MOTIVATION: Imaging mass spectrometry (imaging MS) is a prominent technique for capturing distributions of molecules in tissue sections. Various computational methods for imaging MS rely on quantifying spatial correlations between ion images, referred to as co-localization. However, no comprehensive evaluation of co-localization measures has ever been performed; this leads to arbitrary choices and hinders method development. RESULTS: We present ColocML, a machine learning approach addressing this gap. With the help of 42 imaging MS experts from nine laboratories, we created a gold standard of 2210 pairs of ion images ranked by their co-localization. We evaluated existing co-localization measures and developed novel measures using term frequency-inverse document frequency and deep neural networks. The semi-supervised deep learning Pi model and the cosine score applied after median thresholding performed the best (Spearman 0.797 and 0.794 with expert rankings, respectively). We illustrate these measures by inferring co-localization properties of 10 273 molecules from 3685 public METASPACE datasets. AVAILABILITY AND IMPLEMENTATION: https://github.com/metaspace2020/coloc. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Imaging mass spectrometry (imaging MS) is an enabling technology for spatial metabolomics of tissue sections with rapidly growing areas of applications in biology and medicine. However, imaging MS data is polluted with off-sample ions caused by sample preparation, particularly by the MALDI (matrix-assisted laser desorption/ionization) matrix application. Off-sample ion images confound and hinder statistical analysis, metabolite identification and downstream analysis with no automated solutions available. RESULTS: We developed an artificial intelligence approach to recognize off-sample ion images. First, we created a high-quality gold standard of 23,238 expert-tagged ion images from 87 public datasets from the METASPACE knowledge base. Next, we developed several machine and deep learning methods for recognizing off-sample ion images. The following methods were able to reproduce expert judgements with a high agreement: residual deep learning (F1-score 0.97), semi-automated spatio-molecular biclustering (F1-score 0.96), and molecular co-localization (F1-score 0.90). In a test-case study, we investigated off-sample images corresponding to the most common MALDI matrix (2,5-dihydroxybenzoic acid, DHB) and characterized properties of matrix clusters. CONCLUSIONS: Overall, our work illustrates how artificial intelligence approaches enabled by open-access data, web technologies, and machine and deep learning open novel avenues to address long-standing challenges in imaging MS.

Summary: We have developed a web application curatr for the rapid generation of high quality mass spectral fragmentation libraries from liquid-chromatography mass spectrometry datasets. Curatr handles datasets from single or multiplexed standards and extracts chromatographic profiles and potential fragmentation spectra for multiple adducts. An intuitive interface helps users to select high quality spectra that are stored along with searchable molecular information, the providence of each standard and experimental metadata. Curatr supports exports to several standard formats for use with third party software or submission to repositories. We demonstrate the use of curatr to generate the EMBL Metabolomics Core Facility spectral library http://curatr.mcf.embl.de. Availability and implementation: Source code and example data are at http://github.com/alexandrovteam/curatr/. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Imaging mass spectrometry (IMS) is a maturating technique of molecular imaging. Confidence in the reproducible quality of IMS data is essential for its integration into routine use. However, the predominant method for assessing quality is visual examination, a time consuming, unstandardized and non-scalable approach. So far, the problem of assessing the quality has only been marginally addressed and existing measures do not account for the spatial information of IMS data. Importantly, no approach exists for unbiased evaluation of potential quality measures. RESULTS: We propose a novel approach for evaluating potential measures by creating a gold-standard set using collective expert judgements upon which we evaluated image-based measures. To produce a gold standard, we engaged 80 IMS experts, each to rate the relative quality between 52 pairs of ion images from MALDI-TOF IMS datasets of rat brain coronal sections. Experts' optional feedback on their expertise, the task and the survey showed that (i) they had diverse backgrounds and sufficient expertise, (ii) the task was properly understood, and (iii) the survey was comprehensible. A moderate inter-rater agreement was achieved with Krippendorff's alpha of 0.5. A gold-standard set of 634 pairs of images with accompanying ratings was constructed and showed a high agreement of 0.85. Eight families of potential measures with a range of parameters and statistical descriptors, giving 143 in total, were evaluated. Both signal-to-noise and spatial chaos-based measures performed highly with a correlation of 0.7 to 0.9 with the gold standard ratings. Moreover, we showed that a composite measure with the linear coefficients (trained on the gold standard with regularized least squares optimization and lasso) showed a strong linear correlation of 0.94 and an accuracy of 0.98 in predicting which image in a pair was of higher quality. AVAILABILITY AND IMPLEMENTATION: The anonymized data collected from the survey and the Matlab source code for data processing can be found at: https://github.com/alexandrovteam/IMS_quality.

MOTIVATION: Imaging mass spectrometry has emerged in the past decade as a label-free, spatially resolved and multi-purpose bioanalytical technique for direct analysis of biological samples. However, solving two everyday data analysis problems still requires expert judgment: (i) the detection of unknown molecules and (ii) the testing for presence of known molecules. RESULTS: We developed a measure of spatial chaos of a molecular image corresponding to a mass-to-charge value, which is a proxy for the molecular presence, and developed methods solving considered problems. The statistical evaluation was performed on a dataset from a rat brain section with test sets of molecular images selected by an expert. The measure of spatial chaos has shown high agreement with expert judges. The method for detection of unknown molecules allowed us to find structured molecular images corresponding to spectral peaks of any low intensity. The test for presence applied to a list of endogenous peptides ranked them according to the proposed measure of their presence in the sample. AVAILABILITY: The source code and test sets of mass-to-charge images are available at http://www.math.uni-bremen.de/∼theodore. SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online. CONTACT: [email protected].

Science could benefit from adopting management approaches, techniques, and expertise created in other fields of professional teamwork. The closest field to science is possibly software engineering and development. In its structure and spirit, a small software company very much resembles a scientific lab: It is a small motivated group of highly-educated professionals creating nonmaterial products with teamwork centered around collaboration between individuals. Science could benefit from using frameworks for project management, process control, issue tracking, and time management, which have been undergoing development in IT for decades, such as modern approaches of agile software development describing collaboration of self-organizing cross-functional teams. For a young PI at the beginning of his or

peer reviewed

Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) imaging mass spectrometry, also called MALDI-imaging, is a label-free bioanalytical technique used for spatially-resolved chemical analysis of a sample. Usually, MALDI-imaging is exploited for analysis of a specially prepared tissue section thaw mounted onto glass slide. A tremendous development of the MALDI-imaging technique has been observed during the last decade. Currently, it is one of the most promising innovative measurement techniques in biochemistry and a powerful and versatile tool for spatially-resolved chemical analysis of diverse sample types ranging from biological and plant tissues to bio and polymer thin films. In this paper, we outline computational methods for analyzing MALDI-imaging data with the emphasis on multivariate statistical methods, discuss their pros and cons, and give recommendations on their application. The methods of unsupervised data mining as well as supervised classification methods for biomarker discovery are elucidated. We also present a high-throughput computational pipeline for interpretation of MALDI-imaging data using spatial segmentation. Finally, we discuss current challenges associated with the statistical analysis of MALDI-imaging data.

Image segmentation aims at partitioning an image into multiple segments. The application of this procedure produces a label map (also referred to as segmentation map) that classifies the pixels of the original image. In contrast to "natural" images, label maps are nominal-scale images, typically represented as integer-valued images. Nominal-scaled label maps can also appear as a representation of the raw data in areas, such as in geostatistics. In some applications, the original resolution of a label map does not suffice and a larger size map has to be generated. In this paper, we present a magnification algorithm for label maps and nominal images. The main property of our method is that it preserves the topology during the magnification process, which means that no isolated pixel vanishes. To the best of our knowledge, apart from nearest-neighbor interpolation, the problem of label map magnification has not previously been addressed in the literature. The main idea of the proposed method is to accomplish a boundary refinement by smoothing the regions' boundaries on a finer grid. The method relies on well known methods, namely, the fundamental operations of morphological image processing-erosion and dilation-and the level-set method. The level-set method is well suited for our purposes since it does not depend on a parametrization and it is numerically stable. The topological flexibility of the level-set method-often found to be an advantage in applications-is a drawback here, since the topology of the original label map should be preserved. However, using the so-called simple point criterion from digital topology, one can adapt the conventional level-set method so that the topology will not be modified throughout the magnification procedure.

MOTIVATION: Imaging mass spectrometry (IMS) is one of the few measurement technology s of biochemistry which, given a thin sample, is able to reveal its spatial chemical composition in the full molecular range. IMS produces a hyperspectral image, where for each pixel a high-dimensional mass spectrum is measured. Currently, the technology is mature enough and one of the major problems preventing its spreading is the under-development of computational methods for mining huge IMS datasets. This article proposes a novel approach for spatial segmentation of an IMS dataset, which is constructed considering the important issue of pixel-to-pixel variability. METHODS: We segment pixels by clustering their mass spectra. Importantly, we incorporate spatial relations between pixels into clustering, so that pixels are clustered together with their neighbors. We propose two methods. One is non-adaptive, where pixel neighborhoods are selected in the same manner for all pixels. The second one respects the structure observable in the data. For a pixel, its neighborhood is defined taking into account similarity of its spectrum to the spectra of adjacent pixels. Both methods have the linear complexity and require linear memory space (in the number of spectra). RESULTS: The proposed segmentation methods are evaluated on two IMS datasets: a rat brain section and a section of a neuroendocrine tumor. They discover anatomical structure, discriminate the tumor region and highlight functionally similar regions. Moreover, our methods provide segmentation maps of similar or better quality if compared to the other state-of-the-art methods, but outperform them in runtime and/or required memory. CONTACT: [email protected].

MOTIVATION: Automatic classification of high-resolution mass spectrometry proteomic data has increasing potential in the early diagnosis of cancer. We propose a new procedure of biomarker discovery in serum protein profiles based on: (i) discrete wavelet transformation of the spectra; (ii) selection of discriminative wavelet coefficients by a statistical test and (iii) building and evaluating a support vector machine classifier by double cross-validation with attention to the generalizability of the results. In addition to the evaluation results (total recognition rate, sensitivity and specificity), the procedure provides the biomarker patterns, i.e. the parts of spectra which discriminate cancer and control individuals. The evaluation was performed on matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) serum protein profiles of 66 colorectal cancer patients and 50 controls. RESULTS: Our procedure provided a high recognition rate (97.3%), sensitivity (98.4%) and specificity (95.8%). The extracted biomarker patterns mostly represent the peaks expressing mean differences between the cancer and control spectra. However, we showed that the discriminative power of a peak is not simply expressed by its mean height and cannot be derived by comparison of the mean spectra. The obtained classifiers have high generalization power as measured by the number of support vectors. This prevents overfitting and contributes to the reproducibility of the results, which is required to find biomarkers differentiating cancer patients from healthy individuals. AVAILABILITY: The data and scripts used in this study are available at http://www.math.uni-bremen.de/~theodore/MALDIDWT.