SUMMARY: Joint analysis of mass spectrometry images (MS images) and microscopy images of hematoxylin and eosin (H&E) stained tissues assists pathologists in characterizing the morphological structure of the tissues, and in performing diagnosis. Unfortunately, the analysis is undermined by substantial differences between these modalities in terms of aspect ratios, spatial resolution, number of channels in each image, as well as by large global or small local elastic spatial deformations of one image with respect to the other. Therefore, accurate coregistration of the images is a critical pre-requisite for their joint interpretation. We introduce MSIreg, an open-source R package for coregistration of MSI and H&E images. MSIreg is designed for high-dimensional MSI experiments where each spatial location is represented by thousands of mass features. Unlike most existing coregistration methods, MSIreg implements a landmark free workflow, and quantitative metrics for performance evaluation. We evaluate the performance of MSIreg on six case studies, including coregistration of contiguous tissues with large deformations, as well as simultaneous coregistration of 29 tissue microarray cores. AVAILABILITY AND IMPLEMENTATION: The R package, installation instructions, and fully reproducible vignettes describing methods and Case Studies are available open-source under the GPL-3.0 license at https://github.com/sslakkimsetty/msireg/.

MOTIVATION: Mass Spectrometry Imaging (MSI) analyzes complex biological samples such as tissues. It simultaneously characterizes the ions present in the tissue in the form of mass spectra, and the spatial distribution of the ions across the tissue in the form of ion images. Unsupervised clustering of ion images facilitates the interpretation in the spectral domain, by identifying groups of ions with similar spatial distributions. Unfortunately, many current methods for clustering ion images ignore the spatial features of the images, and are therefore unable to learn these features for clustering purposes. Alternative methods extract spatial features using deep neural networks pre-trained on natural image tasks; however, this is often inadequate since ion images are substantially noisier than natural images. RESULTS: We contribute a deep clustering approach for ion images that accounts for both spatial contextual features and noise. In evaluations on a simulated dataset and on four experimental datasets of different tissue types, the proposed method grouped ions from the same source into a same cluster more frequently than existing methods. We further demonstrated that using ion image clustering as a pre-processing step facilitated the interpretation of a subsequent spatial segmentation as compared to using either all the ions or one ion at a time. As a result, the proposed approach facilitated the interpretability of MSI data in both the spectral domain and the spatial domain. AVAILABILITYAND IMPLEMENTATION: The data and code are available at https://github.com/DanGuo1223/mzClustering. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Mass spectrometry imaging (MSI) characterizes the spatial distribution of ions in complex biological samples such as tissues. Since many tissues have complex morphology, treatments and conditions often affect the spatial distribution of the ions in morphology-specific ways. Evaluating the selectivity and the specificity of ion localization and regulation across morphology types is biologically important. However, MSI lacks algorithms for segmenting images at both single-ion and spatial resolution. RESULTS: This article contributes spatial-Dirichlet Gaussian mixture model (DGMM), an algorithm and a workflow for the analyses of MSI experiments, that detects components of single-ion images with homogeneous spatial composition. The approach extends DGMMs to account for the spatial structure of MSI. Evaluations on simulated and experimental datasets with diverse MSI workflows demonstrated that spatial-DGMM accurately segments ion images, and can distinguish ions with homogeneous and heterogeneous spatial distribution. We also demonstrated that the extracted spatial information is useful for downstream analyses, such as detecting morphology-specific ions, finding groups of ions with similar spatial patterns, and detecting changes in chemical composition of tissues between conditions. AVAILABILITY AND IMPLEMENTATION: The data and code are available at https://github.com/Vitek-Lab/IonSpattern. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Summary: We introduce matter , an R package for direct interactions with larger-than-memory datasets, stored in an arbitrary number of files of any size. matter is primarily designed for datasets in new and rapidly evolving file formats, which may lack extensive software support. matter enables a wide variety of data exploration and manipulation steps, and is extensible to many bioinformatics applications. It supports reproducible research by minimizing the need of converting and storing data in multiple formats. We illustrate the performance of matter in conjunction with the Bioconductor package Cardinal for analysis of high-resolution, high-throughput mass spectrometry imaging experiments. Availability: The package, vignettes, and examples of applications in several areas of bioinformatics are available open-source at www.bioconductor.org under the Artistic-2.0 license. Contact: [email protected].