Sparse Tropical Matrix Factorization (STMF)

STMF is a novel approach for matrix completion based on tropical matrix factorization. Please refer for the model's details to Omanović, A., Kazan, H., Oblak, P. & Curk, T. Sparse data embedding and prediction by tropical matrix factorization. BMC Bioinformatics 22, 89 (2021). https://doi.org/10.1186/s12859-021-04023-9

We used the real TCGA data in our experiments from the paper by Rappoport N. and Shamir R., and the data can be downloaded from the link. Additional preprocessing before running our experiments is provided in our paper. PAM50 data can be found on the link. BRCA subtypes are collected from CBIO portal.

import STMF as stmf
model = stmf.STMF(rank=5, criterion='iterations', max_iter=500, initialization="random_vcol")
model.fit(data)
approx = model.predict_all()

The implementation of the "distance correlation" measure is from the following link.