Non-negative tensor factorization workflow for time series biomedical data

被引：0

作者：

Tsuyuzaki, Koki ^{[1
,2
]}

Yoshida, Naoki ^{[3
]}

Ishikawa, Tetsuo ^{[3
,4
,5
]}

Goshima, Yuki ^{[4
]}

Kawakami, Eiryo ^{[3
,4
,6
,7
]}

机构：

[1] RIKEN Ctr Biosyst Dynam Res, Lab Bioinformat Res, Wako, Saitama 3510198, Japan

[2] Japan Sci & Technol Agcy, PRESTO, 7 Gobancho,Chiyoda ku, Tokyo 1020075, Japan

[3] Chiba Univ, Grad Sch Med, Dept Artificial Intelligence Med, Chiba 2608670, Japan

[4] RIKEN Informat R&D & Strategy Headquarters, Adv Data Sci Project ADSP, Yokohama, Kanagawa 2300045, Japan

[5] Keio Univ, Dept Extended Intelligence Med, Ishii Ishibashi Lab, Sch Med, Shinjuku Ku, Tokyo 1608582, Japan

[6] Japanese Fdn Canc Res JFCR, NEXT Ganken Program, Tokyo 1358550, Japan

[7] Chiba Univ, Inst Adv Acad Res IAAR, Chiba 2608670, Japan

来源：

STAR PROTOCOLS | 2023年 / 4卷 / 03期

基金：

日本科学技术振兴机构;

关键词：

Bioinformatics; Computer sciences; Health Sciences;

D O I：

10.1016/j.xpro.2023.102318

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Non-negative tensor factorization (NTF) enables the extraction of a small number of latent components from high-dimensional biomedical data. However, NTF requires many steps, which is a hurdle to implementation. Here, we provide a protocol for TensorLyCV, an easy to run and reproducible NTF analysis pipeline using Snake make workflow management system and Docker container. Using vaccine adverse reaction data as an example, we describe steps for data processing, tensor decomposition, optimal rank parameter estimation, and visualization of factor matrices. For complete details on the use and execution of this protocol, please refer to Kei Ikeda et al.1

引用

页数：16

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