Semi-supervised Learning While Controlling the FDR with an Application to Tandem Mass Spectrometry Analysis

Freestone, Jack; Käll, Lukas; Noble, William Stafford; Keich, Uri

doi:10.1007/978-1-0716-3989-4_50

Lecture Notes in Computer Science

2024

DOI: 10.1007/978-1-0716-3989-4_50

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Semi-supervised Learning While Controlling the FDR with an Application to Tandem Mass Spectrometry Analysis

Jack Freestone,

Lukas Käll,

William Stafford Noble

et al.

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Cited by 2 publications

References 7 publications

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Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment

Wen,

Freestone,

Riffle

et al. 2024

Preprint

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A pressing statistical challenge in the field of mass spectrometry proteomics is how to assess whether a given software tool provides accurate error control. Each software tool for searching such data uses its own internally implemented methodology for reporting and controlling the error. Many of these software tools are closed source, with incompletely documented methodology, and the strategies for validating the error are inconsistent across tools. In this work, we identify three different methods for validating false discovery rate (FDR) control in use in the field, one of which is invalid, one of which can only provide a lower bound rather than an upper bound, and one of which is valid but under-powered. The result is that the field has a very poor understanding of how well we are doing with respect to FDR control, particularly for the analysis of data-independent acquisition (DIA) data. We therefore propose a new, more powerful method for evaluating FDR control in this setting, and we then employ that method, along with an existing lower bounding technique, to characterize a variety of popular search tools. We find that the search tools for analysis of data-dependent acquisition (DDA) data generally seem to control the FDR at the peptide level, whereas none of the DIA search tools consistently controls the FDR at the peptide level across all the datasets we investigated. Furthermore, this problem becomes much worse when the latter tools are evaluated at the protein level. These results may have significant implications for various downstream analyses, since proper FDR control has the potential to reduce noise in discovery lists and thereby boost statistical power.

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Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment

Wen,

Freestone,

Riffle

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Improved detection of differentially abundant proteins through FDR-control of peptide-identity-propagation

Solivais,

Boekweg,

Smith

et al. 2024

Preprint

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Quantitative analysis of proteomics data frequently employs peptide-identity-propagation (PIP) - also known as match-between-runs (MBR) - to increase the number of peptides quantified in a given LC-MS/MS experiment. PIP can routinely account for up to 40% of all quantitative results, with that proportion rising as high as 75% in single-cell proteomics. Therefore, a significant concern for any PIP method is the possibility of false discoveries: errors that result in peptides being quantified incorrectly. Although several tools for label-free quantification (LFQ) claim to control the false discovery rate (FDR) of PIP, these claims cannot be validated as there is currently no accepted method to assess the accuracy of the stated FDR. We present a method for FDR control of PIP, called "PIP-ECHO" (PIP Error Control via Hybrid cOmpetition) and devise a rigorous protocol for evaluating FDR control of any PIP method. Using three different datasets, we evaluate PIP-ECHO alongside the PIP procedures implemented by FlashLFQ, IonQuant, and MaxQuant. These analyses show that PIP-ECHO can accurately control the FDR of PIP at 1% across multiple datasets. Only PIP-ECHO was able to control the FDR in data with injected sample size equivalent to a single-cell dataset. The three other methods fail to control the FDR at 1%, yielding false discovery proportions ranging from 2-6%. We demonstrate the practical implications of this work by performing differential expression analyses on spike-in datasets, where different known amounts of yeast or E. coli peptides are added to a constant background of HeLa cell lysate peptides. In this setting, PIP-ECHO increases both the accuracy and sensitivity of differential expression analysis: our implementation of PIP-ECHO within FlashLFQ enables the detection of 53% more differentially abundant proteins than MaxQuant and 146% more than IonQuant in the spike-in dataset.

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Semi-supervised Learning While Controlling the FDR with an Application to Tandem Mass Spectrometry Analysis

Cited by 2 publications

References 7 publications

Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment

Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment

Improved detection of differentially abundant proteins through FDR-control of peptide-identity-propagation

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