SpatialDecon-package: SpatialDecon: A package for computating the notorious bar...

Description functions Examples

Description

The SpatialDecon package estimates mixed cell type abundance in the regions of spatially-resolved gene expression studies, using the method of Danaher & Kim (2020), "Advances in mixed cell deconvolution enable quantification of cell types in spatially-resolved gene expression data." It is also appropriate to apply to bulk gene expression data.

functions

Functions to help set up deconvolution:

Deconvolution functions:

Plotting functions:

Examples

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data(mini_geomx_dataset)
data(safeTME)
data(safeTME.matches)
# estimate background:
mini_geomx_dataset$bg <- derive_GeoMx_background(
  norm = mini_geomx_dataset$normalized,
  probepool = rep(1, nrow(mini_geomx_dataset$normalized)),
  negnames = "NegProbe"
)
# run basic decon:
res0 <- spatialdecon(
  norm = mini_geomx_dataset$normalized,
  bg = mini_geomx_dataset$bg,
  X = safeTME
)
# run decon with bells and whistles:
res <- spatialdecon(
  norm = mini_geomx_dataset$normalized,
  bg = mini_geomx_dataset$bg,
  X = safeTME,
  cellmerges = safeTME.matches,
  cell_counts = mini_geomx_dataset$annot$nuclei,
  is_pure_tumor = mini_geomx_dataset$annot$AOI.name == "Tumor"
)

Example output



SpatialDecon documentation built on Nov. 8, 2020, 6 p.m.