Mass spectrometry imaging (MSI) experiments result in complex multi-dimensional datasets, which require specialist data analysis tools. Here we have developed massPix - an R package for analysing and interpreting data from MSI of lipids in tissue. MassPix is an open-source tool for the analysis and statistical interpretation of MSI data, and is particularly useful for lipidomics applications. MassPix produces single ion images, performs multivariate statistics and provides putative lipid annotations based on accurate mass matching against generated lipid libraries. Classification of tissue regions with high spectral similarly can be carried out by principal components analysis (PCA) or k-means clustering. Mouse cerebellum was analysed using matrix assisted laser desorption ionisation (MALDI) MSI. The resulting MSI dataset forms the test data for massPix.

The FragPipe computational proteomics platform is gaining widespread popularity among the proteomics research community because of its fast processing speed and user-friendly graphical interface. Although FragPipe produces well-formatted output tables that are ready for analysis, there is still a need for an easy-to-use and user-friendly downstream statistical analysis and visualization tool. FragPipe-Analyst addresses this need by providing an R shiny web server to assist FragPipe users in conducting downstream analyses of the resulting quantitative proteomics data. It supports major quantification workflows, including label-free quantification, tandem mass tags, and data-independent acquisition. FragPipe-Analyst offers a range of useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To support advanced analysis and customized visualizations, we also developed FragPipeAnalystR, an R package encompassing all FragPipe-Analyst functionalities that is extended to support site-specific analysis of post-translational modifications (PTMs). FragPipe-Analyst and FragPipeAnalystR are both open-source and freely available.

Replication data for the publication: "rSIREM: an R package for MALDI spectral deconvolution" by Del Castillo Pérez et al. The deposited data are SALDI-MSI data of three consectutive thin tissue sections from mouse cerebellum measured at the different mass resolutions at the same instrument (MALDI-MSI: Spectroglyph Injector - Orbitrap Exploris). The paper describes a new R package (rSIREM) to computationally improve the mass resolution of an MSI post-measurement. The developed R package (https://github.com/EdelCastillo/rSirem ) applies a statistical treatment on the concentration of spatial images obtained by separately considering each of the m/z over all the pixels. A representative scalar is associated with each image, obtained by applying a new measure (SIREM) to it, derived from Shannon's entropy. The perturbations of this measure, when considering a sequence of consecutive images, reveal the existence of overlap, if it exists. This information serves as a seed to initialize the EM algorithm in the Gaussian Mixture Model context. The efficiency of the method has been verified using three independent procedures.

In the field of proteomics, generating biologically relevant results from mass spectrometry (MS) signals remains a challenging task. This is partly due to the fact that the computational strategies for converting MS signals into biologically interpretable data depend heavily on the MS acquisition method. Additionally, the processing and the analysis of these data vary depending on whether the proteomic experiment was performed with or without labeling, and with or without fractionation. Several R packages have been developed for processing and analyzing MS data, but they only incorporate identification and quantification data; none of them takes into account other invaluable information collected during MS runs. To address this limitation, we introduce MCQR, an alternative R package for the in-depth exploration, processing, and analysis of quantitative proteomics data generated from either data-dependent or data-independent acquisition methods. MCQR leverages experimental retention time measurements for quality control, data filtering, and processing. Its modular architecture offers flexibility to accommodate various types of proteomics experiments, including label-free, label-based, fractionated, or those enriched for specific post-translational modifications. Its functions, designed as simple building blocks, are user-friendly, making it easy to test parameters and methods, and to construct customized analysis scenarios. These unique features position MCQR as a comprehensive toolbox, perfectly suited to the specific needs of MS-based proteomics experiments.

The repository contains three mzML and four imzML mass spectrometry datasets,

The mzML data are compiled in a single directory 'mzML' and zipped:

• Col_1.mzML is a liquid chromatography (LC) ESI MS dataset from an Arabidopsis extraction published in: Sotelo-Silveira, M., Chauvin, A.-L., Marsch-Martínez, N., Winkler, R. & De Folter, S. Metabolic fingerprinting of Arabidopsis thaliana accessions. Frontiers in Plant Science 6, 1–13 (2015). https://doi.org/10.3389/fpls.2015.00365.
• Cytochrome_C.mzML is an electrospray mass spectrometry (ESI MS) dataset of Cytochrome C. The data were discussed in: Winkler, R. ESIprot: a universal tool for charge state determination and molecular weight calculation of proteins from electrospray ionization mass spectrometry data. Rapid Communications in Mass Spectrometry 24, 285- 294 (2010). https://doi.org/10.1002/rcm.4384.
• T9_A1.mzML is a low-temperature plasma (LTP) MS dataset of the interaction between Arabidopsis and Trichoderma, published in 1. Torres-Ortega, R. et al. In Vivo Low-Temperature Plasma Ionization Mass Spectrometry (LTP-MS) Reveals Regulation of 6-Pentyl-2H-Pyran-2-One (6-PP) as a Physiological Variable during Plant-Fungal Interaction. Metabolites 12, 1231 (2022). https://doi.org/10.3390/metabo12121231.

The imzML mass spectrometry imaging data are zipped individually:

• imzML_AP_SMALDI.zip contains an AP-SMALDI mass spectrometry imaging data set of mouse urinary bladder slides, published by Römpp A, Guenther S, Schober Y, Schulz O, Takats Z, Kummer W, Spengler B., ProteomeXchange dataset PXD001283. 2014., and available from https://www.ebi.ac.uk/pride/archive/projects/PXD001283; Publication: Römpp A, Guenther S, Schober Y, Schulz O, Takats Z, Kummer W, Spengler B; Histology by mass spectrometry: label-free tissue characterization obtained from high-accuracy bioanalytical imaging., Angew Chem Int Ed Engl, 49, 22, 3834-8 (2014). https://doi.org/10.1002/anie.200905559, PubMed: 20397170.
• imzML_DESI.zip is a DESI mass spectrometry imaging data set of human colorectal cancer tissue by Oetjen J, Veselkov K, Watrous J, McKenzie JS, Becker M, Hauberg-Lotte L, Kobarg JH, Strittmatter N, Mróz AK, Hoffmann F, Trede D, Palmer A, Schiffler S, Steinhorst K, Aichler M, Goldin R, Guntinas-Lichius O, von Eggeling F, Thiele H, Maedler K, Walch A, Maass P, Dorrestein PC, Takats Z, Alexandrov T. 2015. Benchmark datasets for 3D MALDI-and DESI-imaging mass spectrometry. GigaScience 4(1):2105 https://doi.org/10.1186/s13742-015-0059-4.
• imzML_LA-ESI.zip is an LA-ESI mass spectrometry imaging data set of an Arabidopsis thaliana leaf by Zheng, Z., Bartels, B., & Svatoš, A. (2020). Laser Ablation Electrospray Ionization Mass Spectrometry Imaging (LAESI MSI) of Arabidopsis thaliana leaf [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3678473.
• imzML_LTP.zip was generated by low-temperature plasma ionization ambient mass spectrometry imaging of a chili fruit, published by Maldonado-Torres M, López-Hernández Jé F, Jiménez-Sandoval P, Winkler R. 2014. Plug and play' assembly of a low-temperature plasma ionization mass spectrometry imaging (LTP-MSI) system. Journal of Proteomics 102C:60–65 https://doi.org/10.1016/j.jprot.2014.03.003; Mauricio Maldonado-Torres, José Fabricio López-Hernández, Pedro Jiménez-Sandoval, & Robert Winkler. (2017). Low-temperature plasma mass spectrometry imaging (LTP-MSI) of Chili pepper [Data set]. In Journal of proteomics (Vol. 102, pp. 60–65). Zenodo. https://doi.org/10.5281/zenodo.484496.

All these datasets are publicly available from different repositories; however, If you reuse them, please attribute the original authors!

Supporting datasets and algorithms (R-based) for the manuscript entitled "Development of a Tool to Determine the Variability of Consensus Mass Spectra", including an R Markdown script to reproduce the manuscript's figures.

This dataset is about: (Appendix 2) Compilation of mass accumulation rates from deep sea sediments during the last glacial maximum. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.726364 for more information.

