BEM: Mining Coregulation Patterns in Transcriptomics via Boolean Matrix Factorization.

ABSTRACT:

Motivation

The matrix factorization is an important way to analyze coregulation patterns in transcriptomic data, which can reveal the tumor signal perturbation status and subtype classification. However, current matrix factorization methods do not provide clear bicluster structure. Furthermore, these algorithms are based on the assumption of linear combination, which may not be sufficient to capture the coregulation patterns.

Results

We presented a new algorithm for Boolean matrix factorization (BMF) via expectation maximization (BEM). BEM is more aligned with the molecular mechanism of transcriptomic coregulation and can scale to matrix with over 100 million data points. Synthetic experiments showed that BEM outperformed other BMF methods in terms of reconstruction error. R

SUBMITTER: Liang L 

PROVIDER: S-EPMC7332573 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature