Joint Grid Discretization for Biological Pattern Discovery

Abstract

The complexity, dynamics, and scale of data acquired by modern biotechnology increasingly favor model-free computational methods that make minimal assumptions about underlying biological mechanisms. For example, single-cell transcriptome and proteome data have a throughput several orders more than bulk methods. Many model-free statistical methods for pattern discovery such as mutual information and chi-squared tests, however, require discrete data. Most discretization methods minimize squared errors for each variable independently, not necessarily retaining joint patterns. To address this issue, we present a joint grid discretization algorithm that preserves clusters in the original data. We evaluated this algorithm on simulated data to show its advantage over other methods in maintaining clusters as measured by the adjusted Rand index. We also show it promotes global functional patterns over independent patterns. On single-cell proteome and transcriptome of leukemia and healthy blood, joint grid discretization captured known protein-to-RNA regulatory relationships, while revealing previously unknown interactions. As such, the joint grid discretization is applicable as a data transformation step in associative, functional, and causal inference of molecular interactions fundamental to systems biology. The developed software is publicly available at https://cran.r-project.org/package=GridOnClusters