FastTENET: an accelerated TENET algorithm based on manycore computing in Python

초록

CONCLUSIONS: TENET reconstructs gene regulatory networks from single-cell RNA sequencing (scRNAseq) data using the transfer entropy, and works successfully on a variety of scRNAseq data. However, TENET is limited by its long computation time for large datasets. To address this limitation, we propose FastTENET, an array-computing version of TENET algorithm optimized for acceleration on manycore processors such as GPUs. FastTENET counts the unique patterns of joint events to compute the transfer entropy based on array computing. Compared to TENET, FastTENET achieves up to 973× performance improvement. METHODS: FastTENET is available on GitHub at https://github.com/cxinsys/fasttenet. BACKGROUND: Supplementary data is available at Bioinformatics online. © The Author(s) 2024. Published by Oxford University Press.