Less is more: relative rank is more informative than absolute abundance for compositional NGS data

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Veröffentlicht in:Briefings in Functional Genomics vol. 24 (2025)
1. Verfasser: Zheng, Xubin
Weitere Verfasser: Jin, Nana, Wu, Qiong, Zhang, Ning, Wu, Haonan, Wang, Yuanhao, Luo, Rui, Liu, Tao, Ding, Wanfu, Geng, Qingshan, Cheng, Lixin
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Oxford University Press
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100 1 |a Zheng, Xubin  |u https://orcid.org/0000-0003-2322-857X xbzheng@gbu.edu.cn Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen Clinical Research Center for Geriatrics, Shenzhen People’s Hospital, Luohu District, Shenzhen 518020, China; Health Data Science Center, Shenzhen People's Hospital (First Affiliated Hospital of Southern University of Science and Technology), Luohu District, Shenzhen 518020, China; School of Computing and Information Technology, Great Bay University, Dongguan 523000, Guangdong, China 
245 1 |a Less is more: relative rank is more informative than absolute abundance for compositional NGS data 
260 |b Oxford University Press  |c 2025 
513 |a Journal Article 
520 3 |a High-throughput gene expression data have been extensively generated and utilized in biological mechanism investigations, biomarker detection, disease diagnosis and prognosis. These applications encompass not only bulk transcriptome, but also single cell RNA-seq data. However, extracting reliable biological information from transcriptome data remains challenging due to the constrains of Compositional Data Analysis. Current data preprocessing methods, including dataset normalization and batch effect correction, are insufficient to address these issues and improve data quality for downstream analysis. Alternatively, qualification methods focusing on the relative order of gene expression (ROGER) are more informative than the quantification methods that rely on gene expression abundance. The Pairwise Analysis of Gene expression method is an enhancement of ROGER, designed for data integration in either sample space or feature space. In this review, we summarize the methods applied to transcriptome data analysis and discuss their potentials in predicting clinical outcomes. 
653 |a Transcriptomes 
653 |a Data processing 
653 |a Data analysis 
653 |a Biomarkers 
653 |a Gene expression 
653 |a Data integration 
653 |a Research centers 
653 |a Hospitals 
653 |a Algorithms 
653 |a Genomics 
653 |a Geriatrics 
653 |a Environmental 
700 1 |a Jin, Nana  |u jinnana7926@hotmail.com Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen Clinical Research Center for Geriatrics, Shenzhen People’s Hospital, Luohu District, Shenzhen 518020, China; Health Data Science Center, Shenzhen People's Hospital (First Affiliated Hospital of Southern University of Science and Technology), Luohu District, Shenzhen 518020, China 
700 1 |a Wu, Qiong  |u wuqiong.2002@gmail.com School of Basic Medicine, North Sichuan Medical College, Nanchong 637000, Sichuan, China 
700 1 |a Zhang, Ning  |u ningzhangicu@outlook.com Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen Clinical Research Center for Geriatrics, Shenzhen People’s Hospital, Luohu District, Shenzhen 518020, China; Health Data Science Center, Shenzhen People's Hospital (First Affiliated Hospital of Southern University of Science and Technology), Luohu District, Shenzhen 518020, China 
700 1 |a Wu, Haonan  |u haonan_wu98@163.com Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen Clinical Research Center for Geriatrics, Shenzhen People’s Hospital, Luohu District, Shenzhen 518020, China; Health Data Science Center, Shenzhen People's Hospital (First Affiliated Hospital of Southern University of Science and Technology), Luohu District, Shenzhen 518020, China 
700 1 |a Wang, Yuanhao  |u 12233056@mail.sustech.edu.cn Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen Clinical Research Center for Geriatrics, Shenzhen People’s Hospital, Luohu District, Shenzhen 518020, China; Health Data Science Center, Shenzhen People's Hospital (First Affiliated Hospital of Southern University of Science and Technology), Luohu District, Shenzhen 518020, China 
700 1 |a Luo, Rui  |u ruiluo@cityu.edu.hk Department of Systems Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR 
700 1 |a Liu, Tao  |u liutao@idea.edu.cn International Digital Economy Academy (IDEA), Futian District, Shenzhen 518020, China 
700 1 |a Ding, Wanfu  |u dingwanfu@foxmail.com Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen Clinical Research Center for Geriatrics, Shenzhen People’s Hospital, Luohu District, Shenzhen 518020, China 
700 1 |a Geng, Qingshan  |u gengqingshan@szhospital.com Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen Clinical Research Center for Geriatrics, Shenzhen People’s Hospital, Luohu District, Shenzhen 518020, China 
700 1 |a Cheng, Lixin  |u https://orcid.org/0000-0002-9427-383X easonlcheng@gmail.com Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen Clinical Research Center for Geriatrics, Shenzhen People’s Hospital, Luohu District, Shenzhen 518020, China; Health Data Science Center, Shenzhen People's Hospital (First Affiliated Hospital of Southern University of Science and Technology), Luohu District, Shenzhen 518020, China 
773 0 |t Briefings in Functional Genomics  |g vol. 24 (2025) 
786 0 |d ProQuest  |t Health & Medical Collection 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3268183202/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3268183202/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch