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HRUV: Hierarchical approach to removal of unwanted variation for large-scale metabolomics data

ABSTRACT: Liquid chromatography-mass spectrometry-based metabolomics studies are increasingly applied to large population cohorts, which run for several weeks or even years in data acquisition. This inevitably introduces unwanted intra- and inter-batch variations over time that can overshadow true biological signals and thus hinder potential biological discoveries. To date, normalisation approaches have struggled to mitigate the variability introduced by technical factors whilst preserving biological variance, especially for protracted acquisitions. Here, we propose a study design framework with an arrangement for embedding biological sample replicates to quantify variance within and between batches and a novel workflow that uses these replicates to remove unwanted variation in a hierarchical (hRUV) manner. We use this design to produce a dataset of more than 1,000 human plasma samples run over an extended period of time. We demonstrate significant improvement of hRUV over existing methods in preserving biological signals whilst removing unwanted variation for large scale metabolomics studies. Our novel tools not only provide a strategy for large scale data normalization, but also provides guidance on the design strategy for large omics studies.

INSTRUMENT(S): Liquid Chromatography MS - positive - hilic

SUBMITTER: Taiyun Kim

PROVIDER: MTBLS2483 | MetaboLights | 2021-07-05

REPOSITORIES: MetaboLights

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			Action	DRS
	MTBLS2483	Other
	FILES	Other
	a_MTBLS2483_LC-MS_positive_hilic_metabolite_profiling.txt	Txt
	i_Investigation.txt	Txt
	m_MTBLS2483_LC-MS_positive_hilic_metabolite_profiling_v2_maf.tsv	Tabular

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Project description:BACKGROUND: Dendritic cells (DC) play a central role in primary immune responses and become potent stimulators of the adaptive immune response after undergoing the critical process of maturation. Understanding the dynamics of DC maturation would provide key insights into this important process. Time course microarray experiments can provide unique insights into DC maturation dynamics. Replicate experiments are necessary to address the issues of experimental and biological variability. Statistical methods and averaging are often used to identify significant signals. Here a novel strategy for filtering of replicate time course microarray data, which identifies consistent signals between the replicates, is presented and applied to a DC time course microarray experiment. RESULTS: The temporal dynamics of DC maturation were studied by stimulating DC with poly(I:C) and following gene expression at 5 time points from 1 to 24 hours. The novel filtering strategy uses standard statistical and fold change techniques, along with the consistency of replicate temporal profiles, to identify those differentially expressed genes that were consistent in two biological replicate experiments. To address the issue of cluster reproducibility a consensus clustering method, which identifies clusters of genes whose expression varies consistently between replicates, was also developed and applied. Analysis of the resulting clusters revealed many known and novel characteristics of DC maturation, such as the up-regulation of specific immune response pathways. Intriguingly, more genes were down-regulated than up-regulated. Results identify a more comprehensive program of down-regulation, including many genes involved in protein synthesis, metabolism, and housekeeping needed for maintenance of cellular integrity and metabolism. CONCLUSIONS: The new filtering strategy emphasizes the importance of consistent and reproducible results when analyzing microarray data and utilizes consistency between replicate experiments as a criterion in both feature selection and clustering, without averaging or otherwise combining replicate data. Observation of a significant down-regulation program during DC maturation indicates that DC are preparing for cell death and provides a path to better understand the process. This new filtering strategy can be adapted for use in analyzing other large-scale time course data sets with replicates.

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HRUV: Hierarchical approach to removal of unwanted variation for large-scale metabolomics data

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