The Quartet Data Portal: integration of community-wide resources for multiomics quality control

Yang, Jingcheng; Liu, Yaqing; Shang, Jun; Chen, Qiaochu; Chen, Qingwang; Ren, Luyao; Zhang, Naixin; Yu, Ying; Li, Zhihui; Song, Yueqiang; Yang, Song; Scherer, Andreas; Tong, Weida; Hong, Huasheng; Shi, Leming; Xiao, Wenming; Zheng, Yuanting

doi:10.1101/2022.09.26.507202

Cited by 11 publications

(16 citation statements)

References 36 publications

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“…Precision, recall, and F1-score were recommended for qualitative omics (small variants and structural variants), and RMSE at the ratio level (scaling to D6) of the feature expressions and the differential expressions between groups (D5/F7, F7/M8, and M8/D5) were recommended for quantitative omics (DNA methylation, transcriptomics, proteomics, and metabolomics). In addition, more comprehensive proficiency tests or inter-lab comparisons can be performed by obtaining the relative quality ranking among the cumulative datasets within the Quartet Data Portal 71 .…”

Section: Resultsmentioning

confidence: 99%

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

Zheng

Liu

Yang

et al. 2022

Preprint

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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.

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Section: Resultsmentioning

confidence: 99%

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

Zheng

Liu

Yang

et al. 2022

Preprint

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“…1b ). As described in accompanying papers 36-38, 40, 41 , a set of DNA, RNA, protein, and metabolite reference materials was established simultaneously from four immortalized B-lymphoblastoid cell lines (LCLs) derived from a Quartet family including monozygotic twin daughters (D5 and D6) and their father (F7) and mother (M8). Reference materials were then distributed to multiple labs for generating multiomic data.…”

Section: Resultsmentioning

confidence: 99%

Correcting batch effects in large-scale multiomic studies using a reference-material-based ratio method

Zhang

Mai

et al. 2022

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Batch effects are notorious technical variations that are common in multiomic data and may result in misleading outcomes. With the era of big data, tackling batch effects in multiomic integration is urgently needed. As part of the Quartet Project for quality control and data integration of multiomic profiling, we comprehensively assess the performances of seven batch-effect correction algorithms (BECAs) for mitigating the negative impact of batch effects in multiomic datasets, including transcriptomics, proteomics, and metabolomics. Performances are evaluated based on accuracy of identifying differentially expressed features, robustness of predictive models, and the ability of accurately clustering cross-batch samples into their biological sample groups. Ratio-based method is more effective and widely applicable than others, especially in cases when batch effects are highly confounded with biological factors of interests. We further provide practical guidelines for the implementation of ratio-based method using universal reference materials profiled with study samples. Our findings show the promise for eliminating batch effects and enhancing data integration in increasingly large-scale, cross-batch multiomic studies.

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“…To facilitate the adoption of multiomic reference materials, reference datasets, and quality metrics from the Quartet project, we developed a Quartet Data Portal (http://chinese-quartet.org/) for access to the Quartet resources and enhancing quality consciousness of the community, as described in an accompanying paper 52 . Researchers can request the multiomic reference materials, multi-level datasets, and reference datasets from the data portal.…”

Section: Discussionmentioning

confidence: 99%

Quartet RNA reference materials and ratio-based reference datasets for reliable transcriptomic profiling

Hou

Wang

et al. 2022

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As an indispensable tool for transcriptome-wide analysis of differential gene expression, RNA sequencing (RNAseq) has demonstrated great potential in clinical applications. However, the lack of multi-group RNA reference materials of biological relevance and the corresponding reference datasets for assessing the reliability of RNAseq hampers its wide clinical applications wherein the underlying biological differences among study groups are often small. As part of the Quartet Project for quality control and data integration of multiomic profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets using multi-batch RNAseq datasets, providing "ground truth" for benchmarking. Moreover, Quartet-sample-based quality metrics were developed for assessing reliability of RNAseq technology in terms of intra-batch proficiency and cross-batch reproducibility. The small intrinsic biological differences among the Quartet samples enable sensitive assessment of performance of transcriptomic measurements. The Quartet RNA reference materials combined with the reference datasets can be served as unique resources for assessing data quality and improving reliability of transcriptomic profiling.

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The Quartet Data Portal: integration of community-wide resources for multiomics quality control

Cited by 11 publications

References 36 publications

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

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

Correcting batch effects in large-scale multiomic studies using a reference-material-based ratio method

Quartet RNA reference materials and ratio-based reference datasets for reliable transcriptomic profiling

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