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

Summary 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...

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Published inBioinformatics (Oxford, England) Vol. 40; no. 12
Main Authors Sung, Rakbin, Kim, Hyeonkyu, Kim, Junil, Lee, Daewon
Format Journal Article
LanguageEnglish
Published England Oxford University Press 28.11.2024
Oxford Publishing Limited (England)
Subjects
Algorithms
Applications Note
Availability
Computation
Computational Biology - methods
Entropy
Gene Regulatory Networks
Gene sequencing
Sequence Analysis, RNA - methods
Single-Cell Analysis - methods
Software
Online AccessGet full text
ISSN1367-4811
1367-4803
1367-4811
DOI10.1093/bioinformatics/btae699

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Summary:Summary 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 973× performance improvement. Availability and implementation FastTENET is available on GitHub at https://github.com/cxinsys/fasttenet. Graphical Abstract Graphical Abstract
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Rakbin Sung and Hyeonkyu Kim equal contribution.
ISSN:1367-4811
1367-4803
1367-4811
DOI:10.1093/bioinformatics/btae699