Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials

• Published in: Nature biotechnology Vol. 42; no. 7; pp. 1133 - 1149
• Main Authors: Zheng, Yuanting, Liu, Yaqing, Yang, Jingcheng, Dong, Lianhua, Zhang, Rui, Tian, Sha, Yu, Ying, Ren, Luyao, Hou, Wanwan, 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, Zehui, Chen, Qiaochu, Chen, Qingwang, Chen, Xingdong, Gao, Yuechen, Jiang, He, Li, Bin, Li, Bingying, Li, Jingjing, Liu, Ruimei, Qing, Tao, Shang, Erfei, Shang, Jun, Sun, Shanyue, Wang, Haiyan, Wang, Xiaolin, Zhang, Naixin, Zhang, Peipei, Zhang, Ruolan, Zhu, Sibo, Scherer, Andreas, Wang, Jiucun, Wang, Jing, Huo, Yinbo, Liu, Gang, Cao, Chengming, Shao, Li, Xu, Joshua, Hong, Huixiao, Xiao, Wenming, Liang, Xiaozhen, Lu, Daru, Jin, Li, Tong, Weida, Ding, Chen, Li, Jinming, Fang, Xiang, Shi, Leming
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group 01.07.2024; Nature Publishing Group US
• Subjects: Biological analysis; Cell lines; Data integration; Deoxyribonucleic acid; DNA; DNA fingerprinting; Families & family life; Information flow; Integration; Metabolites; Proteins; Proteomes; Reference materials; Ribonucleic acid; RNA; Transcriptomes
• ISSN: 1087-0156; 1546-1696; 1546-1696
• DOI: 10.1038/s41587-023-01934-1

Abstract Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free ‘absolute’ feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials.