Several different inventories of global and regional anthropogenic and biomass burning emissions are assessed for the 1980-2010 period. The species considered in this study are carbon monoxide, nitrogen oxides, sulfur dioxide and black carbon. The inventories considered include the ACCMIP historical emissions developed in support of the simulations for the IPCC AR5 assessment. Emissions for 2005 and 2010 from the Representative Concentration Pathways (RCPs) are also included. Large discrepancies between the global and

Prior to statistical analysis of mass spectrometry (MS) data, quality control (QC) of the identified biomolecule peak intensities is imperative for reducing process-based sources of variation and extreme biological outliers. Without this step, statistical results can be biased. Additionally, liquid chromatography–MS proteomics data present inherent challenges due to large amounts of missing data that require special consideration during statistical analysis. While a number of R packages exist to address these challenges individually, there is no single R package that addresses all of them. We present pmartR, an open-source R package, for QC (filtering and normalization), exploratory data analysis (EDA), visualization, and statistical analysis robust to missing data. Example analysis using proteomics data from a mouse study comparing smoke exposure to control demonstrates the core functionality of the package and highlights the capabilities for handling missing data. In particular, using a combined quantitative and qualitative statistical test, 19 proteins whose statistical significance would have been missed by a quantitative test alone were identified. The pmartR package provides a single software tool for QC, EDA, and statistical comparisons of MS data that is robust to missing data and includes numerous visualization capabilities.

The high-resolution and mass accuracy of Fourier transform mass spectrometry (FT-MS) has made it an increasingly popular technique for discerning the composition of soil, plant and aquatic samples containing complex mixtures of proteins, carbohydrates, lipids, lignins, hydrocarbons, phytochemicals and other compounds. Thus, there is a growing demand for informatics tools to analyze FT-MS data that will aid investigators seeking to understand the availability of carbon compounds to biotic and abiotic oxidation and to compare fundamental chemical properties of complex samples across groups. We present ftmsRanalysis, an R package which provides an extensive collection of data formatting and processing, filtering, visualization, and sample and group comparison functionalities. The package provides a suite of plotting methods and enables expedient, flexible and interactive visualization of complex datasets through functions which link to a powerful and interactive visualization user interface, Trelliscope. Example analysis using FT-MS data from a soil microbiology study demonstrates the core functionality of the package and highlights the capabilities for producing interactive visualizations. Author summaryHigh-resolution mass spectrometry instruments provide a mechanism for researchers to better understand the fundamental chemical composition of materials such as soil, plants, petroleum, and beverages. The large and complex data generated by analysis of these materials has led to a growing demand for software tools to aid researchers in processing, analyzing, and creating informative visualizations of these data. To move beyond existing software tools designed for specific purposes and visualizations of data from an individual sample, we present a software package, ftmsRanalysis, that provides researchers with a large collection of methods for streamlining the downstream processing high-resolution mass spectrometry data. ftmsRanalysis provides methods to compute useful chemical properties, filter data, define groups of samples, statistically compare sample groups, and PLOS COMPUTATIONAL BIOLOGY PLOS Computational Biology | https://doi.make visualizations for many samples simultaneously. In this paper, we give an overview of ftmsRanalysis' general structure and capabilities. We then apply ftmsRanalysis to a soil microbiology dataset and present some of the results and visualizations generated by using the software package. This is a PLOS Computational Biology Software paper.