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. Real-world application demonstrated that BEM is applicable to all kinds of transcriptomic data, including bulk RNA-seq, single-cell RNA-seq and spatial transcriptomic datasets. Given appropriate binarization, BEM was able to extract coregulation patterns consistent with disease subtypes, cell types or spatial anatomy.
Availability and implementation: Python source code of BEM is available on https://github.com/LifanLiang/EM_BMF.
Supplementary information: Supplementary data are available at Bioinformatics online.
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