In this #codetutorial, I quickly identify spatially variable genes in a #spatialtranscriptomic dataset w/ 377,985 cells by combining our recently developed preprocessing tool SEraster with our existing tool MERINGUE.

Try it out for yourself: jef.works/blog/2024/03...

#AcademicSky #Rstats

SEraster is a rasterization preprocessing framework that aggregates cellular information into spatial pixels to reduce resource requirements for spatial omics data analysis. SEraster reduces the number of spatial points in spatial omics datasets... lab of @jef.works

bioconductor.org/packages/SEr...

SEraster

Treating imaging-based single-cell #SpatialOmics data as Image to⏬computational burden

Rasterization of transcript/cell info to spatial pixels
100 μm/px rasterized #Xenium whole🐭pup 1.3 M cells😃
>100 time Faster!
Retaining spatial clustering

bioRxiv 2024
www.biorxiv.org/content/10.1...

#SEraster #SpatialBootstrapping
a rasterization preprocessing framework for scalable #SpatialOmics data analysis

"Using spatial bootstraps to evaluate stability of cell-type proportions"😎

Jean Fan lab Bioinformatics 2024

academic.oup.com/bioinformati...

jef.works/blog/2024/07...

We now have bin-level & cell-level results 🤩

Some work will have to be put into integrating results from different tools 🫣 😬 for downstream analyses 🧐.

#bin2cell (shown below ⬇️)
#FICTURE
#SEraster
#MERINGUE
#Giotto
#Banksy
#HERGAST (currently exploring)
#ENACT (next)

bsky.app/profile/lcol...

SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis https://www.biorxiv.org/content/10.1101/2024.02.01.578436v1

SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis https://www.biorxiv.org/content/10.1101/2024.02.01.578436v1