keller-mark / visium-dlpfc-processing

visium-dlpfc

Sample 151673

Data obtained from:

• RData file: https://www.dropbox.com/s/f4wcvtdq428y73p/Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata?dl=1 (linked from https://github.com/LieberInstitute/spatialLIBD/blob/ff49a2eaa1eb5477c5df8e46cf1652cdd8ec7244/R/fetch_data.R#L142C18-L142C125)
• Manual layer annotations: https://drive.google.com/drive/folders/10lhz5VY7YfvHrtV40MwaqLmWz56U9eBP?usp=sharing (linked from https://stagate.readthedocs.io/en/latest/T1_DLPFC.html)
• TIFF image: https://spatial-dlpfc.s3.us-east-2.amazonaws.com/images/151673_full_image.tif (linked from https://github.com/LieberInstitute/spatialLIBD/blob/ff49a2eaa1eb5477c5df8e46cf1652cdd8ec7244/README.md?plain=1#L235)

Data conversion

RData to rds

load("Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata")
saveRDS(sce, file="Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.rds")

rds to AnnData with manual layer annotations

See Jupyter notebook convert_rds_to_adata.ipynb

All to SpatialData

See Jupyter notebook convert_all_to_spatialdata.ipynb

To obtain scale factors

> library(STexampleData)
> spe <- Visium_humanDLPFC()
> imgData(spe)
DataFrame with 2 rows and 4 columns
      sample_id    image_id   data scaleFactor
    <character> <character> <list>   <numeric>
1 sample_151673      lowres   ####   0.0450045
2 sample_151673       hires   ####   0.1500150

> imgData(spe)[1, 'data']
[[1]]
600 x 600 (width x height) LoadedSpatialImage

> imgData(spe)[2, 'data']
[[1]]
2000 x 2000 (width x height) LoadedSpatialImage