Efficiently quantifying dependence in massive scientific datasets using InterDependence Scores.

ABSTRACT: Large-scale scientific datasets today contain tens of thousands of random variables across millions of samples (for example, the RNA expression levels of 20,000 protein-coding genes across 30 million single cells). Being able to quantify dependencies between these variables would help us discover novel relationships between variables of interest. Simple measures of dependence, such as Pearson correlation, are fast to compute, but limited in that they are designed to detect linear relationships between variables. Complex measures are known with the ability to detect any kind of dependence, but they do not readily scale to many modern datasets of interest. We introduce the InterDependence Score (IDS), a scalable measure of dependence that captures linear and various nonlinear dependencies be

SUBMITTER: Radhakrishnan A 

PROVIDER: S-EPMC12403096 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature