MOTIVATION: Mass spectrometry imaging (MSI) and single-cell RNA sequencing (scRNA-seq) offer high-resolution views into tissue and cellular heterogeneity. However, conventional statistical analyses often treat sub-measurements (pixels or cells) as independent, ignoring their nested origin from individual samples. This assumption inflates the effective sample size, increases false discoveries, and undermines biological interpretation. Pixel- or cell-level averaging avoids this but sacrifices spatial or cellular resolution. RESULTS: We evaluated block-SAM on both simulated and real-world datasets. In DESI-MSI data from kidney, lung, and ovarian tumors, block-SAM consistently identified fewer-but more reliable-differential features compared to traditional-SAM. For example, in the kidney dataset, traditional-SAM identified 569 features between tumor and normal tissue, while block-SAM identified 186-all overlapping but excluding 383 likely false positives. Applied to a metastatic RCC scRNA-seq dataset comparing immune checkpoint blockade (ICB)-treated versus untreated patients, traditional-SAM identified over 19,000 differentially expressed genes in malignant cells; block-SAM reduced this to 19. These results demonstrate block-SAM's ability to reduce false discoveries while retaining biologically meaningful signals across diverse high-dimensional datasets. AVAILABILITY AND IMPLEMENTATION: All code and data associated with this study are deposited on Zenodo (https://doi.org/10.5281/zenodo.18273497). The samr package is freely available on the Comprehensive R Archive Network (CRAN).

Abstract Cardinal is an R package for statistical analysis of mass spectrometry-based imaging (MSI) experiments of biological samples such as tissues. Cardinal supports both Matrix-Assisted Laser Desorption/Ionization (MALDI) and Desorption Electrospray Ionization-based MSI workflows, and experiments with multiple tissues and complex designs. The main analytical functionalities include (1) image segmentation, which partitions a tissue into regions of homogeneous chemical composition, selects the number of segments and the subset of informative ions, and characterizes the associated uncertainty and (2) image classification, which assigns locations on the tissue to pre-defined classes, selects the subset of informative ions, and estimates the resulting classification error by (cross-) validation. The statistical methods are based on mixture modeling and regularization. Contact: [email protected] Availability and implementation: The code, the documentation, and examples are available open-source at www.cardinalmsi.org under the Artistic-2.0 license. The package is available at www.bioconductor.org.

Current clinical practice involves classification of biopsied or resected tumor tissue based on a histopathological evaluation by a neuropathologist. In this paper, we propose a method for computer-aided histopathological evaluation using mass spectrometry imaging. Specifically, mass spectrometry imaging can be used to acquire the chemical composition of a tissue section and, hence, provides a framework to study the molecular composition of the sample while preserving the morphological features in the tissue. The proposed classification framework uses statistical modeling to identify the tumor type associated with a given sample. In addition, if the tumor type for a given tissue sample is unknown or there is a great degree of uncertainty associated with assigning the tumor type to one of the known tumor models, then the algorithm rejects the given sample without classification. Due to the modular nature of the proposed framework, new tumor models can be added without the need to retrain the algorithm on all existing tumor models.

Recent experimental advances in matrix-assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI) have demonstrated the usefulness of these technologies in the molecular imaging of biological samples. However, development of computational methods for the statistical interpretation and analysis of the chemical differences present in the distinct regions of these samples is still a major challenge. In this poster, we propose statistically-minded methods and computational tools for analyzing DESI imaging experiments. Specifically, we present techniques for signal processing and unsupervised multivariate image segmentation, which are also applicable to other imaging mass spectrometry (IMS) methods such as MALDI. Signal processing of DESI spectra typically involves binning to reduce dimensionality, but this inefficient for downstream analysis as it retains empty regions of the mass spectrum. In our proposed processing step, we apply a novel peak picking algorithm based on windowed smoothing splines that allows adaptive resolution based on spectral profile. With this approach, peaks are aligned using a recursive dynamic programming algorithm which accounts for the heterogenous nature of IMS data by making pairwise alignments between pixels based on their proximity. Peaks are then normalized using total ion count. In order to segment the sample into sub-regions of homogenous chemical composition in MALDI images, Alexandrov & Kobarg [ 1 ] proposed two efficient spatially-aware clustering techniques. We demonstrate that these approaches are also useful for DESI. Moreover, we extend one of these clustering methods using statistical regularization techniques that enable simultaneous feature selection of structurally-important peaks and facilitate interpretation. We evaluate the performance of the proposed methods in two applications. First, in a non-biological application of DESI-imaging, we recreate a painting from the clustering of its DESI mass spectra (Figure 1 ). Since the visual content of the painting is known, it can be used as a gold standard to evaluate the performance of these methods. In the second application, we present the spatial segmentation of a fetal pig section, and evaluate the performance of our methods by the quality of the mapping between the spatial segmentation and the morphological and functional structures (Figure 2 ). We show that statistical regularization improves accuracy and interpretation of the spatial segmentation over existing approaches. (A) Optical scan of a painting as an example of a non-biological sample and (B) the results of spatially-aware sparse clustering with C = 15 clusters. The discovered sub-regions often match up with the known colors used in the painting, but also shows regions that could not be accurately distinguished, such as the gray donkey in the lower right. (C) The mean mass spectrum of the cluster corresponding to the red houses with the 27 structurally-important ions selected via regularization for s = 0.2 marked in red and (D) the ion image for one such peak at m/z 419. (A) The fetal pig section and (B) the results of spatially-aware sparse clustering with C = 15 clusters. Note that the clusters are regions of homogenous chemical composition and often match up with visually distinguishable anatomical features, while also revealing possible hidden chemical structures in regions that are visually ambiguous. (C) The mean mass spectrum of the cluster corresponding to the liver and part of the brain with the 25 structurally-important ions selected via regularization for s = 0.6 marked in red and (D) the ion image for one such peak at m/z 888.