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Ratio-based quantitative multiomics profiling using universal reference materials empowers data integration

Authors :
Yuanting Zheng
Yaqing Liu
Jingcheng Yang
Lianhua Dong
Rui Zhang
Sha Tian
Ying Yu
Luyao Ren
Wanwan Hou
Feng Zhu
Yuanbang Mai
Jinxiong Han
Lijun Zhang
Hui Jiang
Ling Lin
Jingwei Lou
Ruiqiang Li
Jingchao Lin
Huafen Liu
Ziqing Kong
Depeng Wang
Fangping Dai
Ding Bao
Zehui Cao
Qiaochu Chen
Qingwang Chen
Xingdong Chen
Yuechen Gao
He Jiang
Bin Li
Bingying Li
Jingjing Li
Ruimei Liu
Tao Qing
Erfei Shang
Jun Shang
Shanyue Sun
Haiyan Wang
Xiaolin Wang
Naixin Zhang
Peipei Zhang
Ruolan Zhang
Sibo Zhu
Andreas Scherer
Jiucun Wang
Jing Wang
Joshua Xu
Huixiao Hong
Wenming Xiao
Xiaozhen Liang
Li Jin
Weida Tong
Chen Ding
Jinming Li
Xiang Fang
Leming Shi
Publication Year :
2022
Publisher :
Research Square Platform LLC, 2022.

Abstract

Multiomics profiling is a powerful tool to characterize the same samples with complementary features orchestrating the genome, epigenome, transcriptome, proteome, and metabolome. However, the lack of ground truth hampers the objective assessment of and subsequent choice from a plethora of measurement and computational methods aiming to integrate diverse and often enigmatically incomparable omics datasets. Here we establish and characterize the first suites of publicly available multiomics reference materials of matched DNA, RNA, proteins, and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters, providing built-in truth defined by family relationship and the central dogma. We demonstrate that the "ratio"-based omics profiling data, i.e., by scaling the absolute feature values of a study sample relative to those of a concurrently measured universal reference sample, were inherently much more reproducible and comparable across batches, labs, platforms, and omics types, thus empower the horizontal (within-omics) and vertical (cross-omics) data integration in multiomics studies. Our study identifies "absolute" feature quantitation as the root cause of irreproducibility in multiomics measurement and data integration, and urges a paradigm shift from "absolute" to "ratio"-based multiomics profiling with universal reference materials.

Details

Database :
OpenAIRE
Accession number :
edsair.doi...........5d4252b45ccd70ab4217721ab998eada