metaboprep: an R package for preanalysis data description and processing

Abstract

Motivation Metabolomics is an increasingly common part of health research and there is need for pre-analytical data processing. Researchers typically need to characterise the data and to exclude errors within the context of the intended analysis. While some pre-processing steps are common, there is currently a lack of standardization and reporting transparency for these procedures. Results Here we introduce metaboprep, a standardised data processing workflow to extract and characterise high quality metabolomics data sets. The package extracts data from pre-formed worksheets, provides summary statistics and enables the user to select samples and metabolites for their analysis based on a set of quality metrics. A report summarising quality metrics and the influence of available batch variables on the data is generated for the purpose of open disclosure. Where possible, we provide users flexibility in defining their own selection thresholds. Availability and implementation metaboprep is an open-source R package available at https://github.com/MRCIEU/metaboprep Supplementary information Supplementary data are available at Bioinformatics online.

A multivariant recall‐by‐genotype study of the metabolomic signature of BMI

“…We processed the raw (original scale) data received from Metabolon ( N = 760 samples) in preparation for statistical analysis using a prerelease version of metaboprep (14). Data were filtered based on a series of quality metrics.…”

Impact of weight loss on cancer-related proteins in serum: results from a cluster randomised controlled trial of individuals with type 2 diabetes

“…We applied further quality control (QC) steps using the metaboprep R package: samples and features were excluded based on extreme missingness (80%) before missingness was recalculated and exclusions made using a 20% threshold, additional exclusions were based on sample total sum abundance (>5 SD from the mean) and PCA of independent features (>5 SD from the mean). 30 Protein measurements below the limit of detection were retained with their recorded value with the caveat that data below the limit of detection have a higher risk to be in the non-linear phase of the S-curve of the NPX unit which may bias estimates. All protein measures were standardised and normalised prior to analyses using rank-based inverse normal transformation (RNT).…”

The metabolomic signature of weight loss and remission in the Diabetes Remission Clinical Trial (DiRECT)

“…Data quality checks were carried out locally using a pre-release version of the R package metaboprep [ 18 ] with samples and features excluded from subsequent statistical analysis based on a pre-defined set of quality control (QC) metrics. Full details of the procedures are given in ESM Methods and data summaries produced are included within the associated GitHub repository ( https://github.com/lauracorbin/metabolomics_of_direct ).…”