Quantitative comparison of the protein content of biological samples is a fundamental tool of research. The TMT and iTRAQ isobaric labeling technologies allow the comparison of 2, 4, 6, or 8 samples in one mass spectrometric analysis. Sound statistical models that scale with the most advanced mass spectrometry (MS) instruments are essential for their efficient use. Through the application of robust statistical methods, we developed models that capture variability from individual spectra to biological samples. Classical experimental designs with a distinct sample in each channel as well as the use of replicates in multiple channels are integrated into a single statistical framework. We have prepared complex test samples including controlled ratios ranging from 100:1 to 1:100 to characterize the performance of our method. We demonstrate its application to actual biological data sets originating from three different laboratories and MS platforms. Finally, test data and an R package, named isobar, which can read Mascot, Phenyx, and mzIdentML files, are made available. The isobar package can also be used as an independent software that requires very little or no R programming skills.

This dataset contains aerosol gravimetric analysis of mass, using 2-stage multi-jet cascade impactors, taken aboard the Ron Brown ship during the ACE-Asia field project. This dataset contains the tab delimited (.acf) data files. Data can also be downloaded in a netCDF format.

Motivation

Estimating the properties of galaxies, and even where they are, is a challenging process. The Rubin Observatory, a sky survey telescope located in Chile, will once it becomes operational image tens of billions of astronomical objects, the vast majority of which will be galaxies that have never been imaged before. Analyses of these data will require sophisticated methodologies, ones that will allow us to first determine where the galaxies are (i.e., how far away they are), and then conditional on the distance, how massive they are. Given galaxy distance and mass data, we can test theories of how the Universe evolves, by comparing simulated galaxy data with these data.

The Buzzard-V1.0 simulation was used to generate a realistic sample of Rubin Observatory data. In this dataset are measurements for 111,172 galaxies. Developers used these data to benchmark, e.g., methods for estimating galaxy distance. Here, we can assume the distance has been estimated well, and use these data to try to model galaxy mass as a function of brightness and distance.

Data

The dataset contains measures of magnitude and magnitude uncertainty in six astronomical bands (u for ultraviolet, g for green, r for red, i for infrared, and z and y for two additional infrared bands). Magnitude is a logarithmic measure of brightness, with an increase of 5 representing a decrease in brightness by a factor of 100, and with a value of zero being represented (roughly) by how the star Vega appears in the night sky. In addition, there is a redshift measured for each galaxy; it represents by how much light from the galaxy is stretched (by the expansion of Universe) as it travels to us. Thus higher redshifts represent larger distances. The last measurement is log.mass, which is the base-10 logarithm of the galaxy stellar mass in units of solar mass; for instance, log.mass = 10 means that the galaxy has a mass 10 billion times that of the Sun.

Variable Description

• u Galaxy magnitude in Rubin u band (320.5-393.5 nm)
• g Galaxy magnitude in Rubin g band (401.5-551.9 nm)
• r Galaxy magnitude in Rubin r band (552.0-691.0 nm)
• i Galaxy magnitude in Rubin i band (691.0-818.0 nm)
• z Galaxy magnitude in Rubin z band (818.0-923.5 nm)
• y Galaxy magnitude in Rubin y band (923.8-1084.5 nm)
• u.err Uncertainty for u-band magnitude
• g.err Uncertainty for g-band magnitude
• r.err Uncertainty for r-band magnitude
• i.err Uncertainty for i-band magnitude
• z.err Uncertainty for z-band magnitude
• y.err Uncertainty for y-band magnitude
• log.mass Galaxy stellar mass (log-base-10 solar masses)
• redshift Galaxy redshift

Questions

As noted above, the idea here is to learn a statistical association between measures of magnitude and distance, and galaxy mass.

One wrinkle here that analysts can exploit is that the data contain standard error estimates for the magnitudes (though not for redshift, for which, in practice, the error would be ).

References

Schmidt, Malz, Soo, Almosallam, Brescia, Cavuoti, Cohen-Tanugi, Connolly, DeRose, Freeman, Graham, Iyer, Jarvis, Kalmbach, Kovacs, Lee, Longo, Morrison, Newman, Nourbakhsh, Nuss, Pospisil, Tranin, Wechsler, Zhou, Izbicki, (The LSST Dark Energy Science Collaboration). “Evaluation of probabilistic photometric redshift estimation approaches for The Rubin Observatory Legacy Survey of Space and Time (LSST)”. Monthly Notices of the Royal Astronomical Society 499, December 2020, pages 1587–1606. https://doi.org/10.1093/mnras/staa2799

Acknowledgement

Foto from unsplash

Following files contain the results of LARAPPI/CI (Laser Ablation Remote Atmospheric Pressure Photoionization/Chemical Ionization)-3D-MSI performed employing an ultrahigh resolution QToF (quadrupole-time-of-flight) mass spectrometer Bruker Impact II.2D_Fusarium_graminarum_Bacillus_cereus.d.zip: The .zip file contains raw data from LARAPPI/CI-MSI from the 2D measurement of the sample with Fusarium graminarum and Paenibacillus xylanexedens. The .d files can be accessed using the open-source R package available for free.Fusarium_graminarum_Paenibacillus_xylanexedens.zip: The .zip file contains raw data from LARAPPI/CI-MSI from the 3D measurement of the sample with Fusarium graminarum and Paenibacillus xylanexedens, including a .d file for each of the two steps. The .d files can be accessed using the open-source R package available for free.

Here we describe the release of the measurements of the galaxy Stellar Mass Function and quiescent mass fractions based on the COSMOS2020 Farmer Catalogue and LePhare estimates of redshifts, masses, and rest-frame colours as described in Weaver et al. 2023 (arXiv:2212.02512v1).

When using these data products please cite both this SMF paper (Weaver et al. 2023) and the COSMOS2020 Catalogue (Weaver et al. 2022). Links to ADS export citations:

SMF | https://ui.adsabs.harvard.edu/abs/2022arXiv221202512W
COSMOS2020 | https://ui.adsabs.harvard.edu/abs/2022ApJS..258...11W

Please reach out if you have any questions or concerns.

Supporting datasets and algorithms (R-based) for the manuscript entitled "Development of a Tool to Determine the Variability of Consensus Mass Spectra", including an R Markdown script to reproduce the manuscript's figures.

Requisite R files for the Proteo-SAFARI app

Following files contain the results of LARAPPI/CI (Laser Ablation Remote Atmospheric Pressure Photoionization/Chemical Ionization)-3D-MSI performed employing an ultrahigh resolution QToF (quadrupole-time-of-flight) mass spectrometer Bruker Impact II.3D_step0_6_d.zip; 3D_step1_6_d.zip; 3D_step2_6_d.zip: the .zip files contains raw 3D MSI data in .d format including 3 measurents, one from each level of the sample with Fusarium avenaceum and Priestia megaterium. The data can be opened using an open-source R package that is freely available.3D_step0_8_d.zip; 3D_step1_8_d.zip; 3D_step2_8_d.zip: the .zip files contains raw 3D MSI data in .d format including 3 measurents, one from each level of the sample with Fusarium avenaceum and Bacillus licheniformis. The data can be opened using an open-source R package that is freely available.

Scripts for analysis of DI-qTOF data recorded for studies of refractory dissolved organic matter

This dataset is about: (Table 2) Accelerator mass spectrometry (AMS) radiocarbon dates and calibrated ages. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.819014 for more information.

This dataset contains mass-to-charge ratio (m/z) and monoisotopic intensity peaks detected in the northern and southern Gulf of Mexico dissolved organic matter extracts using a Fourier transform ion cyclotron mass spectrometry (FTICR-MS). The instrument used was manufactured by Bruker Daltonics; Model: Solarix, 12T. The water samples were collected using a CTD-Niskin rosette aboard the R/V Justo Sierra cruise JS-0815 in the southern Gulf of Mexico on 2015-08-03 and aboard the R/V Weatherbird II cruise WB-0815 in the northern Gulf of Mexico on 2015-08-18. The R/V Justo Sierra cruise was led by chief scientist Dr. Steve Murawski and the R/V Weatherbird II cruise was led by chief scientist Dr. Dave Hollander. Mass-to-charge ratio (m/z) and monoisotopic intensity from a sediment core collected aboard the R/V Justo Sierra cruise JS-0815 in the southern Gulf of Mexico is available under GRIIDC UDI R4.x267.179:0006.

The test statistics are: Trend — Cochran-Armitage trend test; Chi-Square — Pearson's chi-square test; LRT — the likelihood ratio test for the proportional odds model computed by using the polr function in the R package MASS; Score — the proposed score statistic computed by using the SNPass.test function in the R package iGasso.Simulated type I error rate in the case of a univariate phenotype under various generating models.

Location codes that start with a “1” indicate the front of the body and codes that begin with a “2” indicate the back of the body.