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

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초록

TENET reconstructs gene regulatory networks from single-cell RNA sequencing (scRNAseq) data using the transfer entropy (TE), 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 TE based on array computing. Compared to TENET, FastTENET achieves up to 973x performance improvement. Availability and implementation: FastTENET is available on GitHub at https://github.com/cxinsys/fasttenet. [GRAPHICS] .

제목
FastTENET: an accelerated TENET algorithm based on manycore computing in Python
저자
Sung, Rakbin; Kim, Hyeonkyu; Kim, Junil; Lee, Daewon
DOI
10.1093/bioinformatics/btae699
발행일
2024-12
유형
Article
저널명
Bioinformatics
권
40
호
12