Mass spectrometry-based proteomics data from thousands of HeLa control samples

Webel, Henry; Perez-Riverol, Yasset; Nielson, Annelaura Bach; Rasmussen, Simon

doi:10.21203/rs.3.rs-3083547/v1

Cited by 3 publications

(5 citation statements)

References 14 publications

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“…txt for protein groups. The full dataset and detailed pre-processing steps are explained in a Data Descriptor 46 .…”

Section: Methodsmentioning

confidence: 99%

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Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning

Webel

Niu

Nielsen

et al. 2023

Preprint

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Imputation techniques provide means to replace missing measurements with a value and are used in almost all downstream analysis of mass spectrometry (MS) based proteomics data using label-free quantification (LFQ). Some methods only impute assuming the limit of detection (LOD) was not passed and therefore impute missing values with too low or too high intensities, potentially leading to biased results in downstream statistical analysis. Here we test how self supervised deep learning models can impute missing values in the context of LFQ at different levels: precursors, aggregated peptides or protein groups. We demonstrate how collaborative filtering, denoising autoencoders, and variational autoencoders can be used to reconstruct missing values and can make more relevant features available for downstream analysis compared to current approaches. Additionally, we show that deep learning approaches can model data in its entirety for imputation and offer an approach for controlled evaluation of imputation approaches. We applied our method, proteomics imputation modeling mass spectrometry (PIMMS), to an alcohol-related liver disease (ALD) cohort with blood plasma proteomics data available for 358 individuals. We identified 49 additional proteins (+52.7%) that are significantly differentially abundant across disease stages compared to traditional methods and found that some of these were predictive of ALD progression in machine learning models. We, therefore, suggest the use of deep learning approaches for imputing missing values in MS-based proteomics and provide workflows for these.

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“…txt for protein groups. The full dataset and detailed pre-processing steps are explained in a Data Descriptor 46 .…”

Section: Methodsmentioning

confidence: 99%

“…From the MaxQuant summary folder we then used the evidence.txt for precursor quantifications, peptides.txt for aggregated peptides and proteinGroups.txt for protein groups. The full dataset and detailed pre-processing steps are explained in a Data Descriptor 46 .…”

Section: Description Of Raw File Processing Of Hela Proteomics Datasetmentioning

confidence: 99%

Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning

Webel

Niu

Nielsen

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our development dataset consisted of 564 HeLa runs of one Q Executive HF-X Orbitrap generated during continuous quality control of the mass spectrometers 35 . We initially investigated the structure of the dataset using the first two principal components (Fig.…”

Section: Evaluating Self-supervised Models For Imputation Of Ms Datamentioning

confidence: 99%

Section: Description Of Raw File Processing Of Hela Proteomics Datasetmentioning

confidence: 99%

Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning

Webel,

Niu,

Nielsen

et al. 2024

Nat Commun

View full text Add to dashboard Cite

Imputation techniques provide means to replace missing measurements with a value and are used in almost all downstream analysis of mass spectrometry (MS) based proteomics data using label-free quantification (LFQ). Here we demonstrate how collaborative filtering, denoising autoencoders, and variational autoencoders can impute missing values in the context of LFQ at different levels. We applied our method, proteomics imputation modeling mass spectrometry (PIMMS), to an alcohol-related liver disease (ALD) cohort with blood plasma proteomics data available for 358 individuals. Removing 20 percent of the intensities we were able to recover 15 out of 17 significant abundant protein groups using PIMMS-VAE imputations. When analyzing the full dataset we identified 30 additional proteins (+13.2%) that were significantly differentially abundant across disease stages compared to no imputation and found that some of these were predictive of ALD progression in machine learning models. We, therefore, suggest the use of deep learning approaches for imputing missing values in MS-based proteomics on larger datasets and provide workflows for these.

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“…The data is available at PRIDE PXD042233 11 . Each uploaded raw file has a MaxQuant search output associated with a set of standard text files as described on their website.…”

Section: Data Recordsmentioning

confidence: 99%

Mass spectrometry-based proteomics data from thousands of HeLa control samples

Webel,

Perez-Riverol,

Nielson

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Here we provide a curated, large scale, label free mass spectrometry-based proteomics data set derived from HeLa cell lines for general purpose machine learning and analysis. Data access and filtering is a tedious task, which takes up considerable amounts of time for researchers. Therefore we provide machine based metadata for easy selection and overview along the 7,444 raw files and MaxQuant search output. For convenience, we provide three filtered and aggregated development datasets on the protein groups, peptides and precursors level. Next to providing easy to access training data, we provide a SDRF file annotating each raw file with instrument settings allowing automated reprocessing. We encourage others to enlarge this data set by instrument runs of further HeLa samples from different machine types by providing our workflows and analysis scripts.

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Mass spectrometry-based proteomics data from thousands of HeLa control samples

Cited by 3 publications

References 14 publications

Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning

Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning

Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning

Mass spectrometry-based proteomics data from thousands of HeLa control samples

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