Ratio-based quantitative multiomics profiling using universal reference materials empowers data integration
Author:
Zheng YuantingORCID, Liu YaqingORCID, Yang JingchengORCID, Dong Lianhua, Zhang Rui, Tian ShaORCID, Yu YingORCID, Ren LuyaoORCID, Hou WanwanORCID, Zhu Feng, Mai Yuanbang, Han Jinxiong, Zhang Lijun, Jiang Hui, Lin Ling, Lou Jingwei, Li Ruiqiang, Lin Jingchao, Liu Huafen, Kong Ziqing, Wang Depeng, Dai Fangping, Bao Ding, Cao ZehuiORCID, Chen QiaochuORCID, Chen QingwangORCID, Chen XingdongORCID, Gao YuechenORCID, Jiang HeORCID, Li Bin, Li Bingying, Li Jingjing, Liu Ruimei, Qing Tao, Shang ErfeiORCID, Shang Jun, Sun Shanyue, Wang Haiyan, Wang Xiaolin, Zhang NaixinORCID, Zhang Peipei, Zhang RuolanORCID, Zhu Sibo, Scherer AndreasORCID, Wang Jiucun, Wang Jing, Xu Joshua, Hong HuixiaoORCID, Xiao WenmingORCID, Liang Xiaozhen, Jin Li, Tong WeidaORCID, Ding Chen, Li Jinming, Fang Xiang, Shi LemingORCID,
Abstract
AbstractMultiomics 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.
Publisher
Cold Spring Harbor Laboratory
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