Pre-processed input matrices derived from TCGA skin cutaneous melanoma (SKCM)
bulk RNA-seq data. The object is a named list that can be passed directly to
compute_racing_kernel() or compute_racing_montecarlo() via the
input_data parameter.
Format
A named list with the following elements:
- Lmatrix
Numeric matrix (9 cell types x 276 ligands). Binary cell-to-ligand compatibility.
- Rmatrix
Numeric matrix (9 cell types x 298 receptors). Binary cell-to-receptor compatibility.
- Cmatrix
Numeric matrix (10 patients x 9 cell types). Row-normalised cell-type abundance estimates from deconvolution, with M1 and M2 macrophages merged into a single M category.
- LRmatrix
3-D numeric array (276 ligands x 298 receptors x 10 patients). Normalised ligand–receptor interaction probability tensor.
- celltypes
Character vector of 9 cell-type names (alphabetically sorted).
- ligands
Character vector of 276 ligand names.
- receptors
Character vector of 298 receptor names.
- Sign_matrix
Numeric matrix (276 x 298) of zeros (unknown interaction signs).
Source
Derived from TCGA SKCM data processed with TMEmod deconvolution and OmniPath ligand–receptor annotations.
Examples
data(skcm_example)
str(skcm_example, max.level = 1)
#> List of 8
#> $ Lmatrix : num [1:9, 1:276] 1 0 1 1 1 1 1 1 0 1 ...
#> ..- attr(*, "dimnames")=List of 2
#> $ Rmatrix : num [1:9, 1:298] 1 0 1 1 0 1 1 1 0 0 ...
#> ..- attr(*, "dimnames")=List of 2
#> $ Cmatrix : num [1:10, 1:9] 0.00374 0.01407 0.02119 0 0.00313 ...
#> ..- attr(*, "dimnames")=List of 2
#> $ LRmatrix : num [1:276, 1:298, 1:10] 0.000228 0 0 0 0 ...
#> $ celltypes : chr [1:9] "B" "CAF" "CD8+ T" "DC" ...
#> $ ligands : chr [1:276] "LGALS9" "ADAM10" "TNFSF12" "ICOSLG" ...
#> $ receptors : chr [1:298] "PTPRC" "MET" "CD44" "LRP1" ...
#> $ Sign_matrix: num [1:276, 1:298] 0 0 0 0 0 0 0 0 0 0 ...
# Use directly with the kernel workflow
# result <- compute_racing_kernel(input_data = skcm_example)