spacemake

spacemake processes and analyzes large-scale spatial transcriptomics datasets to provide reproducible, modular computational workflows that preserve spatial information and enable integration with single-cell transcriptomics.

Key Features:

• Implementation: Implemented as a modular pipeline using snakemake and Python.
• Modular design: Provides separate modules for sample merging, saturation analysis, and long-read analysis.
• Parallel processing: Processes and analyzes multiple samples in parallel, including samples from diverse experimental methods.
• End-to-end reproducibility: Performs reproducible data processing from raw sequencing data to automatically generated downstream analysis reports.
• Integration with novoSpaRc: Integrates novoSpaRc to combine spatial and single-cell transcriptomics and increase gene counts for spatial datasets.
• Technology compatibility: Handles major spatial transcriptomics datasets and can be configured for other technologies.
• Scalability: Designed for robust, scalable processing of large-scale spatial sequencing datasets.

Scientific Applications:

• Spatial transcriptomics processing: Preprocesses and analyzes spatial sequencing data while preserving molecule location information.
• Spatial + single-cell integration: Combines spatial and single-cell transcriptomics via novoSpaRc to increase gene counts and inform tissue architecture and cellular interactions.
• Multi-sample comparative studies: Enables comparative analysis across multiple samples and experimental methods.
• Long-read spatial analysis: Supports analysis of long-reads in spatial transcriptomics datasets.
• Sequencing saturation assessment: Performs saturation analysis to assess sequencing depth and coverage.

Methodology:

Modular snakemake workflows and Python code implement parallel processing, modules for sample merging, saturation analysis and long-read analysis, end-to-end processing from raw sequencing data to downstream reports, and integration with novoSpaRc.