RaspArray and MADApp: System for reproducible microarray imaging and data analysis across research settings

Microarray technology represents a powerful high-throughput analytical platform enabling simultaneous assessment of multiple analytes from minimal biological sample volumes. However, commercially available platforms typically rely on proprietary readers and analysis software. These vendor-specific solutions are costly, lack interoperability, require specialised training, and demand dedicated laboratory infrastructure, limiting their applicability for portable or field-based analyses and in resource-limited settings. We present an open-source, interactive system for microarray analysis comprising two integrated components: the Microarray Data Analysis Application (MADApp), a web-based R Shiny application providing automated, standardised workflows for qualitative and quantitative microarray analysis, and RaspArray, a Raspberry Pi-based image acquisition system. MADApp’s flexible architecture supports multi-modal deployment including on-site acquisition and analysis, server-side deployment, and minimal client-side processing, thereby improving accessibility and reproducibility in microarray research. Systematic comparative analysis against commercial software using multiplexed protein (serological) and DNA microarray image datasets acquired under different imaging modalities and experimental settings validated system performance. Results demonstrated high similarity across multiple imaging modalities, with particularly strong correlation between images captured via commercial readers and our custom RaspArray system. Notably, strong correlation was also observed for images acquired under minimal settings using standard smartphone cameras.