stPipe

stPipe streamlines preprocessing of sequencing-based spatial transcriptomics data from raw FASTQ files to spatially resolved gene count matrices with standardized output containers for downstream analysis.

Key Features:

• Platform-agnostic processing: Handles data from 10× Visium, Slide-seq, Stereo-seq, and other mainstream sequencing-based spatial transcriptomics platforms through modular functions.
• FASTQ processing: Performs FASTQ trimming, spatial barcode detection, and demultiplexing to assign reads to spatial locations.
• Alignment and reformatting: Includes BAM file reformatting and index building for alignment processing.
• Gene quantification: Generates gene count matrices with UMI deduplication to produce accurate molecular counts.
• Quality control: Collates quality control metrics to filter unwanted artifacts from spatial transcriptomics data.
• Standardized outputs: Adopts standardized data storage containers to enable interoperability with downstream analysis packages.
• Configuration-driven reproducibility: Uses Config as an essential input for most functions to improve reproducibility of preprocessing workflows.
• Visualization: Provides relevant visualization functions for quality control and data inspection.

Scientific Applications:

• Methods benchmarking: Enables uniform preprocessing of sequencing-based spatial transcriptomics data from reference tissue samples for comparisons between different technology platforms and downstream analysis tools.
• Cross-platform data integration: Facilitates standardized preprocessing of data from cadasSTre and SpatialBenchVisium projects to support comparative studies across spatial transcriptomics technologies.
• Downstream analysis preparation: Produces conveniently formatted, uniformly preprocessed data for passage to a wide range of downstream analysis packages in the R/Bioconductor ecosystem.

Methodology:

The pipeline processes raw FASTQ files through trimming, spatial barcode detection, demultiplexing, BAM reformatting, and index building to generate spatially resolved gene count matrices with UMI deduplication.