# SMAnalyst: A Web Server for Spatial Metabolomic Data Analysis and Annotation

## TL;DR

SMAnalyst is a new web-based tool that simplifies the analysis of spatial metabolomic data, offering a complete workflow from quality control to differential analysis.

## Contribution

SMAnalyst introduces an integrated, open-source GUI platform for spatial metabolomics analysis, combining multiple functionalities in a single tool.

## Key findings

- SMAnalyst consolidates quality control, annotation, and spatial pattern discovery in a single platform.
- The tool efficiently processes large datasets, such as over 14,000 pixels and 3,000 ion peaks.
- SMAnalyst reduces the need for advanced computational skills and tool switching in spatial metabolomics.

## Abstract

Spatial metabolomics is a rapidly advancing field offering powerful insights into metabolic heterogeneity in biological tissues. However, its widespread adoption is hindered by fragmented tools and the lack of comprehensive, open-source GUI software covering the full analytical workflow (quality control, preprocessing, identification, pattern, and differential analysis). To address this, we developed SMAnalyst, an open-source, integrated web-based platform. SMAnalyst consolidates core functionalities, including multi-dimensional data quality assessment (background consistency, intensity, missing values), a comprehensive metabolite annotation scoring system (mass accuracy, isotopic similarity, adduct evidence), and dual-dimension spatial pattern discovery (metabolite co-expression and pixel clustering). It also offers flexible differential analysis (cluster- or user-defined regions). With its intuitive GUI and modular workflow, SMAnalyst significantly lowers the analysis barrier, by providing a unified solution that eliminates the need for tool switching and advanced computational skills. Tested with a mouse brain dataset, SMAnalyst efficiently handles large-scale data (e.g., >14,000 pixels, >3000 ion peaks), effectively filling a critical gap in integrated analytical solutions for spatial metabolomics.