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MsQuality - MsQuality - Quality metric calculation from Spectra, MsExperiment and Chromatograms objects

The MsQuality provides functionality to calculate quality metrics for mass spectrometry-derived, spectral data at the per-sample level. MsQuality relies on the mzQC framework of quality metrics defined by the Human Proteom Organization-Proteomics Standards Initiative (HUPO-PSI). These metrics quantify the quality of spectral raw files using a controlled vocabulary. The package is especially addressed towards users that acquire mass spectrometry data on a large scale (e.g. data sets from clinical settings consisting of several thousands of samples). The MsQuality package allows to calculate low-level quality metrics that require minimum information on mass spectrometry data: retention time, m/z values, and associated intensities. MsQuality relies on the Spectra package, or alternatively the MsExperiment package, and its infrastructure to store spectral data. Additionally, MsQuality supports Chromatograms objects from the Chromatograms package for chromatographic quality metrics.

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metabolomicsproteomicsmassspectrometryqualitycontrolmass-spectrometryqc

6.16 score 9 stars 8 scripts 364 downloads

MetNet - Inferring metabolic networks from untargeted high-resolution mass spectrometry data

MetNet contains functionality to infer metabolic network topologies from quantitative data and high-resolution mass/charge information. Using statistical models (including correlation, mutual information, regression and Bayes statistics) and quantitative data (intensity values of features) adjacency matrices are inferred that can be combined to a consensus matrix. Mass differences calculated between mass/charge values of features will be matched against a data frame of supplied mass/charge differences referring to transformations of enzymatic activities. In a third step, the two levels of information are combined to form a adjacency matrix inferred from both quantitative and structure information.

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immunooncologymetabolomicsmassspectrometrynetworkregression

4.40 score 3 scripts 575 downloads

MatrixQCvis - Shiny-based interactive data-quality exploration for omics data

Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualizations include amongst others bar- and violin plots of the (count/intensity) values, mean vs standard deviation plots, MA plots, empirical cumulative distribution function (ECDF) plots, visualizations of the distances between samples, and multiple types of dimension reduction plots. Furthermore, MatrixQCvis allows for differential expression analysis based on the limma (moderated t-tests) and proDA (Wald tests) packages. MatrixQCvis builds upon the popular Bioconductor SummarizedExperiment S4 class and enables thus the facile integration into existing workflows. The package is especially tailored towards metabolomics and proteomics mass spectrometry data, but also allows to assess the data quality of other data types that can be represented in a SummarizedExperiment object.

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visualizationshinyappsguiqualitycontroldimensionreductionmetabolomicsproteomicstranscriptomics

4.00 score 9 scripts 398 downloads

MetCirc - Navigating mass spectral similarity in high-resolution MS/MS metabolomics data metabolomics data

MetCirc comprises a workflow to interactively explore high-resolution MS/MS metabolomics data. MetCirc uses the Spectra object infrastructure defined in the package Spectra that stores MS/MS spectra. MetCirc offers functionality to calculate similarity between precursors based on the normalised dot product, neutral losses or user-defined functions and visualise similarities in a circular layout. Within the interactive framework the user can annotate MS/MS features based on their similarity to (known) related MS/MS features.

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shinyappsmetabolomicsmassspectrometryvisualization

3.48 score 5 scripts 510 downloads