13 Re-use of bulk RNA-seq methods for spatial data exercise

Instructor: Leo

New in @sciencemagazine: our work from @LieberInstitute #spatialDLPFC applies #snRNAseq and #Visium spatial transcriptomic in the DLPFC to better understand anatomical structure and cellular populations in the human brain #PsychENCODE https://t.co/DKZqmG4YDi https://t.co/Tjp2OjTo63 pic.twitter.com/vQbjts2JtQ
— Louise Huuki-Myers (@lahuuki) May 23, 2024

13.1 Spatial registration

In 2023, Louise A. Huuki-Myers contributed a new vignette to spatialLIBD as noted on the package news / changelog: http://research.libd.org/spatialLIBD/news/index.html#spatiallibd-1132.

You should be able to run without any issues the code Louise explained at http://research.libd.org/spatialLIBD/articles/guide_to_spatial_registration.html. This same information is displayed at https://bioconductor.org/packages/release/data/experiment/vignettes/spatialLIBD/inst/doc/guide_to_spatial_registration.html.

## get reference layer enrichment statistics
layer_modeling_results <- spatialLIBD::fetch_data(type = "modeling_results")
#> adding rname 'https://www.dropbox.com/s/se6rrgb9yhm5gfh/Human_DLPFC_Visium_modeling_results.Rdata?dl=1'
#> 2024-06-11 20:06:45.072329 loading file /github/home/.cache/R/BiocFileCache/3c16459a3d_Human_DLPFC_Visium_modeling_results.Rdata%3Fdl%3D1

If the above doesn’t work, related to the curl issue we previously discussed, then use this workaround:

tmp_modeling_results <- tempfile("modeling_results.RData")
download.file(
    "https://www.dropbox.com/s/se6rrgb9yhm5gfh/Human_DLPFC_Visium_modeling_results.Rdata?dl=1",
    tmp_modeling_results,
    mode = "wb"
)
load(tmp_modeling_results, verbose = TRUE)
#> Loading objects:
#>   modeling_results


## Let's rename the object into the name used in the
## spatial registration vignette (from spatialLIBD)
layer_modeling_results <- modeling_results

This journal club style video of the main results of the spatialDLPFC paper does explain the basics of spatial registration:

For more on spatialDLPFC, check this second video about the supplementary results:

13.2 Exercise

Exercise: Follow the vignette on spatial registration. Do the results change when you use cutoff_merge_ratio = 0.1? What is this argument controlling?