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

Bioinformatics (Oxford, England) Pub Date : 2024-11-21 DOI:10.1093/bioinformatics/btae699

Rakbin Sung, Hyeonkyu Kim, Junil Kim, Daewon Lee

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Abstract

Summary: 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.

Availability and implementation: FastTENET is available on GitHub at https://github.com/cxinsys/fasttenet.

Supplementary information: Supplementary data is available at Bioinformatics online.

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FastTENET：基于 Python 多核计算的 TENET 加速算法。

摘要：TENET 利用转移熵从单细胞 RNA 测序（scRNAseq）数据中重建基因调控网络，并在各种 scRNAseq 数据上成功运行。然而，TENET 受限于对大型数据集的计算时间过长。为了解决这一限制，我们提出了 FastTENET，这是 TENET 算法的阵列计算版本，经过优化，可在 GPU 等多核处理器上加速。FastTENET 基于阵列计算，计算联合事件的独特模式，从而计算转移熵。与 TENET 相比，FastTENET 实现了高达 973 倍的性能提升：FastTENET可在GitHub上获取：https://github.com/cxinsys/fasttenet.Supplementary：补充数据可在 Bioinformatics online 上获取。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Bioinformatics (Oxford, England)

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