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GNPS Inter-laboratory dataset from a collaborative trial for future use in the development of non-target analysis

ABSTRACT: This study was organized through the Norman Network and included 21 participants from 11 countries. A set of samples based on the passive sampling of drinking water pre and post treatment was shipped to all the participating laboratories for analysis, using one pre-defined method and one locally (i.e. in-house) developed method. The data generated represents a valuable resource (i.e. benchmark) for future developments of algorithms and workflows for NTA experiments

INSTRUMENT(S): LC-MS

ORGANISM(S): Water

SUBMITTER: Dr. Saer Samanipour

PROVIDER: MSV000087368 | GNPS | Thu May 06 00:51:00 BST 2021

REPOSITORIES: GNPS

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Project description:Background: As costs decline, the size and scope of microarray experiments have increased. In multi-centre studies there is a need to ensure consistency of data pre-processing across centres. Similarly, in smaller scale studies the evolution of microarray platforms means that there is often a need to compare data generated on earlier microarrays to that generated on newer ones. It is important in such studies to ensure that platform-dependent biases are removed so that meta-analysis of different datasets can be performed reliably. In both these cases the optimal scenario is to have a small subset of samples repeated at each site or on each platform. These replicates can then be used to learn a relationship between probe intensities on the two platforms. Results: I introduce here a simple, linear-modelling-based method for normalizing data from multiple-platforms by using replicate hybridizations. A dataset of 20 rat liver samples is used as a benchmark. Eight samples are hybridized to two separate versions of Affymetrix microarrays, while the other 12 are hybridized to one, for a total of 28 arrays. Our linear modelling method removes platform bias as assessed using both unsupervised machine-learning and two-group statistical analyses. The method is computationally efficient and works well for data pre-processed by the GCRMA, RMA and MAS5 algorithms and using either default or alternative probe-mappings. The method is very stable towards the number of replicate samples used, with even two replicates greatly reducing platform-specific bias. Conclusions: A simple linear-modelling method can remove platform-specific bias independent of the pre-processing algorithm and ProbeSet-mapping used. This technique can readily be extended to multi-site experiments, and suggests the benefits of including a small number of replicate hybridizations in each new study as a normalization control. Twenty rats livers were processed, eight on both RAE230-A and RAE230-2 arrays, 8 on only RAE230-A arrays, and 4 on RAE230-2 arrays only.

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GNPS Inter-laboratory dataset from a collaborative trial for future use in the development of non-target analysis

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