Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control

Abstract

Direct-infusion mass spectrometry (DIMS) metabolomics is an important approach for characterising molecular responses of organisms to disease, drugs and the environment. Increasingly large-scale metabolomics studies are being conducted, necessitating improvements in both bioanalytical and computational workflows to maintain data quality. This dataset represents a systematic evaluation of the reproducibility of a multi-batch DIMS metabolomics study of cardiac tissue extracts. It comprises of twenty biological samples (cow vs. sheep) that were analysed repeatedly, in 8 batches across 7 days, together with a concurrent set of quality control (QC) samples. Data are presented from each step of the workflow and are available in MetaboLights. The strength of the dataset is that intra- and inter-batch variation can be corrected using QC spectra and the quality of this correction assessed independently using the repeatedly-measured biological samples. Originally designed to test the efficacy of a batch-correction algorithm, it will enable others to evaluate novel data processing algorithms. Furthermore, this dataset serves as a benchmark for DIMS metabolomics, derived using best-practice workflows and rigorous quality assessment.

Missing value imputation strategies for metabolomics data

“…Missing values due to reprocessing errors can be reduced by methods from simply combining duplicate measurements in the peak picking process and taking an average of each [6] to applying a target ion search based on a predefined library such as the previously described recursive analysis [1]. For those missing values that still occur after taking actions such as these, they can be dealt with by changing data-reprocessing parameters or manual assignation of values from raw data [7,8], or imputed by zero [9], median [5], minimum value [10], ½ minimum value [11], arithmetic mean of all [11,12] or some of the more related samples [3], k-means nearest neighbor (kNN) [2,13] etc. There are a range of tools such as SIMCA-P [14], XCMS [15], MeltDB [16], COVAIN [11], and MetaboAnalyst [17] that include a step to deal with missing values; however in many cases, they impute missing values in a way that the data matrix after imputation cannot be visualized or exported with missing values imputed and therefore statistical analyses are confined to those tools.…”

“…For this reason, different proportions of missing data have been studied in the present research. Filtering is usually the first method to deal with missing values, whereby samples [2,3] or variables [4,5] are removed when they contain a high proportion of missing values. One common strategy, and the one employed in this research, is to filter variables based on exhibiting presence in at least 75% of samples in at least one of n groups.…”

Statistical Methods for Handling Unwanted Variation in Metabolomics Data

“…8 The overall removal of unwanted variation (referred to as normalization ) has been considered a gray area in which there is a distinct need to develop a greater understanding of when, why, and how 9 in order to achieve optimal biological outcomes. It has also been shown that the statistical results such as those obtained by identifying differentially abundant metabolites can vary depending on the method chosen for removing unwanted variation.…”

Application of Passive Sampling to Characterise the Fish Exometabolome

“…Given the importance of characterising the fish exometabolome, for studies of fish toxicology, nutrition, health and welfare, here we sought for the first time to evaluate the capability of Chemcatcher ® passive samplers to capture a broad spectrum of endogenous metabolites excreted by fish, and then to measure the exometabolome using a non-targeted direct infusion mass spectrometry based metabolomics approach [32,33,34]. Given that endogenous metabolites vary considerably in polarity we tested and contrasted the capabilities of two receiving phases, C18 and SDB-RPS Empore™ disks, to capture non-polar and polar organics, respectively.…”