Discovering a sparse set of pairwise discriminating features in high-dimensional data.

ABSTRACT:

Motivation

Recent technological advances produce a wealth of high-dimensional descriptions of biological processes, yet extracting meaningful insight and mechanistic understanding from these data remains challenging. For example, in developmental biology, the dynamics of differentiation can now be mapped quantitatively using single-cell RNA sequencing, yet it is difficult to infer molecular regulators of developmental transitions. Here, we show that discovering informative features in the data is crucial for statistical analysis as well as making experimental predictions.

Results

We identify features based on their ability to discriminate between clusters of the data points. We define a class of problems in which linear separability of clusters is hidden in a low-dimensional

SUBMITTER: Melton S 

PROVIDER: S-EPMC8599814 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature