Prior to statistical analysis of mass spectrometry (MS) data, quality control (QC) of the identified biomolecule peak intensities is imperative to remove outliers and random effects that arise from the mapping of raw mass spectra to identified biomolecules with observed values. Without this step, statistical results can be biased. Additionally, liquid-chromatography-MS proteomics data presents 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, normalization, exploratory data analysis (EDA)), visualization, and statistical analysis robust to missing data. Example analysis using proteomics data from a virology study comparing infected and control samples 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 56 proteins were identified whose statistical significance would have been missed by a quantitative test alone. 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. High-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 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.

Most soil carbon (C) is in the form of soil organic matter (SOM), the composition of which is controlled by the plant–microbe–soil continuum. The extent to which plant and microbial inputs contribute to persistent SOM has been linked to edaphic properties such as mineralogy and aggregation. However, it is unknown how variation in plant inputs, microbial community structure, and soil physical and chemical attributes interact to influence the chemical classes that comprise SOM pools. We used two long‐term biofuel feedstock field experiments to test the influence of cropping systems (corn and switchgrass) and soil characteristics (sandy and silty loams) on microbial selection and SOM chemistry. Cropping system had a strong influence on water‐extractable organic C chemistry with perennial switchgrass generally having a higher chemical richness than the annual corn cropping system. Nonetheless, cropping system was a less influential driver of soil microbial community structure and overall C chemistry than soil type. Soil type was especially influential on fungal community structure and the chemical composition of the chloroform‐extractable C. Although plant inputs strongly influence the substrates available for decomposition and SOM formation, total C and nitrogen (N) did not differ between cropping systems within either site. We conclude this is likely due to enhanced microbial activity under the perennial cropping system. Silty soils also had a higher activity of phosphate and C liberating enzymes. After 8 years, silty loams still contained twice the total C and N as sandy loams, with no significant response to biofuel cropping system inputs. Together, these results demonstrate that initial site selection is critical to plant–microbe interactions and substantially impacts the potential for long‐term C accrual in soils under biofuel feedstock production.