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A comprehensive LFQ benchmark dataset to validate data analysis pipelines on modern day acquisition strategies in proteomics

ABSTRACT: A comprehensive LFQ benchmark dataset to validate data analysis pipelines on modern day acquisition strategies in proteomics using SCIEX TripleTOF5600 and 6600+, Orbitrap QE-HFX, Waters Synapt GS-Si and Synapt XS and Bruker timsTOF Pro.

INSTRUMENT(S): timsTOF Pro, TripleTOF 6600, TripleTOF 5600, Synapt MS, Q Exactive HF

ORGANISM(S): Escherichia Coli (ncbitaxon:562) Candida Albicans (ncbitaxon:5476) Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Maarten Dhaenens

PROVIDER: MSV000096179 | MassIVE | Wed Oct 23 14:50:00 BST 2024

SECONDARY ACCESSION(S): PXD028735

REPOSITORIES: MassIVE

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Project description:Background In the last few years, multi-omics data, that is, datasets containing different types of high-dimensional molecular variables for the same samples, have become increasingly available. To date, several comparison studies focused on feature selection methods for omics data, but to our knowledge, none compared these methods for the special case of multi-omics data. Given that these data have specific structures that differentiate them from single-omics data, it is unclear whether different feature selection strategies may be optimal for such data. In this paper, using 15 cancer multi-omics datasets we compared four filter methods, two embedded methods, and two wrapper methods with respect to their performance in the prediction of a binary outcome in several situations that may affect the prediction results. As classifiers, we used support vector machines and random forests. The methods were compared using repeated fivefold cross-validation. The accuracy, the AUC, and the Brier score served as performance metrics. Results The results suggested that, first, the chosen number of selected features affects the predictive performance for many feature selection methods but not all. Second, whether the features were selected by data type or from all data types concurrently did not considerably affect the predictive performance, but for some methods, concurrent selection took more time. Third, regardless of which performance measure was considered, the feature selection methods mRMR, the permutation importance of random forests, and the Lasso tended to outperform the other considered methods. Here, mRMR and the permutation importance of random forests already delivered strong predictive performance when considering only a few selected features. Finally, the wrapper methods were computationally much more expensive than the filter and embedded methods. Conclusions We recommend the permutation importance of random forests and the filter method mRMR for feature selection using multi-omics data, where, however, mRMR is considerably more computationally costly. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04962-x.

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A comprehensive LFQ benchmark dataset to validate data analysis pipelines on modern day acquisition strategies in proteomics

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