching procedures that are too expensive to run on the entire data set. Using an absolute measure of the quality of a matching, the framework supports optimization over parameters including partitioning procedures and matching algorithms. By design, MREC can be applied to extremely large data sets. We analyze the procedure to describe when we can expect it to work well and demonstrate its flexibility and power by applying it to a number of alignment problems arising in the analysis of single cell molecular data.
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