The Visium spatial transcriptomics platform by 10X Genomics is based on Spatial Transcriptomics (ST) technology that was originally published in 2016. In both methods, mRNA from tissue sections is captured on spatially barcoded spots that are immobilized on a microarray slide. Following construction of a barcoded cDNA library, mRNA transcripts can be mapped to specific spots on the microarray slide and overlayed with a high-resolution image of the tissue, allowing for visualization and analysis of gene expression in a spatial context.
Pros and cons
Pros:
- Commercial kit
- Provided by many core facilities
- More widely available than most other spatial transcriptomics technologies
- Transcriptome wide
- Formalin fixed, paraffin embedded (FFPE) tissue compatible
- Can have a panel of proteins in addition to RNA
- Accompanied by H&E or fluorescent images for tissue morphology
- Because of the lower resolution, the data size is more manageable for larger tissue areas and larger number of samples
Cons:
- Lower resolution – 55 m spot diameter and 100 m center to center
- Relatively low detection efficiency of transcripts
- Not full length, but protocol has been adapted for long read sequencing
Getting Started
Dowload Data and Create a SpatialFeatureExperiment
object
Several publicly available Visium datasets are available from 10X
Genomics on their website. The
vignettes below provide examples of processing raw data using a workflow
that includes seqspec, gget, and kallisto/bustools
to generate a count matrix and demonstrate how to read output from a
typical Visium experiment into a SpatialFeatureExperiment
object.
| Vignette | Colab Notebook | Description |
|---|---|---|
Create
a SpatialFeatureExperiment object |
Colab Notebook | Download Visium data, create SFE object, perform basic
QC |
Analysis Workflows
The vignettes below demonstrate workflows that can be implemented
with Voyager using a variety of Visium datasets. 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 |
|---|---|---|
| Basic Visium analysis with 10X example dataset | Colab Notebook | Perform basic QC and standard non-spatial scRNA-seq analysis, and some spatial visualization |
| Spatial analysis with 10X example dataset | Colab Notebook | Tissue segmentation, and Moran’s I for QC metrics and gene expression |
| Basic Visium exploratory data analysis | Colab Notebook | Perform basic QC, data normalization, dimension reduction, compute Moran’s I, identify (spatially) variable genes |
| Spatial Visium exploratory data analysis | Colab Notebook | Perform basic QC, find spatial neighborhood graphs, compute global and local univariate spatial statistics, permutation-based significance testing, compute correlogram |