SPLiT-seq Processing Workflows with Voyager
Source:vignettes/splitseq_landing.Rmd
splitseq_landing.RmdPros and cons
Pros:
- Commercial kit
- Low cost
- Single well capture with randomly primed and polyT oligos in the same library
Cons: * Fewer datasets available compared to other single cell technologies
Getting Started
The vignettes below provide examples of processing raw data using a
workflow that includes seqspec, gget, and kallisto/bustools
to generate a count matrix. We process the output of various
transcriptomics technologies into a SpatialFeatureExperiment(SFE)
object for use with Voyager.
| Vignette | Colab Notebook | Description |
|---|---|---|
| Preprocess raw data | Colab Notebook | Fetch reference data with gget, process raw data with
seqspec, generate a count matrix with
kallisto-bustools
|
Create
a SFE object |
Colab Notebook | Download data, create SFE object |
Analysis Workflows
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 Google Colab notebooks are linked.
| Vignette | Colab Notebook | Description |
|---|---|---|
| SPLiT-seq Basic QC | Colab Notebook | Basic qc and preprocessing |