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The Slide-seqV2 platform enables transcriptome-wide spatial profiling of fresh-frozen tissue sections. In both Slide-seqV2 and its predecessor, Slide-seq, mRNA capture takes place on a DNA-barcoded array of \(10 \mu m\) beads immobilized on a microscopy slide. Slide-seqV2 offers improvements in capture efficiency and enzymatic library generation.

Pros and cons


  • Higher resolution than Visium, as the beads are 10 \(\mu\)m in diameter
  • Transcriptome wide
  • Recently commercialized as Curio, commercial kit coming


  • Still not single cell resolution as two cells can occupy the same bead
  • Relatively low detection efficiency of transcripts
  • Existing datasets may not come with histology image

Getting Started

Dowload Data and Create a SpatialFeatureExperiment object

The vignettes below demonstrate how to convert sequencing data from a spatial transcriptomics experiment to a SpatialFeatureExperiment object in R. Many technologies have not yet standardized their output formats, and the vignettes below provide examples of how to generate a SFE object with various output file types.

Vignette Colab Notebook Description
Create a SpatialFeatureExperiment object Colab Notebook Download data, create SFE object, perform basic QC
Create a SpatialFeatureExperiment from scratch Colab Notebook Download data from GEO using ffq, create SFE object, perform basic QC

Analysis Workflows

The vignettes below demonstrate workflows that can be implemented with Voyager using data generated with the Slide-seqV2 platform. The analysis tasks include basic quality control, spatial exploratory data analysis, identification of spatially variable genes, and computation of global and local spatial statistics. Accompanying Colab notebooks are linked when available.

Vignette Colab Notebook Description
Slide-seqV2 exploratory data analysis Colab Notebook Perform basic QC, data normalization, dimension reduction, compute Moran’s I