STtools: A Comprehensive Software Pipeline for Ultra-high Resolution Spatial Transcriptomics Data

Abstract

Motivation: While there are many software pipelines for analyzing spatial transcriptomics data, few can process ultra high-resolution datasets generated by emerging technologies. There is a clear need for new software tools that can handle sub-micrometer resolution spatial transcriptomics data with computational scalability without compromising its resolution.

Results: We developed STtools, a software pipeline that provides a versatile framework to handle spatial transcriptomics datasets with various resolutions, such as the ones produced by Seq-Scope (<1μm), Slide-seq (10μm) and VISIUM (100μm). It automatically processes raw FASTQ files and runs downstream analyses at several folds higher resolution than existing methods. It also generates various visualizations including transcriptome density, cell type mapping, marker gene highlighting, and subcellular architectures.

Availability: STtools is publically available for download at https://github.com/seqscope/STtools.

Fig. 1.

Visualization of ST data with STtools. (A–D) Visualize Seq-Scope mouse liver dataset, and (E–H) visualize Slide-seq mouse cerebellum dataset. (A, E) Visualize UMAP coordinates and clustered cell types for each squared grid. (B, F) Visualize the clustered cell types for each simple grid (10$\mathrm{\mu }$m for Seq-Scope, 50 $\mathrm{\mu }$m for Slide-seq). (C, G) Visualize the cell types from MSSW with higher resolution (2 $\mathrm{\mu }$m for Seq-Scope and 10$\mathrm{\mu }$m for Slide-seq). (D, H) Visualize selected marker genes in RGB color at ultra-high resolution (1$\mathrm{\mu }$m/pixel for Seq-Scope and 10 $\mathrm{\mu }$m/pixel for Slide-seq)