Description functions Examples
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 to help set up deconvolution:
derive_GeoMx_background Estimates the background levels from GeoMx experiments
collapseCellTypes reformats deconvolution results to merge closely-related cell types
download_profile_matrix Downloads a cell profile matrix.
safeTME: a data object, a matrix of immune cell profiles for use in tumor-immune deconvolution.
Deconvolution functions:
spatialdecon runs the core deconvolution function
reverseDecon runs a transposed/reverse deconvolution problem, fitting the data as a function of cell abundance estimates. Used to measure genes' dependency on cell mixing and to calculate gene residuals from cell mixing.
Plotting functions:
florets Plot cell abundance on a specified x-y space, with each point a cockscomb plot showing the cell abundances of that region/sample.
TIL_barplot Plot abundances of tumor infiltrating lymphocytes (TILs) estimated from the safeTME cell profile matrix
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | 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"
)
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